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Imaging Neuroscience, Volume 3, 2025
https://doi.org/10.1162/imag_a_00418
Research Article
1. INTRODUCTION
The connections between the thalamus and cortex are
essential for maintaining sensory and motor control, in
addition to higher order functions including attention,
memory, emotion, and consciousness ( Jankowski etal.,
2013; Mitchell, 2015; Sherman, 2016; Sherman & Guillery,
2002; Sommer, 2003). It is widely considered that the
diverse functionality of thalamus is underpinned by its
nuclear organisation— consisting of 50– 60 distinct nuclei
with distinctive patterns of molecular, cytoarchitectural,
and connectivity properties ( Fama & Sullivan, 2015;
Jones, 2007; Morel, 2007).
Recent evidence suggests that, in addition to a well-
characterised nuclear structure, the thalamus exhibits
continuous variations in patterns of connectivity, cytoar-
chitecture, and molecular identity that extend both within
and across specic nuclei ( Gao etal., 2020; Howell etal.,
2024; Jones, 1998; John etal., 2024; Li etal., 2020; Mai
& Majtanik, 2019; McFarland & Haber, 2002; Oldham &
Ball, 2023; Park etal., 2024; Phillips etal., 2019; Roy etal.,
2022; Saunders etal., 2018). The spatial organisation of
Perinatal development of structural thalamocortical connectivity
Stuart Oldhama,b, Sina Mansour L.c,d, Gareth Balla,e
aDevelopmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital Melbourne, Parkville, Victoria, Australia
bThe Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
cSystems Lab, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
dCentre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore,
Singapore, Singapore
eDepartment of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
Corresponding Author: Stuart Oldham (stuart.oldham@mcri.edu.au)
ABSTRACT
Thalamocortical connections are crucial for relaying sensory information in the brain and facilitate essential functions
including motor skills, emotion, and cognition. Emerging evidence suggests that thalamocortical connections are
organised along spatial gradients that may reect their sequential formation during early brain development. However,
this has not been extensively characterised in humans. To examine early thalamocortical development, we analysed
diffusion MRI data from 345 infants, scanned between 29 and 45weeks gestational age. Using diffusion tractography,
we mapped thalamocortical connectivity in each neonate and used Principal Component Analysis to extract shared
spatial patterns of connectivity. We identied a primary axis of connectivity that varied along an anterior/medial to
posterior/lateral gradient within the thalamus, with corresponding projections to cortical areas varying along a rostral-
caudal direction. The primary patterns of thalamocortical connectivity were present at 30weeks’ gestational age and
gradually rened during gestation. This renement was largely driven by the maturation of connections between the
thalamus and cortical association areas. Differences in thalamocortical connectivity between preterm and term neo-
nates were only weakly related to primary thalamocortical gradients, suggesting a relative preservation of these fea-
tures following premature birth. Overall, our results indicate that the organisation of structural thalamocortical
connections is highly conserved across individuals, develops early in gestation, and gradually matures with age.
Keywords: thalamus, gradients, perinatal, neurodevelopment, thalamocortical, connectivity
Received: 29 April 2024 Revision: 15 November 2024 Accepted: 19 November 2024 Available Online: 10 December 2024
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S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
the thalamus along continuous axes is reected by con-
certed variations in gene transcription, axonal morphology,
laminar targeting, and electrophysiological properties,
alongside other key principles of cortical organisation
( Oldham & Ball, 2023; Phillips etal., 2019).
The presence of continuous macroscopic variation in
thalamic connectivity presents evidence that thalamo-
cortical patterning is shaped by morphogenetic gradients
during early brain development ( Govek et al., 2022;
Nagalski etal., 2016; Price etal., 2012; Teissier & Pierani,
2021). Firstly, the orientation of the principal axes of vari-
ation correspond to known developmental gradients
( Altman & Bayer, 1979; Cahalane et al., 2012; Finlay &
Uchiyama, 2015; Nakagawa, 2019; Wong et al., 2018).
Secondly, genes expressed along the primary molecular
and connectomic axes are differentially expressed during
the prenatal period ( Oldham & Ball, 2023). Finally, these
genes are enriched for neurodevelopmental disorders
such as schizophrenia ( Elvsåshagen etal., 2021; Oldham
& Ball, 2023).
Despite its central importance in brain organisation
and function, only a relatively limited number of neuroim-
aging studies have addressed the development of thal-
amocortical connectivity in the human brain during the
perinatal period ( Ball et al., 2012, 2013, 2015; Batalle
et al., 2017; Jakab et al., 2020; Sa De Almeida et al.,
2021; Toulmin et al., 2021; Wilson et al., 2023; Zheng
et al., 2023). The second half of gestation is a critical
period for the formation of thalamocortical connections
( Kostović etal., 2019, 2021; Kostović & Judaš, 2010). By
birth, the overall patterning of structural thalamocortical
connections is established ( Kostović et al., 2019), with
maturation progressing from early- maturing primary sen-
sory areas to later- maturing association cortex in the
frontal lobe ( Zheng etal., 2023). Evidence suggests that
interruptions to early brain development due to preterm
birth can negatively impact the structural, and subse-
quently functional, connectivity of the thalamus in infancy,
leading to poor cognitive and motor outcomes ( Alcauter
etal., 2014; Ball et al., 2012, 2015; Jakab et al., 2020;
Toulmin etal., 2015, 2021). Investigating the spatial and
temporal progression in how structural thalamocortical
connections form and mature is vital to understanding
the development of healthy and abnormal brain function.
While other studies have outlined the broad develop-
mental trends of structural thalamocortical connectivity,
previous examples have either examined a limited num-
ber of major thalamocortical tracts or only used a coarse
cortical parcellation to dene cortical connectivity. In this
study, we employ a high- resolution connectome approach
to estimate dense maps of thalamocortical structural
connectivity from 29 to 45weeks gestation, examining
the development of organisational thalamocortical gradi-
ents and providing a granular analysis of early thalamic
structural connectivity to the cortex. In addition, we test
the hypothesis that preterm birth negatively impacts early
patterning of structural thalamocortical connectivity.
2. METHODS
Participant data were acquired from the third release of the
Developing Human Connectome Project (dHCP; ethics
approved by the United Kingdom Health Research Ethics
Authority, reference no. 14/LO/1169) ( Edwards etal., 2022;
Hughes etal., 2017) which consisted of 783 neonates (360
female; median birth age [range]=39+2 weeks [23– 43+4])
across 889 scans (median scan age [range]=40+6 [26+5–
45+1] weeks; 107 neonates were scanned multiple times).
Following strict quality control (see below), 363 scans were
retained for analysis. For the 18 neonates with multiple
scans, we selected the scan closest to their birth age. The
nal cohort comprised 345 neonates (165 females; median
birth age [range] = 39weeks [23+4– 42+2]; median scan age
[range]=40+3 [29+2– 45+1] weeks). Of these, we selected the
oldest 20 by scan age, who were born at term age and had
a radiological score of 1 (indicating no radiological abnor-
malities or pathologies), to create a term template which
acted as a reference.
2.1. MRI acquisition and processing
Images were acquired on a Phillips Achieva 3T scanner at
St Thomas Hospital, London, United Kingdom ( Hughes
et al., 2017). T2- weighted images were acquired using
multislice fast spin- echo sequence with TR=12,000ms,
TE=156ms, using overlapping slices (0.8×0.8×1.6mm).
Diffusion data were acquired with TR = 4,000 ms,
TE=90ms, 20b=0s/mm2 volumes and 64 400s/mm2,
88 1,000s/mm2 and 128 2,600s/mm2 b- value volumes,
and 1.5x1.5x3mm voxels in 64 slices.
Structural images were processed using the dHCP’s
minimal preprocessing pipeline ( Makropoulos et al.,
2018). This included applying bias correction and brain
extraction. The DRAW- EM algorithm was then used to
create tissue segmentations based on the T2- weighted
images.
A neonatal- specic processing pipeline was applied to
the diffusion data, the full details of which are described
elsewhere ( Bastiani et al., 2019). In brief, this involved
selecting b0 volumes least affected by within- volume
motion and using this to estimate the off- response eld
using FSL’s TOPUP ( Andersson etal., 2003) followed by
distortion corrections using FSL’s EDDY ( Andersson etal.,
2016, 2017, 2018; Andersson & Sotiropoulos, 2016). A
super- resolution algorithm was applied to achieve an iso-
tropic resolution of 1.5mm ( Kuklisova- Murgasova et al.,
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S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
2012). The diffusion data were aligned to the individual T2w
images ( Greve & Fischl, 2009; Jenkinson etal., 2002), and
then to the 40- week neonatal dHCP template ( Schuh etal.,
2018) using nonlinear registration ( Andersson etal., 2010).
To ensure only high- quality scans without major
motion- related artefacts were used, we examined the
quality control summaries of the dHCP diffusion process-
ing pipeline. Scans which were more than 2 standard
deviations away from the mean on any of the volume-
to- volume motion, within- volume motion, susceptibility-
induced distortions, and eddy current- induced distortions
metrics were excluded (n=626).
2.2. Thalamic seed denition
Using the thalamic mask manually dened on the dHCP
extended 40- week template ( Bozek et al., 2018), we
placed 800 seeds (arranged in a 1.75mm 3D grid) evenly
distributed throughout the thalamic volume in the left
hemisphere (Fig.1A). Each seed was transformed from
the template to each participant’s diffusion scan using
the pre- calculated non- linear transformations. Thalamic
seed registrations were inspected visually to ensure cor-
rect alignment.
2.3. Thalamic seed connectivity
For each neonate, we generated 5,000 streamlines from
each of the 800 thalamic seeds using MRtrix3 ( Jeurissen
etal., 2014; Tournier etal., 2004, 2019). To calculate the
bre orientation distributions (FODs) needed for tractog-
raphy, we rst extracted only the 0 and 1,000 s/mm²
b- value volumes for the diffusion data were extracted, as
using just these to dene FODs using single- shell 3- tissue
constrained spherical deconvolution (CSD) has shown
better denition of crossing bres than multi- shell CSD
approaches in neonatal data ( Dhollander et al., 2019).
Next, the response function was estimated using 20 of
the oldest term- born subjects to ensure only estimates of
relatively mature white matter were used. White matter,
grey matter, and cerebral spinal uid (CSF) response
functions were estimated using the dhollander algorithm
in MRtrix3 ( Dhollander et al., 2016). The estimated
response functions were used in single- shell three- tissue
CSD to obtain FODs for every participant ( Dhollander
etal., 2019). A ve- tissue- type image was created using
segmentations of the grey matter, white matter, and CSF
provided by the dHCP to apply Anatomically Constrained
Tractography ( R. E. Smith et al., 2012). Connectivity
between the thalamus and cortex was then calculated
using second- order integration over bre orientation dis-
tributions (iFOD2) tractography ( Tournier et al., 2010,
2019) (0.75mm step size; 45° maximum angle; 0.05 bre
orientation distribution cut- off). We visually inspected the
resulting tractograms to check anatomically plausible
streamlines were obtained and no gross abnormalities
were present as to ensure our approach was successful
in delineating white- matter pathways.
To estimate the spatial distribution of cortical connec-
tions from each thalamic seed, we used a surface- based
mapping approach. The dHCP neonatal 40- week white
matter surface was aligned to each individual’s diffusion
space using transforms provided by the dHCP. This pro-
vided a surface mesh with matched geometry to each
individual’s surface with vertex correspondence across
individuals for comparison.
Fig.1. Schematic of processing steps (A) 800 seeds are dened across the thalamic volume and are registered to each
individual’s diffusion data. (B) Connectivity between each seed and 28,766 cortical vertices is estimated with probabilistic
tractography (example connectivity from one thalamic seed is shown; top), producing a dense thalamic- seed- by- cortical-
vertex connectivity matrix. (C) A term connectome template was created by averaging the connectivity of the oldest 20
term- born neonates, followed by a linear decomposition to construct principal thalamocortical gradients. The remaining
neonate’s connectivity matrices were then individually decomposed and the resulting decompositions were aligned to the
template gradients for comparison.
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S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
Tractography streamlines were used to map high-
resolution thalamocortical connectivity maps ( Mansour L
etal., 2021). As most thalamocortical connections are ipsi-
lateral ( Dermon & Barbas, 1994), we only measured con-
nectivity from the left thalamic seeds to vertices in the left
cortical hemisphere. For all thalamic seeds, reconstructed
streamlines were assigned to the nearest cortical vertex
within a 5mm radius of their end point, and each stream-
line was weighted by the average of sampled mean frac-
tional anisotropy (FA) along its length. The connectivity
between each thalamic seed to a given cortical vertex was
then taken as the sum of streamline weights. Connectome
spatial smoothing (3mm FWHM, 0.01 epsilon) was subse-
quently performed to account for the susceptibility of high-
resolution connectomes to the impacts of accumulated
integration errors in streamline propagation ( Mansour
et al., 2022). This method involves applying a pair of
Gaussian smoothing kernels to adjust the strength of con-
nectivity across cortical vertices (Fig.1B) and has previ-
ously been shown to improve the reliability of individual
connectivity measures ( Mansour et al., 2022). Using this
approach, we created a dense connectome matrix sum-
marising white- matter connectivity between 800 thalamic
seeds and all 28,766 cortical vertices of the left hemisphere
(excluding those assigned to the medial wall; Fig.1C).
Reconstructing the structural connectivity from deep
thalamic structures (especially towards medial areas)
using tractography is challenging, due to the impact of
partial volume effects on these regions. Additionally, a
tract seeded from one of these regions needs to traverse
many voxels of low anisotropy to reach the cortex, reduc-
ing the likelihood that such a connection will be reliably
detected by tractography. These difculties may be com-
pounded by lower signal in neonatal imaging data. We
examined average connectivity of each seed to the cor-
tex (across individuals), identifying a set of seeds located
near to the midline with very low cortical connectivity
(Supplementary Fig.S1A). To avoid potential biases from
poor tracking from the medial wall, we removed low con-
nectivity seeds (connected to <100 cortical vertices;
Supplementary Fig. S1B). This resulted in 646 seeds
retained for the nal analysis (Supplementary Fig.S1C).
Cortical connectivity across seeds was normalised
using a scaled sigmoid transformation to the interval
[0,1]. This rst involved applying a sigmoidal transforma-
tion to the raw data:
S
x
( )
=
1
1+exp −x−(x)
σx
⎛
⎝
⎜⎞
⎠
⎟
,
(1)
where
S
x
( )
is the normalised value of a connection,
x
is the raw value,
x
is the mean, and
σ
x is the standard
deviation of the values of that connection across thalamic
seeds. Following the sigmoidal transform, cortical con-
nections were linearly scaled to the unit interval. This
transformation was used to reduce the impact of outliers
in the data ( Fulcher & Fornito, 2016; Parkes etal., 2017).
2.4. Gradient decomposition
We decomposed the concatenated 646- by- 28,766 (
n×m
)
normalised thalamocortical connectivity matrix
M
(
M
was
centred prior to the decomposition) into a set of orthogonal
components using Principal Component Analysis (PCA)
via Singular Value Decomposition (SVD):
M
=
USV
T
, (2)
where
U
is an
n×n
matrix of left singular vectors;
S
is
an
n×m
rectangular diagonal matrix of the singular val-
ues
s
of
M
; and
V
is an
m×m
matrix of right singular
vectors. This approach reduces the dimensionality of the
data by nding components (patterns of variation which
together maximise the variance explained in the data)
which are orthogonal to each other. The decomposition is
normally truncated to
k
<min n,m
( )
,
and the variance
explained by each component,
λ
k, is given by its singular
values,
sk
:
λk=sk
2
n−1
.
(3)
Therefore,
US
is a 646
×
k
matrix representing the Prin-
cipal Component (PC) scores, one per thalamic seed for
each of
k
components; and
V
is a 28,766
×
k
matrix rep-
resenting the PC loadings that denote the contributions of
each cortical vertex’s (normalised) thalamic connectivity
to each component. Each PC indicates a pattern of con-
nectivity that accounts for a proportion of the variance in
the data. The loadings for each PC indicate a pattern of
cortical connectivity, while the corresponding PC scores
indicate how strongly the connectivity from a particular
thalamic seed aligns with the cortical loadings. The sign of
the score indicates the direction of the association. A pos-
itive value means that seed is strongly connected to corti-
cal areas with positive loadings (for that PC), and weakly
connected to cortical areas with negative loadings. Simi-
larly, a negative score indicates the seed is weakly con-
nected to cortical areas with positive loadings, but strongly
connected to negative ones. The magnitude of the score
indicates the strength of this association.
2.5. Gradient alignment
To allow comparison of individual connectivity compo-
nents across the third trimester, we aligned all PC score
5
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
matrices using a Procrustes transform. To do so, we
selected the 20 oldest neonates who were born at term,
averaged their thalamocortical connectivity matrices, and
decomposed this average matrix via PCA to create a
“term template” of scores and loadings. The remaining
neonatal thalamocortical connectivity matrices not
included in construction of the term template were then
individually decomposed using PCA. A Procrustes rota-
tion (based on the rst ve components as to reduce
computational demands) was used to align each individ-
ual decomposition to the term decomposition (alignment
was performed on the
US
matrix, the resulting transforms
derived from this alignment were used to align the
V
matrix; Fig.1C).
2.6. Cortical null models
Tissue properties (thickness, cytoarchitecture, connectiv-
ity) vary smoothly across the cortex with nearby areas
sharing similar features, a phenomenon driven by spatial
autocorrelation ( Markello & Misic, 2021). The presence of
spatial autocorrelations can lead to overestimation of cor-
relations between different, smoothly varying cortical
properties. To mitigate this effect, we compared spatial
correspondences across cortical properties to a spatial-
autocorrelation- preserving null model using a permutation
test (via spin test) ( Alexander- Bloch etal., 2018; Markello &
Misic, 2021). The position of cortical vertices was rst ran-
domly rotated on the spherical representation of the corti-
cal surface. Each of the rotated vertices is then matched to
the closest original vertex, creating a mapping of rotated-
to- original vertices which can then be used to rotate the
values of a cortical feature. This vertex mapping preserves
the spatial autocorrelation and can then be used to con-
duct permutation testing. We assessed the Pearson cor-
relation between a pair of brain maps and compared it with
a distribution of correlations generated from 1,000 null
permutations (i.e., one of the brain maps was permuted
using the vertex mapping, and then the permuted map
was correlated with the other non-permuted brain map).
This process was then repeated in the opposite direction
(i.e., the second brain map was also permuted as to
remove any bias from only ever permuting one brain map),
and the average p- value was taken to obtain a spin- test
derived p- value (
p
spi
n
), which was considered signicant
at <0.05 ( Alexander- Bloch et al., 2018; Markello & Misic,
2021).
3. RESULTS
We used PCA to decompose a matrix of average thalam-
ocortical connectivity from 20 term- born neonates. The
rst three principal components (PCs) explained 48.6%,
28.6%, and 8.22% of variance, respectively. Thalamic PC
scores varied along an anterior/medial to posterior/lateral
axis (Fig. 2A) and were strongly correlated with both
medial- lateral (x axis; r=0.70, p<0.001; Supplementary
Fig. S2A) and anterior- posterior seed positions (y axis;
r=- 0.85, p<0.001; Supplementary Fig.S2B), but only
weakly correlated with inferior- superior position (z axis;
r=- 0.27, p<0.001). In contrast, thalamic PC2 scores var-
ied in a radial pattern outwards from a lateral anchor point
(Fig. 2B), while PC3 was oriented along an axis varying
from anterior/ventral posterolateral areas to posterior/ven-
tral lateral ones (Fig.2C). Compared with PC1, the spatial
patterns of PC2 and PC3 were only weakly correlated with
cardinal image axes (Supplementary Fig.S2D– I).
Thalamocortical connectivity is topographically
arranged, such that the spatial arrangement of thalamic
connections is mirrored in their cortical targets ( Jones,
2007). The topography of the thalamic PC scores is, in
turn, mirrored by the corresponding PC loadings of each
cortical vertex ( Oldham & Ball, 2023). Projecting the load-
ings for PC1 onto their respective cortical vertices
revealed a rostral- caudal gradient of connectivity
(Fig.2D). Rostral cortical areas were negatively loaded,
and thus preferentially connected to medial- anterior tha-
lamic regions, while caudal regions were positively
loaded, indicating preferential connectivity to thalamic
posterior- lateral regions. The cortical loadings for PC2
revealed preferential connectivity between the lateral
thalamus and primary sensory and motor cortex along a
dorsal- ventral axis (Fig.2E), while PC3 revealed preferen-
tial connectivity to frontal and parietal association areas
( Margulies etal., 2016; Sydnor etal., 2021) (Fig.2F).
This analysis demonstrates that a simple, low-
dimensional representation of thalamocortical connectivity
can efciently capture macroscale patterns of preferential
connectivity between regions of the thalamus and cortex,
around the time of birth. We next sought to determine how
these patterns are established in the time prior to birth. To
measure changes in thalamocortical connectivity across
the third trimester, we aligned individual connectivity
decompositions from the full cohort (scan age: 29–
45weeks) to the average term PC components, the “term
template”, via Procrustes rotations ( Vos De Wael et al.,
2020). The amount of variance explained for PC1 (34.4%;
SD=3.2), PC2 (21.5%; SD=2.1), and PC3 (7.5%; SD=0.9)
across individuals was lower than observed in the term PC
decomposition, likely reecting the greater amount of noise
present in individual data.
Overall, individual PCA decompositions of thalamocor-
tical connectomes were highly similar to the term tem-
plate (r = 0.90 to 0.99; Fig. 3A), revealing conserved
patterns of connectivity among individuals present from
at least 30weeks gestational age. However, we found that
6
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
similarity to average term connectome decomposition
was signicantly correlated with age at time of scan
(r=0.76, p<0.001), with greatest dissimilarity at the ear-
liest time points (Fig.3A). We conrmed that similar asso-
ciations were observed between PC loadings across
individuals, nding a strong correlation between similarity
to the term data and scan age (r=0.78, p<0.001; Fig.3B).
Similarity of PC2 and PC3 to the term template data was
also signicantly associated with age in the thalamus
(PC2: r=0.77, p<0.001, Supplementary Fig.S3A; PC3;
r=0.77, p<0.001, Supplementary Fig.S4A) and cortex
(PC2: r=0.74, p<0.001, Supplementary Fig.S3B; PC3;
r = 0.80, p < 0.001, Supplementary Fig. S4B). We also
compared how closely each individual’s decomposition
correlated with a template constructed from all other non-
duplicate scans, nding highly consistent results (PC1
score: r=0.68, p<0.001; PC2 loading: r=0.77, p<0.001)
to those obtained when using the term template.
These data suggest a gradual renement of thalamo-
cortical connectivity towards the time of normal birth. We
next sought to establish where in the thalamus and cor-
tex, age- related renement was most prominent by test-
ing the associations between thalamic seed PC scores
and scan age across individuals. As determined by a
Pearson correlation with a false- discovery rate correction
( Benjamini & Hochberg, 1995) of
p
FDR
<0.05
, for PC1 we
observed decreasing PC scores in the medial- anterior
thalamus and pulvinar with increasing age, while ventro-
lateral thalamic areas showed increases in PC scores
(Fig.3C). This reects an increasing differentiation of tha-
lamic connectivity patterns along PC1. A negative cor-
relation indicates an increasingly negative PC1 score
over the third trimester, while a positive correlation indi-
cates an increasingly positive PC1 score. This reects a
shift in the pattern of connectivity between that seed and
the cortex over development. For example, the connec-
tivity of seeds with high PC1 score (in the term template)
showing a positive correlation with age will have had their
connectivity with posterior areas strengthen (relative to
anterior areas) across the third trimester. In the cortex,
these changes in underlying connectivity are reected by
large age- related changes in PC1 loadings in parietal
(increases) and medial frontal areas and motor areas
(decreases; Fig. 3D). Similarly, differentiation between
thalamic seed connectivity patterns along PC2 and PC3
becomes more apparent with age (Supplementary
Figs.S3 and S4).
Age- related changes were only weakly correlated with
average term PC1 scores (
r=0.13
; Supplementary
Fig.S5A), suggesting that age- related changes were not
simply a strengthening or reinforcement of the existing
pattern in the thalamus. A stronger association was
observed in cortical vertex PC1 loadings and age- related
changes (
r=0.44
; Supplementary Fig. S5B), indicating
Fig.2. Axes of thalamocortical connectivity. (A) Projection of PC1 scores onto thalamic voxels, revealing variation
along medial- lateral and anterior- posterior directions. (B) Projection of PC2 scores onto thalamic voxels. (C) Projection of
PC3 scores onto thalamic voxels. PC scores for each seed are projected onto the closest voxels in the thalamic mask,
overlaid on six axial sections (inset; the colour of the number corresponds to the slice in the insert). (D) The PC1 loadings
for cortical regions are shown projected onto the cortical surface, revealing a rostral- caudal gradient of thalamocortical
connectivity. (E) PC2 loadings for cortical regions projected onto the cortical surface. (F) PC3 loadings for cortical regions
projected onto the cortical surface. Preferential connectivity between thalamic seeds and cortical vertices is shown by
similar colours for the respective PC.
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S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
that areas with the strongest positive or negative PC1
score displayed the strongest age- related increases or
decreases, respectfully, in gradient position. However,
there was noticeable variability in this relationship (e.g.,
some regions with a strong PC1 score displayed strong
decreases; this would indicate that these regions became
less preferentially connected to posterior areas across
development). We tested an alternative hypothesis: that
age- related changes occur along Cartesian thalamic
planes ( Oldham & Ball, 2023; Vogel etal., 2022). For PC1,
we observed signicant age- related changes in thalamic
scores along the medial- lateral (
r=0.32,p<0.001
) and
anterior- posterior (
r=−0.35,p<0.001
) axes (Supplemen-
tary Fig.S6A– C). Age- related changes in PC2 and PC3
showed stronger correlations (compared with PC1) with
their respective term template score/loadings, but
inspection of these relationships shows that considerable
heteroskedasticity in regional maturation of thalamic
seeds and cortical vertices positioned along each axis
(Supplementary Fig. S5C– F). These PC2 and PC3 tha-
lamic age- related changes showed the strongest associ-
ation with the ventral- dorsal plane (Supplementary
Fig.S6D– I).
Finally, we examined whether interruption to brain
development in the third trimester affects macroscale
patterns of thalamocortical connectivity. In total, n=108
neonates included in this study were born preterm (born
at less than 37weeks gestation), a demographic in which
abnormal thalamocortical connectivity has been previ-
ously observed ( Ball et al., 2013, 2015; Toulmin et al.,
2021). We selected a subset of scans where neonates
born premature had been subsequently scanned at term
equivalent age (n = 51; 25 females; median birth age
[range] = 33+1 weeks [23+4– 36+6]; median scan age
[range]=40+4 [37– 45+1] weeks), and a set of controls who
were born at term matched for age at scan and sex.
For each neonate, we calculated the total connectivity
of each vertex to the thalamus (i.e., summed the connec-
tivity across thalamic seeds for each vertex). Differences
between preterm and term neonates thalamocortical cor-
tical connections were measured by a two- tailed t- test,
controlling for scan age and gender, with a Threshold- Free
Cluster Enhancement correction ( S. Smith & Nichols,
2009). Signicant differences were detected by permuta-
tion testing (10,000 permutations) at a family- wise error
rate of
p
FWER
<0.05
, as implemented in Permutation Anal-
ysis of Linear Models (PALM) toolbox ( Winkler etal., 2014).
We found signicant differences in thalamocortical con-
nectivity in preterm infants, compared with term controls,
with decreased connectivity in frontal areas and occipital
areas but moderate increases in parietal areas (Fig. 4).
After controlling for scan age and sex, the amount of vari-
ance explained by PC1 (mean±SD % variance explained;
Term = 33.81 ± 3.09%, Preterm = 33.78 ± 2.29%;
F
1,98
( )
=
0.002,
p
=
0.99
7
) and PC3 (Term= 7.38 ± 0.62%,
Preterm = 7.65 ± 0.95%;
F
1,98
( )
=
0.002,p
=
0.08
7
)
Fig.3. Age- related changes in the primary thalamocortical axis (A) Scatter plot of the correlation between individual and
template PC1 thalamic scores and individual scan age. (B) Scatter plot of the correlation between individual and template
PC1 cortical loadings and individual scan age. (C) Correlation between individual PC1 scores and scan age for each
thalamic seed (voxels are coloured according to the value of the nearest seed; pFDR
<
0.0
5
; non- signicant thalamic areas
are not coloured). (D) Correlation between individual PC1 loading and scan age for each cortical vertex (
p
FDR
<
0.0
5
;
non- signicant vertices are not coloured).
8
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
did not signicantly differ between groups. The amount of
variance explained by PC2 did differ between preterm
and term neonates, although the difference was small
(Term = 22.26 ± 2.12%, Preterm = 21.04 ± 2.13%;
F
1,98
( )
=
0.002,
p
=
0.00
1
). In line with these results,
preterm– term differences were only weakly correlated with
PC loadings and age- related changes in PCs (Supplemen-
tary Fig.S7), indicating that the direction and magnitude of
primary axes of thalamocortical connectivity were largely
unaffected by preterm birth.
4. DISCUSSION
In this study, we describe macroscale patterns of thalam-
ocortical connectivity using high- resolution tractography
in a cohort of newborn infants. We observe a primary axis
of variation orientated along an anterior/medial- to-
posterior/lateral direction in the thalamus. This is consis-
tent with previous ndings in neonates ( Wilson et al.,
2023; Zheng et al., 2023) and adults ( Oldham & Ball,
2023). The primary thalamic axis is associated with a pat-
tern of preferential connectivity to cortical areas that var-
ies along a rostral- caudal direction, as observed in adults
( Oldham & Ball, 2023). The topographic arrangement of
thalamocortical bres is observed across species ( Brysch
etal., 1990; Höhl- Abrahão & Creutzfeldt, 1991), and sug-
gests a foundational principle of thalamic organisation.
Our results suggest that this basic topographic pattern is
present from the start of the third trimester. While our
observations are limited to newborn infants, including
those born preterm, recent work using foetal diffusion
MRI has reported a similar topographical arrangement as
early as the second trimester ( Wilson etal., 2023).
Thalamocortical axons begin to gather in the sub-
plate before innervating the cortex from mid- gestation
( Kostović & Judaš, 2010) with their spatial distribution in
this stage aligning with their eventual cortical targets
( Molnár etal., 2012). As such, the primary topographical
patterning of thalamocortical connections is likely estab-
lished prior to the third trimester ( Kostović & Judaš,
2010). Despite this, we observed signicant renement to
thalamocortical connectivity strength along primary
organisational axes between 30 and 45 weeks PMA. The
changing strength of preferential connectivity to cortical
areas is likely reective of the maturation of existing thal-
amocortical bres, potentially capturing processes that
include both the removal of supporting radial glial struc-
tures and the onset of myelination ( Machado‐Rivas etal.,
2021; Wilson etal., 2023).
We observed that age- related changes in the pattern
of connectivity associated with the primary thalamocorti-
cal component were most pronounced in medial- anterior,
ventro- lateral, and ventral thalamic areas, with preferen-
tial connectivity to cortical association areas. Previous
studies of the dHCP cohort have reported that the micro-
structure of thalamic subdivisions develops along a
lateral- to- medial temporal axis, while thalamic connec-
tions to the cortex develop along a posterior- anterior axis
( Zheng etal., 2023). Our results unify these observations
by demonstrating that observed thalamic and cortical
changes reect renements to the underpinning topo-
graphic pattern of thalamocortical connections. Further-
more, cortical association areas showed the greatest
changes in thalamocortical connectivity during the third
trimester, which aligns with broader cortical developmen-
tal patterns of delayed maturation in association and
frontal areas ( Tau & Peterson, 2010). Thalamocortical
innervations to cortical association areas occur later in
development ( Altman & Bayer, 1979, 1988a, 1988b,
1988c; Chaln et al., 2007; Finlay & Uchiyama, 2020),
Fig.4. Signicant differences in thalamocortical connectivity due to prematurity. (A) Differences between preterm and
term neonate thalamocortical connectivity in the cortex. The t statistic is positive when preterm neonates had higher
connectivity than term neonates and is negative when it was lower. Higher or lower values indicate the proportion of
connectivity in that area was increased or decreased, respectively, when compared with what was expected in term
neonates. Non- signicant areas (
p
FWER
<0.05
) are not coloured. (B) Mean connectivity of signicant clusters. We took the
mean connectivity of all vertices that showed a signicant negative or positive difference in thalamocortical connectivity
between preterm and term neonates (Term > Preterm corresponds to blue areas in (A), while Term < Preterm corresponds
to red areas). We then compared this mean connectivity between term (yellow) and preterm (purple) neonates. The line
indicates the mean for each group/cluster.
9
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
matching the pattern of thalamocortical development
observed in our results. Evidence suggests there is con-
siderable interplay between thalamic and cortical devel-
opment ( Antón- Bolaños etal., 2018; Finlay & Uchiyama,
2015, 2020; Park etal., 2024). Thalamocortical connec-
tivity contributes to the arealisation of the cortex ( Cadwell
et al., 2019; Finlay & Uchiyama, 2015, 2020; Molnár &
Kwan, 2024), and are involved in shaping the laminar
organisation of the cortex ( Dehay etal., 2001; Molnár &
Kwan, 2024; Pouchelon et al., 2014; Sato et al., 2022),
regulating cortical progenitors ( Gerstmann etal., 2015),
connectivity patterns ( Finlay, 1991; Finlay & Uchiyama,
2020), and the initialisation of functional dynamics
( Antón- Bolaños etal., 2018; Martini etal., 2021). Accord-
ing to the “handshake hypothesis," ascending thalamic
and descending cortical bres meet in the subplate and
reciprocally guide the tracts to their respective cortical
and thalamic targets ( Molnár et al., 2012; Molnár &
Blakemore, 1995), further suggesting interactions
between cortical and thalamic development are vital to
shaping both structures. Our observations of age- related
changes in thalamocortical connectivity t with these
wider ndings that underscore the interdependence
between thalamic and cortical development to shape the
maturation and functional organisation of the developing
brain. As thalamocortical connectivity is related to multi-
ple facets of cortical organisation ( Oldham & Ball, 2023),
and is considered to constrain how this organisation is
established ( Park etal., 2024), further investigation of the
relationship between thalamocortical connectivity and
major cortical organisational properties (e.g., integration/
segregation, intrinsic timescales, cortical thickness) is
warranted to more deeply understand the role of these
connections in neurodevelopment.
We note that changes in the strength of PC scores and
loadings with age were closely aligned with Cartesian
anterior- posterior, medial- lateral, and inferior- superior
axes. Spatial axes constitute a critical foundation for
early brain development, corresponding to the direction
of early molecular gradients ( Vogel etal., 2022). Thalamic
microstructure varies along cardinal planes ( Altman &
Bayer, 1979; Nakagawa, 2019; Scholpp & Lumsden,
2010; Wong et al., 2018) and neurogenesis progresses
from the lateral to medial thalamus ( Altman & Bayer,
1979; Nakagawa, 2019; Wong etal., 2018) with thalamic
subdivisions also emerging along this same axis ( Zheng
etal., 2023), and myelination of white matter tracts occur-
ring along an anterior- posterior direction ( Abe etal., 2004;
Counsell etal., 2002; Machado‐Rivas etal., 2021). There-
fore, the orientation of the primary thalamocortical con-
nectivity components dened here may arise from the
intersection of different developmental gradients. The
temporal interaction of these gradients could account
for the observed heterogeneity in age- related changes
across thalamocortical axes.
Linear and nonlinear decomposition methods have
become increasingly common in the neuroimaging litera-
ture to generate low- dimensional representations of
complex imaging data ( Margulies etal., 2016; Oldehinkel
et al., 2023; Paquola et al., 2020; Vos De Wael et al.,
2020). However, recent studies have highlighted potential
biases in PCA decompositions of spatially autocorrelated
data ( Novembre & Stephens, 2008; Shinn, 2023; Watson
& Andrews, 2023), whereby PCs can resolve into distinc-
tive sinusoidal patterns that do not necessarily reect the
true structure of the underlying data. To avoid interpreting
“phantom” oscillations, we turn to converging lines of
evidence from imaging ( Lambert et al., 2017; Oldham &
Ball, 2023; Pang et al., 2023; Yang et al., 2020; Zheng
etal., 2023), histological/cellular data ( Roy etal., 2022),
animal tract tracing ( Brysch etal., 1990; Höhl- Abrahão &
Creutzfeldt, 1991), and transcriptomics ( Phillips et al.,
2019; Vogel etal., 2022) that support the organisation of
thalamic neurobiology along major cardinal planes, like
those we observe in the current study. These convergent
ndings using different modalities indicate the observed
axes are not merely a statistical artefact. There are dis-
tinctive spatiotemporal dynamics of molecular expres-
sion during thalamic development ( Kim et al., 2023),
which likely drive the formation of continuous axes of
neurobiological features ( Le Dréau & Martí, 2012; Sansom
& Livesey, 2009). Determining how various organisational
axes of different brain systems are aligned and function-
ally related; how common or distinct mechanisms may
shape their emergence; and how these patterns are
related to cognition, behaviour, sensation, and symptom-
ology is key to understanding thalamocortical organisa-
tion and development.
The nuclear organisation of the thalamus is extremely
well characterised ( Jones, 2007; Phillips etal., 2019; Roy
etal., 2022), and distinct developmental processes are
associated with different nuclei ( Huang et al., 2024;
Nakagawa, 2019). The presence of continuous develop-
mental gradients and axis of connectivity should be con-
sidered a complementary organisational principle. For
example, the core matrix ( Jones, 1998) and higher rst
order theories of thalamic organisation ( Guillery &
Sherman, 2002; Sherman, 2016; Sherman & Guillery,
2002) propose that nuclei have both diffuse and focal
cortical targets (with nuclei varying in their ratio of these
connection types). These diffuse connections, which
extend across multiple cortical areas ( Jones, 1998),
potentially correspond to the principal components
dened herein, rather than the more areal specic, tar-
geted connectivity that denes focal/core connections
( Jones, 1998) as the extracted connectivity components
10
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
smoothly vary across the cortex without respect to areal
boundaries. In addition, diffuse and specic thalamocor-
tical connections may emerge due to distinctive develop-
mental events. For example, matrix axons have been
suggested to invade the cortex prior to core axons
( Clascá etal., 2012), or alternatively that core/rst- order
connections develop from matrix/higher order connec-
tions ( Lo Giudice etal., 2024). By denition, PCA extracts
patterns which explain the most variance in the data,
therefore, the rst few components are expected to
dene widespread connectivity patterns. This does not
preclude the existence of focal connectivity patterns,
which are a highly important aspect of thalamic organisa-
tion and function ( Bosch- Bouju et al., 2013; Fama &
Sullivan, 2015; Guillery & Sherman, 2002; Jones, 2007),
but these are likely to explain less variance in the data as
such connectivity will only apply to a select subset of tha-
lamic seeds. Establishing methods which can disentan-
gle the focal and diffuse connectivity patterns of the
thalamus is important to allow more detailed exploration
of thalamic organisation and its development ( Howell
etal., 2024).
We also observed signicant differences in thalamo-
cortical connectivity between preterm and term- born
neonates. At term- equivalent age, preterm infants had a
lower proportion of thalamic connections to frontal and
occipital areas compared with term- born peers, with
some parietal areas showing a higher proportion. These
areas affected by prematurity are similar to what previous
studies have reported ( Ball et al., 2012, 2013, 2015;
Batalle et al., 2017; Jakab etal., 2020; Sa De Almeida
et al., 2021), although the abnormalities we observed
were not as widespread or pronounced. This could be
because in previous studies, preterm neonates had a
greater degree of prematurity than the cohort considered
in this study, and neurodevelopmental abnormalities
scale with the severity of preterm birth ( Drommelschmidt
et al., 2024). Premature birth disrupts normal develop-
mental processes that shape brain connectivity through
inammation, infection, and/or perinatal hypoxia ( Deng,
2010; Volpe, 2009). In particular, preterm birth is linked to
subplate damage, a structure critical to enabling a variety
of maturational processes such as synaptogenesis and
axonal guidance ( Volpe, 2009). Therefore, damage to it
may impair the proper establishment of thalamocortical
connectivity. While we observed impaired connectivity in
the preterm neonates of this study, these impairments
were only weakly correlated with the major axes of thal-
amocortical connectivity, indicating that prematurity did
not fundamentally change the core organisational topog-
raphy of thalamocortical connections. As the topograph-
ical organisation of thalamic connections is established
early in gestation ( Kostović etal., 2019; Kostović & Judaš,
2010) and diffuse connections form potentially prior to
functionally specialised, focal connections ( Clascá etal.,
2012; Lo Giudice et al., 2024), the adverse effects of
preterm birth may be more evident in focal thalamocorti-
cal connections, rather than macroscale, diffuse organi-
sational patterns, but further characterisation of focal and
diffuse thalamocortical connectivity during early neuro-
development will be required to further advance this
hypothesis.
Our study has several limitations which are important
to note. First, we used MRI scans of preterm infants to
investigate thalamocortical development prior to the
time of normal birth. While prematurity does alter the
developmental trajectory of structural thalamocortical
connectivity ( Ball etal., 2012, 2013; Batalle etal., 2017;
Sa De Almeida et al., 2021), the major developmental
trends— such as would be captured by the rst few
components— are likely to be more pronounced than
such differences this would introduce ( Zhao etal., 2019;
Zheng et al., 2023). This is supported by our ndings
showing prematurity was only weakly related to thalam-
ocortical gradient patterning. Secondly, we only focused
on the left hemisphere in our analysis, as the majority
of thalamocortical connections are ipsilateral ( Dermon &
Barbas, 1994). However, for a complete representation
of thalamocortical connectivity, future studies should
endeavour to measure connections across both hemi-
spheres. A more accurate characterisation of thalamo-
cortical connectivity may also be achieved by using
alternative measures to dene white matter connections.
Using measures of connectivity aimed at more explicitly
modelling the biological properties of axonal bres
( Raffelt et al., 2017; R. E. Smith et al., 2022; F. Zhang
et al., 2022; H. Zhang et al., 2012) may yield deeper
insights into the maturational patterns of thalamocortical
connectivity. Thirdly, conducting tractography from the
deep thalamus is challenging which is compounded by
using a neonatal cohort. Neonates are more susceptible
to head motion, which is disruptive to diffusion sequences
( Heemskerk etal., 2013; Pannek etal., 2012), which can
be highly difcult to correct for and so residual motion
artefacts may still be present in our data. We used data
processing with optimised pipelines to migrate these
issues ( Bastiani etal., 2019) and employed strict quality
control to minimise any impact to our results. Our thal-
amocortical ndings are consistent with histological
and animal studies ( Brysch etal., 1990; Höhl- Abrahão &
Creutzfeldt, 1991), and are in agreement with thalamo-
cortical connectivity observed in adult imaging data
( Howell et al., 2024; Oldham & Ball, 2023), and other
early life studies ( Wilson etal., 2023; Zheng etal., 2023),
adding further condence our results are not adversely
affected by motion- induced distortions.
11
S. Oldham, S. Mansour L. and G. Ball Imaging Neuroscience, Volume 3, 2025
In summary, this study investigated the development
of thalamocortical connectivity in the perinatal period. We
nd that a primary thalamocortical axis, describing
changing patterns of cortical connectivity along an ante-
rior/medial- to- posterior/lateral orientation in the thala-
mus, is established by 30weeks gestation. Changes to
this axis prior to the time of normal birth are largely driven
by maturation of connections between the thalamus and
associative cortical areas. Finally, thalamocortical con-
nectivity differences due to prematurity were only weakly
related to thalamocortical axes, suggesting the conser-
vation of these major organisational features following
preterm birth.
DATA AND CODE AVAILABILITY
Neuroimaging data for the developing Human Connec-
tome Project are available on the NIMH Data Archive.
Instructions on how to access are available here: https://
biomedia . github . io / dHCP - release - notes/. Processed data
are available on Zenodo (https://zenodo.org/records
/11059162) and code is available at https://github . com
/ StuartJO / ThalamicDevGrad.
AUTHOR CONTRIBUTIONS
Stuart Oldham: Conceptualisation, Methodology, Formal
analysis, Resources, Data curation, Writing— Original draft,
Writing— Review & editing, Visualization. Sina Mansour L:
Methodology, Writing— Review & editing. Gareth Ball:
Conceptualization, Supervision, Project administration.
DECLARATION OF COMPETING INTEREST
The authors have no competing interests to declare.
ACKNOWLEDGEMENTS
The authors would like to thank Ashlea Segal for provid-
ing technical assistance for the analysis. This research
was supported by an NHMRC Investigator Grant
(1194497 to G.B.), the Murdoch Children’s Research
Institute, the Royal Children’s Hospital, Department of
Paediatrics, The University of Melbourne and the Victo-
rian Government’s Operational Infrastructure Support
Program. The project was generously supported by The
Royal Children’s Hospital Foundation devoted to raising
funds for research at The Royal Children’s Hospital. Data
were provided by the developing Human Connectome
Project, KCL- Imperial- Oxford Consortium funded by the
European Research Council under the European Union
Seventh Framework Programme (FP/2007- 2013)/ERC
Grant Agreement no. [319456]. We are grateful to the
families who generously supported this trial.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available with
the online version here: https://doi . org / 10 . 1162 / imag _ a
_ 00418.
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