=Paper= {{Paper |id=Vol-3363/paper.13 |storemode=property |title=Thalamic features extraction and analysis in magnetic resonance imaging of preterm infants |pdfUrl=https://ceur-ws.org/Vol-3363/paper13.pdf |volume=Vol-3363 |authors=Emiliano Trimarco,Bahram Jafrasteh,Simón Pedro Lubián-López,Isabel Benavente-Fernández |dblpUrl=https://dblp.org/rec/conf/determined/TrimarcoJLB22 }} ==Thalamic features extraction and analysis in magnetic resonance imaging of preterm infants== https://ceur-ws.org/Vol-3363/paper13.pdf

Thalamic features extraction and analysis in magnetic
resonance imaging of preterm infants
Emiliano Trimarco1,*,† , Bahram Jafrasteh1,† , Simoń Pedro Lubiań-Loṕez2,3 and
Isabel Benavente-Fernańdez1,2,3
1
Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University, Cádiz, Spain
2
Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cad́iz, Spain
3
Area of Paediatrics, Department of Child and Mother Health and Radiology, Medical School, University of Cad́iz, Cad́iz,
Spain

Abstract
Preterm birth is the primary cause of infant death and is associated with later neurodevelopmental
impairments. Neuroimaging is a powerful tool to analyse neuroanatomy abnormalities in preterm infants.
It allows analysing of different brain structures, such as the thalamus and their alterations. Thalamus
is a crucial hub for regulating cortical connectivity. Moreover, white matter (WM) injury in preterm
infants can impact thalamic growth and maturation in long-term periods. Therefore, the study of the
thalamus morphology during the neonatal period using magnetic resonance imaging (MRI) can help to
identify those features that predict neurodevelopmental outcomes in these vulnerable population. In
this study, we automatically segmented the thalamus structure from 3D MRI scans and extracted the
thalamic features from these segmentations. The gestational age at birth and post-menstrual age at the
scan time is also taken into account in our study. The K-means clustering, an unsupervised machine
learning algorithm, was employed to explore the hidden pattern related to thalamus features from early
and term-equivalent scans. Finally, we studied the association of these features to a scoring system used
in clinical settings to assess MRI scans in very preterm infants at term-equivalent age. The main results
highlight that 77 percent of preterm-born infants with abnormal MRI scores share the same cluster.

Keywords
Thalamus, K-means clustering, Atlas-based segmentation, Preterm infants

1. Introduction
Preterm birth, before 37 weeks of gestation, affects fifteen million children each year in the world
[1]. It remains the main cause of infant death [1]. The severity of long-term neurodevelopmental
impairments increases with decreasing gestational age [2]. In particular, early exposure to
extrauterine life is closely associated with deficits in cognitive, motor, visual, socio-emotional,
sleep, and language domains [3]. The thalamus is a meaningful hub that shapes brain connec-
tivity during prenatal and postnatal life. It is commonly affected in preterm infants by white

DETERMINED 2022: Neurodevelopmental Impairments in Preterm Children — Computational Advancements,
August 26, 2022, Ljubljana, Slovenia
*
Corresponding author.
†
These authors contributed equally.
$ emiliano.trimarco@inibica.es (E. Trimarco); jafrasteh.bahram@uca.es (B. Jafrasteh);
simonp.lubian.sspa@juntadeandalucia.es (S. P. Lubiań-Loṕez); isabel.benavente@uca.es (I. Benavente-Fernańdez)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)

167
matter (WM) injuries, either directly or through maturational disruption [4, 5]. Preterm birth
influences the growth of thalamocortical connectivity and the steps in the sensory organisation
and functional specialisation of the cerebral cortex [6, 7]. Thalamo-cortical connectivity is
regionally altered for preterm infants, and the thalamic volume is related to both the cortical
volume and the WM tracts [8]. There is also some evidence that alterations in fronto-temporal
and parieto-occipital cortical areas are related to the thalamic structural connectivity, and the
volumetric measurements obtained from the thalamic region [9]. Thalamocortical connectivity
abnormalities identified after preterm birth can be correlated with the future neurodevelopmen-
tal impairments [10, 11, 12].
In this study, we develop a protocol to evaluate the importance of thalamic features of preterm
infants. Our hypothesis aims to relate the morphological characteristics of the thalamus to
the Kidokoro score [13]. Firstly, we use an automatic method to segment Magnetic Resonance
Images (MRIs) from a preterm infant cohort [14]. Then, we extract morphological features from
the region of interest (ROI), i.e. the segmented thalamus area. After an exploratory analysis
of the extracted features, an unsupervised machine learning algorithm is used to cluster the
features. Finally, we show that it is possible to cluster the abnormal MRIs through thalamus
measurements using term-equivalent scans. In addition, the results show the extracted fea-
tures are sufficient to differentiate between healthy term-born infants and preterm infants at
term-equivalent age.

2. Method
2.1. Atlas correction and automatic thalamus segmentation
Melbourne Children’s Regional Infant Brain (M-CRIB 2.0) atlas [15] is used to segment thalamus
structure from the MRI images of our cohort. In particular, the atlas contains ten scans from
healthy term-born infants [15].
Preliminary visualization of the M-CRIB 2.0 atlas showed an overestimation of thalamic seg-
mentation, including the nuclei and the hippocampal gyrus. Therefore, an expert in our group
reviewed and corrected thalamic segmentation manually. We automatically segmented the
thalamus from the MRI images according to the neonatal pipeline proposed by Makropoulos et
al. [14, 16]. In principle, the pipeline registers the image to a neonatal atlas image at a similar
gestational age [16] to separate non-cortical grey matter from the WM, grey matter and the
cerebrospinal fluid (CSF). Then, the image is registered to the M-CRIB 2.0 atlas. Finally, local
atlas weighting and DrawEM [16] are used to separate the thalamus from the other brain struc-
tures. This pipeline is widely used and supported by the literature studies [17, 18]. According
to the changes made in the atlas, we adapted the pipeline [14] for thalamus segmentation. The
clinical experts in the group have verified the quality of the automatic segmentation.
Figure 1(a)-1(c) shows an example of the original scan from the atlas, a corrected scan with
a redundant part, and one of the segmentation provided by the pipeline after the correction,
respectively.

168
(a) (b) (c)

(d) (e) (f)

Figure 1: Atlas correction and the segmentation results. a) A slice from the coronal plane of the original
atlas T2. b) A slice from the coronal plane of the correction in the atlas. c) A slice from the coronal
plane of the corrected thalamus segmentation in the atlas. d) A slice from the coronal T1 plane of the
preterm infant at term-equivalent age. e) A slice from the coronal plane of the automated segmentation
in preterm infants at term-equivalent age. f) A slice from the coronal plane of segmented thalamus in
preterm infants at term-equivalent age. The green colour indicates thalamus structure, and the purple
colour refers to the corrections made by an expert on the atlas.

2.2. Thalamic feature extraction
We extracted ten features from the segmented thalamus (Table 2). All the volumetric mea-
surements have been standardised by the Total Brain Volume (TBV). We prioritise TBV over
Intra-Cranial Volume (ICV) as ICV includes extra-axial CSF. A physiological increase in extra-
axial CSF in preterm infants may facilitate suboptimal brain growth in the neonatal period.
Therefore, it is crucial to prioritise the TBV over the ICV to ensure measuring real brain tissue.
The other measurements, like area, have been standardised by the maximum brain area at
the axial plane, where the thalamus has the largest area. The following thalamus features are
summarised in Figure 2 and Table 1: post-menstrual age (PMA) at the scan time, the TBV, the
Standardised Left Thalamus 3D Surface (SLTS) and the Standardised Right Thalamus 3D Surface
(SRTS). Furthermore, the other extracted variables are reported in Table 2. Notably, the 3D
surface of the thalamus is standardised by the largest area of the brain in the axial plane. In

169
this way, we obtain an indirect measurement regarding the relationship between the thalamus
and general brain maturation. The distributions of the four variables are shown in the diagonal
boxes of Figure 2 and in Table 1. Moreover, Figure 2 shows the two-way relationships between
these variables. For example, the last row of Figure 2 reveals that the volume brain increases
with increasing PMA, but the standardised 3D surface of the thalamus decreases with increasing
PMA. Since the brain regions grow considerably during this period, this behaviour is seen in
this figure [19], and their proportion to the thalamus changes. Therefore, it affects the data
standardisation and a value decrease does not concur with a natural reduction. It is relative to
the growing trend of the TBV.

800

600
TBV

400

200

0.10

0.08

0.06
SLTS

0.04

0.02

0.00

0.100

0.075
SRTS

0.050

0.025

0.000

40
PMA

200 400 600 800 0.02 0.04 0.06 0.08 0.025 0.050 0.075 30 40
TBV SLTS SRTS PMA

Figure 2: Distribution of Total Brain Volume in cm3 (TBV), Standardised Left Thalamus 3D Surface
(SLTS) and Standardised Right Thalamus 3D Surface (SRTS), post-menstrual age in weeks (PMA) in the
diagonal boxes and the two-way relationships in the other boxes

170
Table 1
Statistical description of Total Brain Volume (TBV), Standardised Left Thalamus 3D Surface (SLTS) and
Standardised Right Thalamus 3D Surface (SRTS), post-menstrual age (PMA)
TBV (cm³) SLTS SRTS PMA (weeks)
Mean 402.37 0.041 0.042 36.494
S.D. 156.47 0.018 0.019 5.462
Min 153.60 0.016 0.016 26.715
25% 244.80 0.029 0.030 30.571
50% 448.93 0.036 0.037 39.715
75% 527.14 0.055 0.056 40.857
Max 830.73 0.086 0.092 44.571

2.3. MRI Score
MRI scans were acquired using a 1.5 Tesla scanner (Magneton Symphony, Siemens Health
Care, Erlangen, Germany) located in the radiology unit in the University Hospital of Puerta
del Mar (HUPM), Cadiz, Spain. The acquisition parameters are as follows: spacing in x, y and
z direction : 0.8, 0.8, 0.8; echo time = 3.67 ms; flip angle = 15°and repetition time = 1910.0 ms.
T1 weighted spin echo imaged sequences were used to collect our data. Potential risks caused
by the physical properties of the MRI equipment were evaluated and minimised following the
recommendations provided for preterm infants [20] and our previous experiences [21, 22]. The
images obtained from the scans were evaluated through the clinicians’ observation using a
scoring system developed by Kidokoro et al. [13]. It provides a comprehensive and objective
characterisation of the regional and global brain lesions and brain growth. In particular, it is
used to confirm the clustering results and check whether patients with an abnormal score are
clustered into the same group (For more details, see section 4.2). The scoring system suggested
by Kidokoro et al. [13] groups the global score into four categories: (normal, mild, moderate,
and severe). We then binarised the variable by considering normal versus abnormal MRI (the
latter including mild, moderate and severe) as we wanted to see if the thalamic features could
be associated with any degree of MRI abnormality.

2.4. K-means clustering
Given a set of observations having 𝐷 dimensions, the k-means clustering as an unsupervised
machine learning algorithm aims to partition the observations into 𝑘 different groups by
minimising within cluster sum of the squared error without having access to the outcomes.
We set K to three in our analysis because our dataset has three main groups (see section 3).
It should be noted that clustering is only carried out based on the thalamic features. Table 2
shows the included attributes for K-means clustering. The K-means clustering is also performed
using morphological features extracted from the atlas images. Moreover, the score proposed in
Kidokoro et al. [13] was used to validate the K-means algorithm. In conclusion, according to the
preliminary statistical analysis, the plots of each feature vs others (Figure 4), and other algorithms
comparison, we conclude that K-means clustering as a simple algorithm can efficiently cluster
our dataset (table 4).

171
Table 2
List of dataset variables included in the clustering. Total Brain Volume (TBV), Standardised Left Thalamus
3D Surface (SLTS), Standardised Right Thalamus 3D Surface (SRTS), post-menstrual age (PMA)
Variable Clustering
TBV (cm ) 3
included
Left Thalamus Volume (cm3 ) included
SLTS included
Left Thalamus perimeter (cm) included
Left Thalamus Angle (degrees) included
Right Thalamus Volume (cm3 ) included
SRTS included
Right Thalamus perimeter (cm) included
Right Thalamus Angle (degrees) included
Distance Left and Right Thalamus cm included
Angle between Left and Right Thalamus included
Left Centroid not included
Left Highest Point not included
Right Centroid not included
Right Highest Point not included
Kidokoro score not included
SEX not included
GA not included
PMA not included

3. Experimental configuration and cohort
We included 48 scans from 31 patients of a longitudinal cohort that involves preterm infants
from the preterm cohort at Hospital Puerta del Mar (HUPM), Cad́iz, Spain, with very low
weight at birth, equal or <1,500 grams, and/or gestational age (GA) at birth equal or <32 weeks.
The parents or legal guardians of these infants have signed the informed consent. Data were
recorded prospectively from these patients as they underwent MRI as part of a cohort study of
the preterm brain damage group at the Biomedical research and innovation institute of Cad́iz
(INIBICA). GA is calculated from the date of the last menstrual period and confirmed using
data from early antenatal ultrasound scans. The weeks of postnatal life (age) are added to the
weeks of GA at birth, giving the so-called post-menstrual age (PMA). Typically, two MRI scans
are taken from each infant. An early scan was performed within the first ten days of life, and
a late one was at the term-equivalent age (38–42 weeks of corrected age), according to PMA.
Following this principle, the initial 48 MRI scans are divided into two groups, i.e. 23 early scans
and 25 term-equivalent scans (17 patients have both scans).In addition, 12 patients are identified
as abnormal in agreement with Kidokoro et al. [13]. Therefore, early and term-equivalent scans,
plus abnormal/normal MRI scores, provide four different groups: early normal MRI score, early
abnormal MRI score, term-equivalent normal MRI score, and term-equivalent abnormal MRI
score. Moreover, ten scans from the M-CRIB 2.0 atlas [15] are added to the analyses. These
scans are from healthy term-born neonates and are used as the control group.

172
4. Results
4.1. Analysis of extracted features
We extracted ten features from the segmented thalamus (see section 2.2). As visualizing all these
features are not easy, we rely on the dimension reduction methods such as principal component
analysis (PCA). Figure 3 shows the results of PCA on term-equivalent and M-CRIB 2.0 atlas [15]
scans. Initial results demonstrate that the first five principal components can explain more than
92% of the variabilities among features. Therefore, these components are enough to explain
our data. Table 3 shows the percentage of explained variance for each component. The first
two components explain more than 60% of the variation among thalamic features. Moreover,
Figure 3, indicates that it is possible to separate the M-CRIB 2.0 atlas scans from those of the
preterm infants in our cohort according to the first two components. One of the advantages of
PCA is its interpretability. For example, ID 23, highlighted in red, shows an anomaly in its first
component with a value less than -0.9. This result suggests that clinicians should check this
infant. In addition, according to the Kidokoro assessment score, this ID has an abnormal MRI
score.
PCA heatmap

1.0 0.5 0.0 0.5 1.0

patient ID
210 209 208 207 206 205 204 203 202 201 172 171 170 169 167 166 165 164 26 25 24 23 20 19 16 15 13 12 11 10 9 8 5 4 3

2
PCA

Figure 3: Visualising the PCA components of thalamic measurements in preterm infants at term-
equivalent age and healthy term-born ones. The horizontal axis of the figure shows the anonymised ID
of the patients, and the vertical one shows the number of principal components. The atlas scans IDs
start from 201. The box in black colour separates the atlas scans from the rest. Notice that we use 5
components for PCA analysis.

PCA components 1 2 3 4 5
Explained variance 34.7 28.1 13.6 10.5 5.7

Table 3
The percentage of variance explained in measurements of thalamic features from different components
of PCA.

173
4.2. Clustering results
After clustering, the three clusters are represented by different coloured points and also compared
with MRI score [13] (abnormal = green star, normal = magenta cross) and M-CRIB 2.0 atlas [15]
(yellow cross) in Figure 4. All the images in the M-CRIB 2.0 atlas [15] are correctly classified in
the third cluster. These findings highlight the significant difference between healthy term-born
infants and preterm infants at term-equivalent age. Furthermore, Figure 4 also shows that most
premature infants with abnormal MRI scores are in the second cluster, i.e. 77%. However, the
clustering of thalamic features of the early scan group does not differentiate between abnormal
and normal term-equivalent MRI. The second cluster gets the highest probability values for
early scans, i.e. 47%. The clustering accuracy is summarized in Table 4.
PMA vs Cluster PMA vs TBV
3 Abnormal Abnormal
Atlas Atlas
Normal 800 Normal
K-means clustering results

Clsuter 1 Clsuter 1
Clsuter 2 Clsuter 2
Clsuter 3 Clsuter 3
700

2 TBV 600

500

400
1

36 38 40 42 44 36 38 40 42 44
PMA PMA

PMA vs SRTS PMA vs SLTS
0.06
Abnormal Abnormal
0.06 Atlas Atlas
Normal Normal
Clsuter 1 Clsuter 1
Clsuter 2 0.05 Clsuter 2
0.05 Clsuter 3 Clsuter 3
SLTS
SRTS

0.04
0.04

0.03
0.03

0.02 0.02

36 38 40 42 44 36 38 40 42 44
PMA PMA

Figure 4: The plots of post-menstrual age in weeks (PMA) vs Total Brain Volume in cm3 (TBV),
Standardised Left Thalamus 3D Surface (SLTS) and Standardised Right Thalamus 3D Surface (SRTS).

Table 4
The percentage of abnormal infants in each cluster for the early and term-equivalent scans.
Cluster 1 Cluster 2 Cluster 3
Early scans 28% 47% 25%
Term-equivalent scans 23% 77% 0%

174
5. Discussion
The K-means clustering correctly separated 77% of abnormal patients into the correct cluster
(Table 4) and correctly distinguished all healthy neonates from the M-CRIB 2.0 atlas images, as
our reference group (Figure 4). The separation of the third cluster seems easy as the first and
the second component of PCA results (Figure 3) indicate the difference in the thalamic features
between healthy term-born infants and preterm infants at term-equivalent age.
Nevertheless, the clustering of thalamic features of the early scans does not differentiate between
the abnormal and normal MRI (Table 4). This result could be explained by postnatal brain
maturation, as the MRI score system only applies to term-equivalent scans, so the patient’s
situation can change from one scan to the next. In particular, a patient could have a normal
early MRI scan and develop clinical complications that will lead to brain injury and an abnormal
term-equivalent MRI. After describing the thalamic features of a cohort of preterm and term-
born infants related to GA at birth and PMA at the time of scans, we show how the thalamic
features can be associated with clinical MRI scores. Furthermore, they share three clustering
patterns: the first cluster can be interpreted as patients with normal MRI score, the second
cluster can belong to the abnormal MRI score, and the third cluster can be associated with the
M-CRIB 2.0 atlas scans.
Some other groups have done previous research on this topic. For example, Ball et al. [12], Jakab
et al. [10] and Menegaux et al. [23] focused on the diffusion-weighted imaging. In contrast,
we focus on the T1-weighted images in the current study. Furthermore, our work includes a
detailed analysis of the thalamic features, while the work published by Wisnowski et al., [24]
Lao et al. [25] and Loh et al. [26] considered only one feature, i.e. thalamic volume. Interestingly,
our results align with those from Lao et al. [25], who described the standardised 3D surface as
an important thalamic feature. Our study includes a more exhaustive analysis of the thalamic
features and extensively extracted 2D parameters, including the thalamic perimeter where the
largest thalamic area was found in the axial plane, and 3D information from the thalamus [25].
Moreover, we have normalised the thalamic volume to the TBV and studied the association
between the specific morphological characteristics of the thalamus and the Kidokoro score [13]
at the term-equivalent MRI.
According to Kostović et al.[19], during the beginning of the third trimester of fetal development,
thalamocortical and cortico-cortical afferents migrate to the cortex and finally form their primary
connections. The ontogeny of this migration process suggests that these connections grow with
different starting times but from the same point. Consequently, the other brain regions grow
considerably during this period, and their proportion to the thalamus significantly changes (see
figure2). Conclusively, damage in the preterm brain affects thalamus features and their relation
with the TBV. In some extreme cases, the atlas-based segmentation includes other structures
and overestimates the thalamus and its features. Manual segmentation and the development of
advanced machine learning methods can help to solve this problem.

175
6. Conclusion
In the current study, we associated the thalamic features with the MRI score assessment of
preterm infants and explored the importance of thalamic features for the clustering of the
patients. The standardised thalamic 3D surface can be suggested as a crucial morphological
feature to cluster patients. Further studies, including a bigger sample size and external validation,
are warranted to investigate the potential role of these thalamic features as a diagnostic and
predictive tool of brain injury and long-term neurodevelopmental outcomes in preterm infants.

Acknowledgments
This study was funded by the PARENT project from the European Union’s Horizon 2020 research
and innovation program under the Marie Sklodowska-Curie Innovative Training Network 2020.
Grant Agreement N 956394. BJ, SPLL and IBF acknowledge funding from the Cadiz integrated
territorial initiative for biomedical research, European Regional Development Fund (ERDF)
2014–2020. Andalusian Ministry of Health and Families, Spain. Registration number: ITI-0019-
2019.

References
[1] M. Delnord, J. Zeitlin, Epidemiology of late preterm and early term births–an international
perspective, in: Seminars in Fetal and Neonatal Medicine, volume 24, Elsevier, 2019, pp.
3–10.
[2] N. Marlow, Outcomes of preterm birth and evidence synthesis, Developmental Medicine
& Child Neurology 60 (2018) 330.
[3] A. Pascal, P. Govaert, A. Oostra, G. Naulaers, E. Ortibus, C. Van den Broeck, Neurodevel-
opmental outcome in very preterm and very-low-birthweight infants born over the past
decade: a meta-analytic review, Developmental Medicine & Child Neurology 60 (2018)
342–355.
[4] W. Y. Loh, P. J. Anderson, J. L. Cheong, A. J. Spittle, J. Chen, K. J. Lee, C. Molesworth,
T. E. Inder, A. Connelly, L. W. Doyle, et al., Longitudinal growth of the basal ganglia and
thalamus in very preterm children, Brain imaging and behavior 14 (2020) 998–1011.
[5] J. J. Volpe, Brain injury in premature infants: a complex amalgam of destructive and
developmental disturbances, The Lancet Neurology 8 (2009) 110–124.
[6] I. Kostović, M. Judaš, The development of the subplate and thalamocortical connections in
the human foetal brain, Acta paediatrica 99 (2010) 1119–1127.
[7] H. Toulmin, J. O’Muircheartaigh, S. J. Counsell, S. Falconer, A. Chew, C. F. Beckmann, A. D.
Edwards, Functional thalamocortical connectivity at term equivalent age and outcome at
2 years in infants born preterm, cortex 135 (2021) 17–29.
[8] R. Ceschin, J. L. Wisnowski, L. B. Paquette, M. D. Nelson, S. Blüml, A. Panigrahy, Devel-
opmental synergy between thalamic structure and interhemispheric connectivity in the
visual system of preterm infants, NeuroImage: Clinical 8 (2015) 462–472.
[9] G. Ball, J. P. Boardman, P. Aljabar, A. Pandit, T. Arichi, N. Merchant, D. Rueckert, A. D.

176
Edwards, S. J. Counsell, The influence of preterm birth on the developing thalamocortical
connectome, Cortex 49 (2013) 1711–1721.
[10] A. Jakab, G. Natalucci, B. Koller, R. Tuura, C. Rüegger, C. Hagmann, Mental development
is associated with cortical connectivity of the ventral and nonspecific thalamus of preterm
newborns, Brain and behavior 10 (2020) e01786.
[11] D. K. Thompson, W. Y. Loh, A. Connelly, J. L. Cheong, A. J. Spittle, J. Chen, C. E. Kelly, T. E.
Inder, L. W. Doyle, P. J. Anderson, Basal ganglia and thalamic tract connectivity in very
preterm and full-term children; associations with 7-year neurodevelopment, Pediatric
Research 87 (2020) 48–56.
[12] G. Ball, J. P. Boardman, D. Rueckert, P. Aljabar, T. Arichi, N. Merchant, I. S. Gousias, A. D.
Edwards, S. J. Counsell, The effect of preterm birth on thalamic and cortical development,
Cerebral cortex 22 (2012) 1016–1024.
[13] H. Kidokoro, J. J. Neil, T. E. Inder, New mr imaging assessment tool to define brain
abnormalities in very preterm infants at term, American Journal of Neuroradiology 34
(2013) 2208–2214.
[14] A. Makropoulos, E. C. Robinson, A. Schuh, R. Wright, S. Fitzgibbon, J. Bozek, S. J. Counsell,
J. Steinweg, K. Vecchiato, J. Passerat-Palmbach, et al., The developing human connec-
tome project: A minimal processing pipeline for neonatal cortical surface reconstruction,
Neuroimage 173 (2018) 88–112.
[15] B. Alexander, W. Y. Loh, L. G. Matthews, A. L. Murray, C. Adamson, R. Beare, J. Chen, C. E.
Kelly, P. J. Anderson, L. W. Doyle, et al., Desikan-killiany-tourville atlas compatible version
of m-crib neonatal parcellated whole brain atlas: the m-crib 2.0, Frontiers in Neuroscience
13 (2019) 34.
[16] A. Makropoulos, I. S. Gousias, C. Ledig, P. Aljabar, A. Serag, J. V. Hajnal, A. D. Edwards,
S. J. Counsell, D. Rueckert, Automatic whole brain mri segmentation of the developing
neonatal brain, IEEE transactions on medical imaging 33 (2014) 1818–1831.
[17] G. Li, L. Wang, P.-T. Yap, F. Wang, Z. Wu, Y. Meng, P. Dong, J. Kim, F. Shi, I. Rekik, et al.,
Computational neuroanatomy of baby brains: A review, NeuroImage 185 (2019) 906–925.
[18] I. Grigorescu, L. Vanes, A. Uus, D. Batalle, L. Cordero-Grande, C. Nosarti, A. D. Edwards,
J. V. Hajnal, M. Modat, M. Deprez, Harmonized segmentation of neonatal brain mri,
Frontiers in Neuroscience 15 (2021) 662005.
[19] I. Kostović, N. Jovanov-Milošević, The development of cerebral connections during the
first 20–45 weeks’ gestation, in: seminars in fetal and neonatal medicine, volume 11,
Elsevier, 2006, pp. 415–422.
[20] I. Benavente-Fernández, P. Lubián-López, M. Zuazo-Ojeda, G. Jiménez-Gómez, A. Lechuga-
Sancho, Safety of magnetic resonance imaging in preterm infants, Acta Paediatrica 99
(2010) 850–853.
[21] M. L. Gutiérrez, I. B. Fernández, A. Z. Ojeda, S. L. López, Alteraciones en resonancia
magnética asociadas a tratamiento con vigabatrina, Anales de Pediatría: Publicación
Oficial de la Asociación Española de Pediatría (AEP) 96 (2022) 165–166.
[22] I. Benavente-Fernández, A. García-Cazorla, Y. Jordán-García, A. Capdevila-Cirera, J. Camp-
istol, Diffusion-weighted imaging in pediatric central nervous system infections, Revista
de Neurologia 50 (2010) 133–138.
[23] A. Menegaux, C. Meng, J. G. Bäuml, M. T. Berndt, D. M. Hedderich, B. Schmitz-Koep,

177
S. Schneider, R. Nuttall, J. Zimmermann, M. Daamen, et al., Aberrant cortico-thalamic
structural connectivity in premature-born adults, Cortex 141 (2021) 347–362.
[24] J. L. Wisnowski, R. C. Ceschin, S. Y. Choi, V. J. Schmithorst, M. J. Painter, M. D. Nelson,
S. Blüml, A. Panigrahy, Reduced thalamic volume in preterm infants is associated with
abnormal white matter metabolism independent of injury, Neuroradiology 57 (2015)
515–525.
[25] Y. Lao, Y. Wang, J. Shi, R. Ceschin, M. D. Nelson, A. Panigrahy, N. Leporé, Thalamic
alterations in preterm neonates and their relation to ventral striatum disturbances revealed
by a combined shape and pose analysis, Brain Structure and Function 221 (2016) 487–506.
[26] W. Y. Loh, P. J. Anderson, J. L. Cheong, A. J. Spittle, J. Chen, K. J. Lee, C. Molesworth,
T. E. Inder, A. Connelly, L. W. Doyle, et al., Neonatal basal ganglia and thalamic volumes:
very preterm birth and 7-year neurodevelopmental outcomes, Pediatric research 82 (2017)
970–978.

178