figurec

# Volumetric and structural connectivity abnormalities co-localise in TLE

Jonathan J. Horsley1, Gabrielle M. Schroeder1,
Rhys H. Thomas2, Jane de Tisi3, Sjoerd B. Vos3,4,5, Gavin P. Winston3,6,
John S. Duncan3, Yujiang Wang1,2,3 and Peter N. Taylor∗1,2,3

###### Abstract

Patients with temporal lobe epilepsy (TLE) exhibit both volumetric and
structural connectivity abnormalities relative to healthy controls. How these
abnormalities inter-relate and their mechanisms are unclear. We computed grey
matter volumetric changes and white matter structural connectivity
abnormalities in 144 patients with unilateral TLE and 96 healthy controls.
Regional volumes were calculated using T1-weighted MRI, while structural
connectivity was derived using white matter fibre tractography from diffusion-
weighted MRI. For each regional volume and each connection strength, we
calculated the effect size between patient and control groups in a group-level
analysis. We then applied hierarchical regression to investigate the
relationship between volumetric and structural connectivity abnormalities in
individuals. Additionally, we quantified whether abnormalities co-localised
within individual patients by computing Dice similarity scores.

In TLE, white matter connectivity abnormalities were greater when joining two
grey matter regions with abnormal volumes. Similarly, grey matter volumetric
abnormalities were greater when joined by abnormal white matter connections.
The extent of volumetric and connectivity abnormalities related to epilepsy
duration, but co-localisation did not. Co-localisation was primarily driven by
neighbouring abnormalities in the ipsilateral hemisphere. Overall, volumetric
and structural connectivity abnormalities were related in TLE. Our results
suggest that shared mechanisms may underlie changes in both volume and
connectivity alterations in patients with TLE.

  1. 1.

CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex
BioSystems Group, School of Computing, Newcastle University, Newcastle upon
Tyne, United Kingdom

  2. 2.

Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United
Kingdom

  3. 3.

Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology,
University College London, London, United Kingdom

  4. 4.

Centre for Microscopy, Characterisation, and Analysis, The University of
Western Australia, Nedlands, Australia

  5. 5.

Centre for Medical Image Computing, Computer Science Department, University
College London, London, United Kingdom

  6. 6.

Division of Neurology, Department of Medicine, Queen’s University, Kingston,
Canada

*<EMAIL_ADDRESS>
##  1 Introduction

Epilepsy is a common neurological disorder affecting around 50 million people
worldwide [29]. There are many different types of epilepsy, but the most
common are focal epilepsies and in particular temporal lobe epilepsy (TLE). In
TLE, a variety of structural alterations have been found in grey and white
matter [74] [35]. Both grey and white matter alterations may be related to the
causes and consequences of the disorder, but it is currently unclear how these
changes are inter-related within individual patients. Improving our knowledge
of the relationship between volumetric measures of grey matter and structural
connectivity in white matter is important for understanding the
pathophysiology of TLE.

TLE has been considered a focal disorder characterised with grey matter
lesions and/or volume atrophy. The hippocampus in particular is a key
epileptogenic region [24], with atrophy observed in patients with hippocampal
sclerosis [71]. Numerous other studies have observed significant volume
reductions in the ipsilateral hippocampus compared to healthy controls [74]
[42] [26] [76]. However, atrophy is not restricted to the hippocampus and is
also seen in ipsilateral subcortical and temporal regions, including the
entorhinal cortex [13] [42], thalamus [74], temporal gyri, parahippocampal
gyrus, and temporal pole [51]. Moran et al. (2001) found a positive
correlation between the severity of hippocampal and extrahippocampal atrophy,
suggesting a common mechanism for abnormalities across different brain regions
[51]. Some studies have also identified more widespread atrophy, including in
the frontal lobe [23], and in contralateral temporal and subcortical regions
[5] [42]. Both cross-sectional [8] and longitudinal data [3] have related the
degree of atrophy to epilepsy duration. Additionally, volumetric abnormalities
can lateralise the side of seizure onset with a high degree of accuracy
[47][26]. Overall, these findings demonstrate the prevalence of volumetric
abnormalities in TLE, particularly in ipsilateral subcortical and temporal
regions, and their relation to clinical factors such as epilepsy duration.

Neuroimaging techniques such as diffusion MRI tractography allow non-invasive
modelling of the connections between different brain regions [6]. Fractional
anisotropy (FA) quantifies the degree to which diffusion of water molecules in
white matter axons is directional. FA is often used as a marker for axonal
integrity and structural connectivity in epilepsy [22]. Distinct regions of
the brain can be modelled as nodes and the connections between them as edges
[7]. This allows network analysis and the facilitates the study of epilepsy as
a network disorder [10] [65]. Studies of diffusion data have examined
structural connectivity in TLE. Compared to healthy controls, patients with
TLE exhibit reduced FA in both the ipsilateral anterior and mesial temporal
lobe [60], reduced FA in the ipsilateral parahippocampal cingulum and external
capsule [35], and reduced connectivity between the ipsilateral thalamus and
precentral gyrus [14]. Besson et al. (2014) found that patients with left TLE
had FA reductions that were strongly lateralised to the ipsilateral temporal
lobe, whilst FA reductions in patients with right TLE were less extreme and
restricted to bilateral limbic and ipsilateral temporal cortex [12]. However,
structural connectivity may also be significantly decreased beyond
epileptogenic regions [11]. These structural connectivity changes may relate
to duration [58] [35], and be predictive of surgical outcome [70] [64] [15] or
secondary generalisation of seizures [63].

Only few studies have considered both volumetric and structural connectivity
abnormalities. Keller et al. (2012) found associations between FA reductions
within regions and volume atrophy in a subset of subcortical regions,
including the contralateral hippocampus and bilateral thalami [41]. Atrophy of
the hippocampus in patients with hippocampal sclerosis has also been related
to whole-brain alterations in network properties including path length and
clustering [9]. Another study found that grey matter atrophy co-localised with
hub regions inferred from the connectome of healthy subjects, but did not
consider connectivity abnormalities [44]. To our knowledge, no study has
investigated the relationship between abnormalities in the two modalities by
considering regions and connections in a within-patient analysis.

It is currently unknown whether volumetric and connection abnormalities co-
localise within individual patients and whether they have shared mechanisms
that drive their co-localisation. Clearly, both volume and connectivity
provide useful, and potentially complementary, information and should be
considered simultaneously to to better understand the pathophysiology of TLE.
Importantly, we move beyond group-level analysis and perform hierarchical
statistical modelling to quantify abnormality co-occurrence within individual
patients. Within-patient analysis allows for individual patient models and
predictions, and allows for the study of disease progression in a cross-
sectional manner.

In this study, we analyse the relationship between volumetric and structural
connectivity abnormalities to address the following questions:

  1. 1.

At a group level, where do volumetric and connectivity abnormalities occur?

  2. 2.

Within individual patients, are connectivity abnormalities greater when
joining regions with abnormal volumes? Similarly, are volumetric abnormalities
greater when joined by abnormal connections?

  3. 3.

Within individual patients, do regions (network nodes) with atrophy also have
abnormal connections (co-localise) or not (Figure 1), and does co-localisation
relate to epilepsy duration?

Figure 1: Possible relationships between volumetric and connectivity
abnormalities in TLE. a) Volumetric and connectivity abnormalities are co-
localised if nodes with abnormal volumes also have abnormal connections. b)
Volume and connectivity abnormalities are not co-localised if nodes with
abnormal volumes have normal connections, and abnormal connections are
connected to normal volumes.

##  2 Methods

###  2.1 Patients and MRI acquisition

We retrospectively studied two cohorts of patients with unilateral TLE from
the National Hospital of Neurology and Neurosurgery, London, United Kingdom.
Pseudonymised data were analysed under the approval of the Newcastle
University Ethics Committee (2225/2017). Both cohorts had an accompanying
sample of healthy controls. All subjects underwent anatomical T1-weighted MRI
and diffusion-weighted MRI. In total, there were 144 patients and 96 healthy
controls.

The first cohort consisted of 84 patients and 29 healthy controls. For this
cohort, MRI studies were performed on a 3T GE Signa HDx scanner (General
Electric, Waukesha, Milwaukee, WI). Standard imaging gradients with a maximum
strength of $40mTm^{-1}$ and slew rate $150Tm^{-1}s^{-1}$ were used. All data
were acquired using a body coil for transmission, and 8-channel phased array
coil for reception. Standard clinical sequences were performed including a
coronal T1-weighted volumetric acquisition with 170 contiguous 1.1 mm-thick
slices (matrix, 256 × 256; in-plane resolution, 0.9375 × 0.9375 mm).

Diffusion-weighted MRI data were acquired using a cardiac-triggered single-
shot spin-echo planar imaging sequence [73] with echo time = 73$ms$. Sets of
60 contiguous 2.4 mm-thick axial slices were obtained covering the whole
brain, with diffusion sensitizing gradients applied in each of 52 noncollinear
directions (b value of 1,200 $s/mm^{2}$ [$\delta=21ms$, $\Delta=29ms$, using
full gradient strength of 40$mTm^{-1}$]) along with 6 non-diffusion-weighted
scans. The gradient directions were calculated and ordered as described
elsewhere [19]. The field of view was 24$cm$, and the acquisition matrix size
was 96 × 96, zero filled to 128 × 128 during reconstruction, giving a
reconstructed voxel size of 1.875 × 1.875 × 2.4 mm. The DTI acquisition time
for a total of 3480 image slices was approximately 25 min (depending on
subject heart rate).

The second cohort consisted of 60 patients and 67 healthy controls. For this
cohort, MRI studies were performed on a 3T GE MR750 scanner. Standard imaging
gradients with a maximum strength of 50$mTm^{-1}$ and slew rate
200$Tm^{-1}s^{-1}$ were used. All data were acquired using a body coil for
transmission, and 32-channel phased array coil for reception. Standard
clinical sequences were performed including a coronal T1-weighted volumetric
acquisition with 224 contiguous 1mm-thick slices (matrix, 256 × 256; in-plane
resolution, 1 × 1 mm).

Diffusion-weighted MRI data were acquired using a single-shot spin-echo planar
imaging sequence with echo time = 74.1$ms$. Sets of 70 contiguous 2 mm-thick
axial slices were obtained covering the whole brain. A total of 115 volumes
were acquired with 11, 8, 32, and 64 gradient directions at b-values of 0,
300, 700, and 2500 $s/mm^{2}$ respectively ($\delta=21.5ms$, $\Delta=35.9ms$)
as well as a single b = 0-image with reverse phase-encoding (B0). The field of
view was 25.6$cm$, and the acquisition matrix size was 128 × 128, giving a
reconstructed voxel size of 2 × 2 x 2 mm.

###  2.2 Image processing

####  2.2.1 T1 processing

T1-weighted MRI was used to generate parcellated grey matter regions of
interest. FreeSurfer’s recon-all pipeline was applied
(https://surfer.nmr.mgh.harvard.edu/), which performs intensity normalization,
skull stripping, subcortical volume generation, gray/white segmentation, and
parcellation [30]. Surfaces and volumes were corrected where appropriate
according to ENIGMA pipelines [35][74]. The default parcellation scheme from
FreeSurfer (the Desikan-Killiany atlas [21]) contains 82 cortical and
subcortical regions and is widely used in the literature [55][69].
Additionally, further denominations of the Desikan-Killiany atlas were used,
with 128, 233 and 462 regions to investigate consistency across parcellations.

####  2.2.2 DWI processing

Diffusion-weighted MRI data were first corrected for signal drift [72], then
eddy current and movement artefacts were corrected using the FSL eddy_correct
tool [4] (first cohort) or using EDDY/TOPUP (second cohort). The b vectors
were then rotated appropriately using the ‘fdt-rotate-bvecs’ tool as part of
FSL [38][46]. The diffusion data were reconstructed in MNI-152 space using
q-space diffeomorphic reconstruction (QSDR) [77] with a diffusion sampling
length ratio of 1.2. The HCP-1065 tractography atlas [78] was used to
determine connections between regions. The use of a tractography atlas is
expected to result in fewer false positive connections than fibre tracking
algorithms, since each tract has been visually confirmed to be expected and
not spurious. This approach has the benefit of reducing the influence of
network density on the subsequent group comparisons of networks [75], and has
been used previously [63] [52]. A connection between MNI-152 space regions of
the same parcellation was defined as present if streamlines passed into both
regions in the corresponding region pair.

###  2.3 Data processing

All data processing was performed using R version 4.02
(https://www.r-project.org), unless otherwise stated. The overall processing
and analysis pipeline is shown in Figure 2.

####  2.3.1 Volume

The volume of each region in each subject was computed using FreeSurfer. The
two scanning protocols were systematically different; therefore, ComBat was
applied to remove scanner differences while preserving biological variability
[31]. Accounting for known covariates (age, sex and ICV) using robust linear
regression applied to healthy controls, we calculated volume residuals of each
region in each subject. These volume residuals captured how much a regional
volume differed to the average healthy control given a subject’s age, sex and
ICV. We then transformed the residuals into $z$-scores based on the volume
distribution of healthy controls.

####  2.3.2 Connectivity

Connectivity matrices were computed for each subject using DSI Studio
(http://dsi-studio.labsolver.org), using the average FA of connections between
regions as a measure of connectivity strength. In a similar way to volume,
ComBat was applied to each connection to remove scanner differences. Known
covariates (age and sex) were then accounted for using robust linear
regression, which was applied to healthy controls to calculate connection
residuals. These connection residuals quantify the amount by which the average
FA in a connection differs compared to the average healthy control given a
subject’s age and sex. We then transformed the residuals into $z$-scores based
on the connection distribution of healthy controls.

Figure 2: Processing and analysis pipeline. We used T1-weighted MRI (a) with
the Desikan-Killiany parcellation scheme (c) to calculate regional volumes for
each subject (d). We also used diffusion-weighted MRI (b) with the same
parcellation scheme (c) to generate connectivity matrices using average FA
between regions (e). Both regional volumes (f) and connections (g) were
harmonized across scanning protocols using ComBat. Age and sex were accounted
for in both pipelines, with ICV also accounted for in the volume pipeline (h,
i). Within subjects, each regional volume (j) and connection strength (k) were
expressed as $z$-scores relative to a cohort of healthy controls. These
$z$-scores formed the input for our group-level analysis (l), hierarchical
models (m) and co-localisation analysis (n).

###  2.4 Statistical analysis

We flipped the z-scores of right and left hemispheres in patients with RTLE to
perform an ipsilateral-contralateral analyses. This flipping was only done
after each region and connection was z-scored relative to controls. This
approach ensured that region and connection abnormalities were only calculated
by comparison against the same hemisphere in controls. This comparison is
important since left and right hemispheres are not simply mirror images of one
another. Our approach preserved our large sample size and followed previous
literature [70] [64] [42].

####  2.4.1 Group-level analysis

Using the volume $z$-scores of each region (82 in total), we computed an
effect size between patients and healthy controls using Cohen’s $d$, defined
as:

Cohen’s $d$ $\displaystyle=\frac{\overline{x_{P}}-\overline{x_{C}}}{s},$

where $\overline{x_{P}}$ is the mean volume $z$-score of a region in patients,
$\overline{x_{C}}$ is the mean volume $z$-score of a region in controls and
$s$ is the pooled standard deviation.

Similarly, using the connection $z$-scores between each pair of regions (1289
in total), we computed an effect size between patients and healthy controls
using Cohen’s d.

####  2.4.2 Hierarchical modelling

Methods in the previous section examined volumetric and connectivity
abnormalities at a group level, but did not provide information at an
individual patient level. However, group-level analysis has the potential to
be misleading. As an example, consider a cohort with 50% of patients with
volumetric, but not connectivity, abnormalities in ipsilateral temporal
regions (Supplementary Analysis 1 - Figure S1a). If the other 50% of patients
had connectivity, but not volumetric, abnormalities in ipsilateral temporal
regions (Figure S1b) then, at a group level, the abnormalities would appear to
co-localise. However, in this example scenario, no single patient would have
both volumetric and connectivity abnormalities together in ipsilateral
temporal regions. A traditional group analysis is therefore insensitive to
within-patient features. In contrast, hierarchical modelling uses within-
patient information to investigate the relationship between volumetric and
connectivity abnormalities; one of the key novelties of our study.

In our cohort of 144 patients, volumetric and connection abnormalities were
potentially inter-related on a subject-specific level. To account for this
nested nature of the data, we fitted a two-level hierarchical model to our
data, allowing random intercepts and slopes between individual subjects.
Hierarchical modelling is appropriate for nested data organized at more than
one level (e.g. many connections within individual patients) and accounts for
a potential lack of independence between connection $z$-scores within the same
patient [57]. For example, the probability of reduced FA in a connection is
likely related to the quantity and location of other FA reductions in that
patient. The hierarchical modelling approach also allows for individual
heterogeneity to inform the overall model estimates.

Abnormal regions were defined as those with volumes below the 2.5th percentile
threshold ($z<-1.96$). This threshold value was scanned from $-1.0$ to $-2.5$
in steps of $0.1$ to determine the robustness of results. Specifically, we
modelled whether connection abnormalities ($z$-scores) were larger when they
connected one or two volumetric abnormalities, compared to the case when they
connected two normal ROIs. The hierarchical model was fitted using restricted
maximum likelihood (REML) from the the ‘lmer’ function in R’s ‘lme4’ package.

Mathematically, we modelled the connection abnormality $C_{ijk}$ for the
connection between regions $i$ and $j$ in subject $k$ with the following
system of equations:

Level One:

$\displaystyle C_{ijk}$
$\displaystyle=a_{k}+b_{k}V_{ijk}^{\prime}+c_{k}V_{ijk}^{\prime\prime}+\epsilon_{ijk}$

Level Two:

$\displaystyle a_{k}$ $\displaystyle=\alpha_{0}+u_{k}$ $\displaystyle b_{k}$
$\displaystyle=\beta_{0}+v_{k}$ $\displaystyle c_{k}$
$\displaystyle=\gamma_{0}+w_{k},$

Coefficient |  Interpretation
---|---
$C_{ijk}$ |  the connection $z$-score for the connection between regions $i$ and $j$ in subject $k$
$a_{k}$ |  mean connection $z$-score in subject $k$ for connections between two normal regions
$b_{k}$ |  mean change in connection $z$-score in subject $k$ for connections between one normal and one abnormal region, as compared to two normal regions
$c_{k}$ |  mean change in connection $z$-score in subject $k$ for connections between two abnormal regions, as compared to two normal regions
$\epsilon_{ijk}$ |  difference between fitted and observed abnormalities for the connection between regions $i$ and $j$ in subject $k$
$V_{ijk}^{\prime}$ |  $\begin{cases}1,&\text{if connection is between one normal and one abnormal region}\\\ 0,&\text{otherwise}\end{cases}$
$V_{ijk}^{\prime\prime}$ |  $\begin{cases}1,&\text{if connection is between two abnormal regions}\\\ 0,&\text{otherwise}\end{cases}$
$\alpha_{0}$ |  true mean connection $z$-score for connections between two normal regions across all subjects
$u_{k}$ |  difference between $\alpha_{0}$ and mean connection $z$-score for connections between two normal regions in subject $k$
$\beta_{0}$ |  true mean difference in connection $z$-score for connections between one normal and one abnormal region, as compared to two normal regions across all patients
$v_{k}$ |  difference between $\beta_{0}$ and mean difference in connection $z$-score for connections between one normal and one abnormal region, as compared to two normal regions in subject $k$
$\gamma_{0}$ |  true mean difference in connection $z$-score for connections between two abnormal regions, as compared to two normal regions across all patients
$w_{k}$ |  difference between $\gamma_{0}$ and mean difference in connection $z$-score for connections between two abnormal regions, as compared to two normal regions in subject $k$
Table 1: Hierarchical model coefficient interpretations.

The interpretation of the model coefficients are given in Table 1.
$V_{ijk}^{\prime}$ and $V_{ijk}^{\prime\prime}$ capture whether a connection
in subject $k$ is between one or two abnormal regions, respectively. In this
model, there are three key fixed effects to estimate: $\alpha_{0}$,
$\beta_{0}$ and $\gamma_{0}$, which allow us to test our hypotheses. The error
terms $\epsilon_{ijk}$, $u_{k}$, $v_{k}$ and $w_{k}$ represent random effects.
These error terms describe specific patients, which are a sample of a larger
population (e.g. our cohort as a sample of all patients with temporal lobe
epilepsy). We account for the influence of these random effects in our model,
but do not draw conclusions about specific levels.

For comparison, and as a null model, we also fit our hierarchical model to two
other cases. First, we shuffled the connection $z$-scores and re-fit the same
model. Second, we fit the model to our cohort of healthy controls.

We had two main hierarchical models. First, we modelled whether volumetric
abnormalities coincided with adjacent connectivity abnormalities. Second, we
investigated if connection abnormalities coincided with adjacent volumetric
abnormalities. Abnormal connections were defined as those with FA reductions
below the 2.5th percentile threshold ($z<-1.96$). Again, this threshold value
was scanned (-1.0 to -2.5 in steps of 0.1) to determine the robustness of
results (see Supplementary Analysis 4). Specifically, we modelled whether the
mean volume abnormality of connected regions was larger when the regions were
connected by abnormal connections. This is a near-identical approach as to the
first hierarchical model, and is fully detailed in Supplementary Analysis 3
for completeness.

####  2.4.3 Co-localisation analysis

Finally, we investigated if abnormalities co-localised (occurred in connected
regions) more than would be expected by chance.

We first defined a measure of co-localisation. Taking each connection
$z$-score, along with the volume $z$-scores of the two connected regions, we
applied a threshold in a similar way as to the previous section. Volumes and
connections with $z$-scores below the 5th percentile ($z<-1.645$) were deemed
abnormal. Z ¡ -1.645 defines the lower 5% of a standard normal distribution,
we see no deviation from normality in our population of controls. This
slightly less stringent threshold was used to ensure that a greater proportion
of patients had abnormalities in both modalities. This threshold value was
scanned to ensure robust results, and these results are shown in Supplementary
Analysis 5.

Region $i$ | Region $j$ | $V_{ik}^{\prime}$ | $C_{ijk}^{\prime}$ | $V_{jk}^{\prime}$
---|---|---|---|---
Ipsilateral Thalamus | Ipsilateral Hippocampus | 0 | 0 | 1
Ipsilateral Thalamus | Ipsilateral Amygdala | 0 | 1 | 0
Ipsilateral Thalamus | Ipsilateral Temporal Pole | 0 | 1 | 1
… | … | … | … | …
Contralateral Insula | Contralateral Transverse Temporal Gyrus | 0 | 0 | 0
Table 2: Volumetric and connectivity abnormalities in example subject k. In
columns $V_{ik}^{\prime}$, $C_{ijk}^{\prime}$ and $V_{jk}^{\prime}$, 0 denotes
normality and 1 denotes abnormality. In this subject, both the ipsilateral
hippocampus and ipsilateral temporal pole have abnormal volumes. The
connections ipsilateral thalamus - ipsilateral amygdala and ipsilateral
thalamus - ipsilateral temporal pole are abnormal. Using the Desikan-Killiany
atlas with the HPC1065 tractography atlas, there were 1289 unique connections
between regions.

To gauge the similarity of the spatial locations of our abnormalities within a
patient, we computed the Dice similarity of the volumetric abnormalities and
joining connection abnormalities. For each unique connection, we first
nominally assigned an originating and destination region. Then
$\boldsymbol{V}_{ik}^{\prime}$ represented the set of originating volumetric
abnormalities within subject k (column 3 in Table 2),
$\boldsymbol{C}_{ijk}^{\prime}$ represented the set of connectivity
abnormalities within subject k (column 4 in Table 2), and
$\boldsymbol{V}_{jk}^{\prime}$ represented the set of destination volumetric
abnormalities within subject k (column 5 in Table 2). Then:

$\displaystyle
DS_{ik}=\frac{|\boldsymbol{V}_{ik}^{\prime}\cap\boldsymbol{C}_{ijk}^{\prime}|}{|\boldsymbol{V}_{ik}^{\prime}|+|\boldsymbol{C}_{ijk}^{\prime}|}$
$\displaystyle
DS_{jk}=\frac{|\boldsymbol{C}_{ijk}^{\prime}\cap\boldsymbol{V}_{jk}^{\prime}|}{|\boldsymbol{C}_{ijk}^{\prime}|+|\boldsymbol{V}_{jk}^{\prime}|}$

$\displaystyle DS_{k}$ $\displaystyle=\frac{DS_{ik}+DS_{jk}}{2}$

In summary, the Dice similarity quantified the extent to which abnormalities
occurred simultaneously in white matter connections and the connected grey
matter regions, producing a single value, $DS_{k}$, per patient. Because
$DS_{k}$ was biased by the number of abnormalities of each set, a greater
number of abnormalities generally produced a greater dice similarity score. As
a result, we randomly permuted the connection abnormalities and recomputed
Dice similarity 5,000 times and defined our unbiased co-localisation measure
as:

Co-localisation
$\displaystyle=\frac{1}{5000}\sum_{n=1}^{5000}\begin{cases}1,&DS_{\text{actual}}>DS_{\text{n}}\\\
0,&DS_{\text{actual}}\leq DS_{\text{n}}\end{cases}.$

Co-localisation was a number between 0 and 1 for each patient, unbiased by the
number of abnormalities, where 1 signifies that abnormalities occurred in
adjacent regions/connections above chance and 0 signifies that abnormalities
occurred in adjacent regions/connections below chance.

It is plausible that the relationship between volumetric and connectivity
abnormalities is related to disease progression. Over time, atrophy may spread
from region to region via abnormal connections. To investigate this, we used
Pearson correlation to relate epilepsy duration with our co-localisation
measure, in addition to the proportion of abnormal regional volumes and
proportion of abnormal connections.

Finally, in those patients with abnormalities co-localising across the full
brain (co-localisation $>$ 0.95), we re-computed co-localisation separately
for ipsilateral-only and contralateral-only connections. We performed a paired
Mann-Whitney U test to determine whether abnormalities were more likely to co-
localise in the ipsilateral hemisphere compared to the contralateral
hemisphere.

##  3 Results

The results are presented in four sections. First, we highlight abnormalities
in volume and connectivity in a group-level analysis. Second, we show how
volumetric abnormalities relate to adjacent connectivity abnormalities within
individuals using hierarchical modelling. Third, we show how connectivity
abnormalities relate to adjacent volumetric abnormalities within individuals
using hierarchical modelling. Finally, we investigate whether adjacent
volumetric and connectivity abnormalities within individual patients relate to
epilepsy duration, surgical outcome and secondary generalization of seizures.

###  3.1 Volumetric and connectivity abnormalities coexist in patients

For the volume of each region, we computed an effect size between patients and
healthy controls (Figure 3a). We observed reduced volumes in several regions,
which were primarily in ipsilateral temporal and subcortical areas, most
notably the ipsilateral hippocampus (Cohen’s $d$ = -1.02) and ipsilateral
thalamus ($d$ = -0.65).

Similarly, for the FA of each connection, we computed an effect size between
patients and healthy controls (Figure 3b). We observed widespread structural
connectivity reductions in patients relative to controls. Connections between
temporal and subcortical regions bilaterally, and ipsilateral frontal regions,
especially those involving the ipsilateral temporal pole were most affected.

At a group level, regions with the largest volumetric atrophy and structural
connectivity reductions (in white matter) were exclusively located in
ipsilateral temporal and subcortical areas. These regions were the thalamus,
hippocampus, superior temporal gyrus, middle temporal gyrus and temporal pole
(Figure 3c). A full list of regions and their associated lobe is given in
Supplementary Analysis 7.

Figure 3: Volumetric and connectivity abnormalities coexist in patients. At a
group level, a) the distribution and location of regional volumetric
abnormalities in patients compared to controls. Negative effect sizes indicate
a reduction in patients. b) The distribution and location of structural
connectivity abnormalities in patients compared to controls. The colour of the
node denotes the lobe. c) Regions identified as having both reduced volume and
connectivity were located exclusively in ipsilateral subcortical and temporal
areas. d) For one specific connection (ipsilateral thalamus - ipsilateral
hippocampus), both the effect size of the connection abnormality and the
effect size of the mean volumetric abnormality is shown. e) More generally,
the relationship between the two modalities are shown for within-lobe
connections. Specific connections are represented by small, faint points, and
the lobe means are in large, bold points.

Next, we related within-lobe volumetric abnormalities to connectivity
abnormalities at a group level. We hypothesised that larger connection
abnormalities would coincide with larger neighbouring volumetric
abnormalities. For each connection within each subject, we calculated the mean
of the volume $z$-scores of the two connected regions, and calculated effect
sizes for the connection $z$-score and the mean volume $z$-score between
patients and healthy controls. While there was limited evidence for our
hypothesis at the connection-level, lobes with larger connection abnormalities
displayed larger volumetric abnormalities (Figure 3e). Ipsilateral temporal
and subcortical lobes were more abnormal than other areas (see inset black
arrow in Figure 3e).

###  3.2 Connection abnormalities are greater when joining regions with
abnormal volumes

The previous analysis in Figure 3 did not fully account for individual
subject-level co-localisations. This is addressed using a hierarchical
approach in Figure 4. Since we used a random intercepts and slopes model, the
relationship between abnormalities are modelled individually within each
subject, so that the mean connection abnormality between none, one or two
abnormal regions is estimated, accounting for random effects. The individual
estimates are then aggregated across all subjects to produce overall model
estimates of the mean connectivity abnormality in each case. We hypothesised
that greater connection abnormalities would coincide with neighbouring
volumetric abnormalities.

In patients with TLE, connections between one normal and one atrophied region
(estimate = -0.446 $\pm$ 0.063; p=0.003), and connections between two
atrophied regions (-0.492 $\pm$ 0.075; p=0.018) had significantly reduced FA
compared to connections between two normal regions (Figure 4c). Connections
between two normal regions still exhibited reduced FA in comparison to healthy
controls (-0.378 $\pm$ 0.059). These results were robust to different
$z$-score thresholds (see Supplementary Analysis 4).

Figure 4: Connection abnormalities are greater when joining regions with
abnormal volumes. a) Each connection within a patient can be connected to
zero, one or two abnormal regions. We hypothesised that connections would have
greater reductions in FA if they were connected to abnormal regions. b) The
individual connection and volumetric abnormalities within each subject formed
the input for the hierarchical model. c) Connections had significantly reduced
FA when they were connected to one or two abnormal regions. The error bars
indicate the standard error of the model estimate. Null models were fitted for
comparison using d) patients with the connections shuffled and e) healthy
controls.

We fitted two null models for comparison. Firstly, we randomly permuted the
connection abnormalities within patients (Figure 4d). As expected, the mean
connection abnormality did not significantly differ depending on whether the
connections were joining two normal regions (-0.387 $\pm$ 0.0003), one normal
and one abnormal region (-0.382 $\pm$ 0.0008; p=0.53), or two abnormal regions
(-0.388 $\pm$ 0.0242; p=0.94). Secondly, we applied the same hierarchical
modelling approach to healthy controls (Figure 4e). Again, the mean connection
abnormality did not significantly differ depending on whether the connections
were joining two normal regions (0.001 $\pm$ 0.066), one normal and one
abnormal region (-0.043 $\pm$ 0.076; p=0.26), or two abnormal regions (-0.089
$\pm$ 0.117; p=0.37).

###  3.3 Volumetric abnormalities are greater when joined by abnormal
connections

In complement to the previous section, we hypothesised that regions connected
by abnormal connections would be more atrophied than regions connected by
normal connections. Specifically, we modelled the mean volume $z$-score of two
connected regions, using the thresholded connection abnormality as a binary
predictor.

In TLE patients, the volume of regions connected by abnormal connections were
significantly reduced, as compared to regions connected by normal connections
(estimate = -0.266 $\pm$ 0.052; p=0.021). This is shown in Figure 5c. The
volumes of regions connected by normal connections still exhibited atrophy in
comparison to healthy controls (-0.206 $\pm$ 0.045). These results were robust
to different threshold values (see Supplementary Analysis 4).

Figure 5: Volumetric abnormalities are greater when joined by abnormal
connections. a) The connection joining two regions can be either normal or
abnormal. We hypothesised that the mean volume of two connected regions would
have a greater reduction if the regions were joined by an abnormal connection.
b) The individual connection and volumetric abnormalities within each subject
formed the input for the hierarchical model. c) Regions had significantly
reduced volume when they were connected by an abnormal connection. The error
bars indicate the standard error of the model estimate. Null models were
fitted for comparison using d) patients with the connections shuffled and e)
healthy controls.

Again, we fitted two null models for comparison. Firstly, we randomly permuted
the connection abnormalities within patients (Figure 5d). As expected, the
mean volume abnormality did not significantly differ depending on whether
regions were connected by a normal (-0.219 $\pm$ 0.046) or abnormal connection
(-0.214 $\pm$ 0.056; p=0.51). Secondly, we applied the same hierarchical
modelling approach to healthy controls (Figure 5e). Again, the mean volume
abnormality did not significantly differ depending on whether regions were
connected by a normal (0.002 $\pm$ 0.045) or abnormal connection (-0.065 $\pm$
0.058; p=0.07).

###  3.4 Volume and connectivity abnormality co-localisation differs across
patients

Next, for each patient, we determined the extent to which abnormalities in the
two modalities co-localised. Specifically, we investigated to what extent were
atrophied regions found to be connected by abnormal connections. We used Dice
similarity as a measure of overlap, then randomly permuted each patients’
connection abnormalities 5000 times to generate a null distribution of Dice
similarities, given each patients’ number of abnormalities. Our measure of co-
localisation for each patient was the Dice similarity percentile as compared
to the null distribution, and this value could be between zero (not co-
localised) and one (co-localised).

Abnormalities co-localised in 30% of patients, did not co-localise in 40% of
patients, and co-localised to some degree in the remaining patients (Figure
6a). Of those patients whose abnormalities co-localised (defined as co-
localisation $\geq$ 0.95), co-localisation was significantly higher in the
ipsilateral hemisphere than the contralateral hemisphere (p=0.01) (Figure 6b).
Three example patients are presented in Figure 6c with Co-localisation = 0,
Co-localisation = 0.63 and Co-localisation = 1.

Figure 6: Abnormalities co-localised in some patients, but not others. a) Abnormalities co-localised in some patients, but not others. b) Of the patients whose abnormalities co-localised (co-localisation $\geq 0.95$), this was significantly more likely to be driven by co-localised abnormalities in the ipsilateral hemisphere. c) Three example patients are given for whose abnormalities were not co-localised, partially co-localised, and co-localised. d) Both the proportion of abnormal regional volumes and abnormal connections increased as epilepsy duration increased. e) However, the co-localisation of abnormalities did not relate to epilepsy duration. | Co-localisation | Number of Vol | Number of Conn
---|---|---|---
Surgical Outcome | 0.21 (0.20) | -0.06 (0.81) | 0.01 (0.72)
Epilepsy Duration | 0.04 (0.59) | 0.17 (0.05) | 0.23 (0.005)
Secondary Generalisation | 0.19 (0.56) | 0.04 (0.87) | -0.23 (0.24)
Table 3: Co-localisation differences between groups showing effect size
(p-value). Cohen’s d and Mann-Whitney U test were used for surgical outcome
and secondary generalisation. Pearson correlation coefficient was used for
epilepsy duration. Surgical outcome was defined as ILAE 1 vs ILAE 2+.

Both the proportion of regional volumes and the proportion of connections that
were abnormal increased as epilepsy duration increased (Pearson’s r=0.18,
p=0.028; r=0.23, p=0.005 respectively) (Figure 6d). However, these
abnormalities did not necessarily occur in the same regions (i.e. co-localise)
and Figure 6e shows that co-localisation did not increase as epilepsy duration
increased (r=0.04, p=0.59). Neither the number of volumetric abnormalities nor
the number of connectivity abnormalities within a patient was related to
surgical outcome or secondary generalisation of seizures (Table 3).

##  4 Discussion

We investigated the relationship between volumetric and structural
connectivity abnormalities in patients with TLE. We first analysed these
abnormalities at a group-level, which assessed the location of these
abnormalities separately and jointly. Next, we used hierarchical modelling to
uncover the relationship between volumetric and structural connectivity
abnormalities within individual patients. Finally, we determined if
abnormalities co-localised in patients more than would be expected by chance.
We found that both volumetric and structural connectivity abnormalities were
greater when they coincided with abnormalities in the other modality, and
these abnormalities were primarily in ipsilateral subcortical and temporal
regions. Volumetric and structural connectivity abnormalities separately were
more widespread as epilepsy duration increased, but their co-localisation
(i.e. the extent to which they overlap beyond chance, given their prevalence)
did not change. These findings suggest that common, or related, mechanisms may
underlie changes in both volume and connectivity in patients with TLE.

At a group level, our volumetric results were broadly consistent with the
literature. Atrophy was unsurprisingly largest in the ipsilateral hippocampus,
since 52% of our patients had hippocampal sclerosis and hippocampal atrophy
has been reported frequently in TLE [74] [26] [42]. We also found reduced
volume in the ipsilateral thalamus, as shown in a recent ENIGMA study [74], as
well as in the ipsilateral temporal lobe, specifically the temporal pole,
middle and superior temporal gyri in agreement with Moran et al (2001) [51].
Similarly, our connectivity results concur with previous findings. Connections
with the largest FA reductions were connected to bilateral temporal, bilateral
subcortical, and ipsilateral frontal regions. The location of these FA
reductions strongly aligns with a previous study, which found that connections
were most disrupted in temporal and subcortical regions in TLE, albeit to
varying degrees [12]. The largest study of white matter abnormalities
investigated specific white matter regions and found the largest FA reductions
in ipsilateral external capsule and parahippocampal cingulum [35]. We instead
looked at connections between grey matter regions, and found regions with
multiple FA reductions in similar areas of the brain, specifically putamen,
pallidum and temporal gyri bilaterally, as well as ipsilateral temporal pole
and amygdala. Bilateral reductions in FA of white matter tracts have
previously been reported [18].

Neurobiological explanations for reduced volumes and connectivity have been
postulated from experimental and other data. For example, during seizures,
excessive glutamatergic neurotransmission can cause grey matter cell bodies to
be flooded with calcium leading to excitotoxicity, osmolytic stress and
eventually cell death [36]. In addition to excitotoxicity, other mechanisms
for grey matter cell death exist such as reduced post-ictal localised blood
flow (ischaemia/hypoxia) [28] [27] [32], or protein aggregation. The unfolding
of these proteins causes protein deposition, triggering degenerative signals
in neurons [32]. In particular, hyperphosphorylation of tau into
neurofibrillary tangles has been implicated in TLE [67] [34] [80] [2]. The
specific mechanism(s) for atrophy in any given region/patient is unclear, and
difficult to determine from MRI (although see e.g. [33] [43] for recent
attempts). However, our imaging findings do strongly suggest that the observed
atrophy co-localises with white matter connectivity alterations in at least
some individuals. These co-localised connectivity alterations may reflect
axonal degeneration following upstream cell body loss. Whilst diffusion MRI-
based tractography cannot be used infer the direction of connections, our
results demonstrate a greater degree of atrophy in regions joined by
connections with reduced FA. These results may reflect the loss of downstream
grey matter neurons, although longitudinal studies are needed to confirm
causality.

Network properties may also influence the relationship between volumetric and
structural connectivity abnormalities. Hub nodes are regions with connections
to many other regions and are of significance in TLE, since they may spread
seizures or pathology far around the network [66, 1]. At a group-level, grey
matter atrophy co-localises with hub regions [44] [20]. In patients with
generalized tonic-clonic seizures, hub regions have connectivity reductions
[48], specifically in connections to other hub regions. The thalamus is known
to be a hub region in the brain [37], and is particularly important in TLE
[16]. At a group-level, we found both reduced volume in the ipsilateral
thalamus, and connectivity reductions between ipsilateral thalamus and
temporal lobe. If hubs are particularly susceptible to both grey matter
atrophy and structural connectivity reductions, then the chance of observing
both abnormalities simultaneously is likely to be increased. Our results were
driven by co-localised abnormalities in ipsilateral temporal and subcortical
regions, suggesting that TLE has the greatest effect on grey matter and axons
in epileptogenic regions, and weaker effects elsewhere.

Alongside the observed relationship between volumetric and structural
connectivity abnormalities, our model estimated that connections between
normal regions still have reduced FA on average, compared to healthy controls
(Figure 4c, solid black bar). Similarly, the volume of regions connected by
normal connections are still significantly reduced on average compared to
healthy controls (Figure 5c, solid black bar). These reductions suggest that
there is some, but not complete spatial overlap between volumetric and
structural connectivity abnormalities within patients. Therefore, some
complementary information exists between the two modalities. It could be the
case that connectivity abnormalities are more extensive than volumetric
abnormalities in some patients, or vice versa, and this will be investigated
in future work.

Our approach may have clinically relevant implications for two key reasons.
Firstly, the incorporation of multiple modalities in computational methods has
significant potential to improve localisation of epileptogenic zone [40] [59].
The epileptogenic zone is the region indispensable for generating seizures
[61]. In addition to the modalities presented here, we know that multiple
modalities separately offer some ability to localise cortical zones that may
be related to the epileptogenic zone, including fMRI [53], scalp EEG [62] [50]
[49], iEEG [68] and MEG [56] [25]. Quantitatively combining modalities could
therefore greatly improve our localisation and understanding of the
epileptogenic zone, and as a result, improve quality of life for patients with
drug-resistant TLE. Secondly, it is important that this localisation of the
epileptogenic zone can be done on individual patients. Abnormalities which co-
localise spatially across multiple modalities may help to localise the
epileptogenic zone [17]. Multiple abnormalities across modalities can be
considered simultaneously and quantitatively using methods such as Mahalanobis
distance [58] [54] or using multilayer or multiplex networks [79]. Co-
localised abnormalities in multiple modalities outside of the surgically
resected regions may indicate a failure to remove critical parts of
epileptogenic network, leading to poor post-surgical outcomes.

A strength of our study is the sample size of 144 patients and 96 healthy
controls. Outside multi-centre studies such as the ENIGMA-Epilepsy working
group, our sample is a large size for a single centre study. The use of two
cohorts of subjects, using different scanning parameters (albeit at the same
site) suggests our results are somewhat generalisable. This generalisability
could be further improved by the inclusion of additional sites. To our
knowledge, our use of a hierarchical modelling approach to examine the
relationship between abnormalities across modalities is novel in epilepsy
research.

One potential limitation of our approach is our heterogeneous cohort of
patients at various stages and with potentially different subtypes of temporal
lobe epilepsy. The relationship between volume and connectivity abnormalities
may differ depending on a patient’s stage or subtype, and individual patients
may have distinct patterns of gray and white matter structural pathology [45].
For example, it has been suggested that patients with hippocampal sclerosis
may have more widespread connectivity abnormalities than those with non-
lesional TLE [35] [9], and this may also effect the relationship with
volumetric abnormalities. Additionally, in our group-level analysis, we
flipped the right and left hemispheres in RTLE patients to perform an
ipsilateral-contralateral analysis. This may be important, since it has been
acknowledged that right and left hemispheres are not identical [12]. However,
this was done only after z-scoring against the same hemisphere in controls and
there is precedence for this flipping in the literature [14] [55] [39] [70].
Additionally, our hierarchical modelling approach and co-localisation measure
are independent of hemisphere (although hemispheres were eventually compared
for co-localisation).

Distinct neuroimaging modalities offer different perspectives on neurological
disorders such as TLE. Considering only one modality may omit useful
information for diagnosing and treating these conditions. There is a need to
understand how these different modalities highlight different aspects of
neurological disorders, separately and jointly. Several other modalities from
epilepsy monitoring are available, including time series data (e.g. from EEG,
MEG, fMRI). Currently clinicians use these different modalities for diagnosis
and localisation of epileptogenic tissue. Developing quantitative methods to
improve treatment is vital for improving quality of life for patients.

##  5 Acknowledgements

The authors acknowledge the facilities and scientific and technical assistance
of the National Imaging Facility, a National Collaborative Research
Infrastructure Strategy (NCRIS) capability, at the Centre for Microscopy,
Characterisation, and Analysis, the University of Western Australia. G.P.W.
was supported by the MRC (G0802012, MR/M00841X/1). P.N.T. is supported by a
UKRI Future Leaders Fellowship (MR/T04294X/1). J.J.H. is supported by the
Centre for Doctoral Training in Cloud Computing for Big Data (EP/L015358/1).

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