White matter microstructure and serum biomarkers of inflammation in functional seizures

Автор: Mueller Ch. Szaflarski J.P.

Теги: chemistry biology microbiology science molecular chemistry white matter microstructure

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Текст

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White matter microstructure and serum biomarkers of inflammation in functional seizures

Christina Mueller, PhD & Jerzy P. Szaflarski, MD, PhD

Address for correspondence:

CIRC 312, 1719 6th Ave S, Birmingham, AL 35233

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P: 205-975-4219 | Fax: 205-996-4802 | e-mail: cm1@uab.edu or jszaflarski@uabmc.edu

Author affiliation:

University of Alabama at Birmingham (UAB) Heersink School of Medicine, Department of

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Neurology and the UAB Epilepsy Center, Birmingham, AL, USA

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Abstract

Background: Neuroinflammation may contribute to the pathophysiology of functional seizures

(FS). However, it is unclear whether and to what degree comorbid psychiatric symptoms explain
this association. In this study, we investigated the neuroinflammatory signature specific to FS

and how it compared to that of psychiatric controls (PCs).

Methods: We prospectively assessed differences in neurite density (NDI), orientation dispersion

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(ODI), and isotropic diffusion (F-ISO) between 23 participants with FS and 27 PCs, and their
relationships to serum levels of tumor necrosis factor (TNF)-, TNF receptor 1 (TNF-R1), TNFrelated apoptosis-inducing ligand (TRAIL), interleukin (IL)-6, intercellular adhesion molecule

(ICAM)-1, and monocyte chemoattractant protein (MCP)-1 using voxelwise multiple linear
regressions. Pearson correlations between serum biomarkers and clinical symptoms were also
obtained.

Results: There were no WM microstructural differences between groups. In FS, TNF-R1 was
negatively associated with NDI in the right UF and positively associated with F-ISO in the left

uncinate fasciculus (UF). IL-6 was positively associated with NDI and negatively with F-ISO in
the left UF. ICAM-1 was positively associated with ODI in the left UF. TNF- was negatively

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associated with ODI in the left cingulum bundle. The opposite relationships were observed in
PCs. Higher TNF-R1 was associated with higher depression, anxiety, lower emotional quality of

life, and higher levels of disability in FS.
Conclusions: For the first time, we report relationships between peripheral inflammatory

biomarkers and WM integrity in FS including abnormalities in the UF and cingulum bundle. Our
results suggest that serum biomarkers of inflammation may, with additional studies, become a

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useful aid to FS diagnosis, especially in settings where video-EEG is not available. The lack of

group differences in WM microstructure suggests that previously identified WM abnormalities in
FS versus healthy controls may be related to psychological comorbidities of FS.

Key words: functional seizures, neuroinflammation, neurite orientation dispersion and density

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imaging, white matter, psychiatric comorbidities

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Introduction

Functional seizures (FS) are a subtype of functional neurological symptom disorder

(FNSD) involving seizure-like episodes without the presence of epileptiform discharges. Similar
to other neuropsychiatric conditions such as depression, anxiety, and posttraumatic stress

disorder (PTSD), the pathophysiology of FS may involve abnormal immune system functioning
and neuroinflammation (NI). 1 In the recently proposed two-hit hypothesis of FS, we suggested

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early life stress (physical or psychological), structural abnormalities, and brain inflammation as
potential factors in FS development and maintanance. 1 Within this model, trauma induces
neuroendocrine (e.g., hypothalamic-pituitary axis activation (HPAA), cortisol release) and

neuroinflammatory (e.g., immune cell activation and pro-inflammatory cytokine release) changes
that prime the brain toward NI in response to subsequent traumatic events.
Gledhill et al. 2 recently assessed inflammatory markers in FS, epilepsy, and healthy

controls (HCs) to report elevated tumor necrosis factor-related apoptosis-inducing ligand
(TRAIL) and intercellular adhesion molecule (ICAM)-1 following FS compared to epileptic

seizures, lending support to the two-hit hypothesis of FS. In further support of the chronic HPAA
model, a recent meta-analysis in FS found elevated baseline heart rate, peri-ictal cortisol, and

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adrenocorticotropic hormone, and decreased brain-derived neurotropic factor (BDNF). 3 In
response to systemic inflammation, the liver secretes C-reactive protein, 4 which is used

clinically as a marker of inflammation. One study reported C-reactive protein elevations in
adolescents with FS compared to HCs, with levels being similar to other FNSD types (motor,

sensory, and mixed). 5 Brain micro-pathology is common in systemic inflammatory disorders, 6-9

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inflammation and white matter (WM) structure in FS. 10

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but studies to date have not assessed the relationship between peripheral biomarkers of

Neurite orientation dispersion and density imaging (NODDI) is a multi-shell diffusion-

weighted imaging approach that quantifies neurite density (neurite density index, NDI), axonal

coherence (orientation dispersion index, ODI), and the fraction of isotropic (free water) diffusion

(F-ISO) in the brain. 11 The NDI has been histologically validated to reflect the density of axons

and dendrites in the brain, and their loss in disease. 12,13 The ODI has been similarly validated on

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histology, with lower ODI indicating damaged white matter (WM). 13 NODDI is an extension of
conventional diffusion imaging approaches such as diffusion tensor imaging (DTI), which have
shown diminished WM integrity in sensorimotor, attention, and emotion regulation networks in

FS, including in the uncinate fasciculus (UF), superior temporal gyrus, and the insula. 14-17 One
study from our group has applied NODDI to a large sample of FS patients to report decreased
NDI in the cingulum bundle, UF, fornix/stria terminalis (FST), and corticospinal tract (CST) in

FS compared to controls. 18
One of the most prominent criticisms of currently available FS studies is that

comparisons are typically made to HCs rather than to participants with similar psychiatric
comorbidities or to organic counterpart conditions (e.g., epileptic seizures) to facilitate more in-

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depth evaluation of the underlying neuropathology. 19 First attempts to address this shortcoming
have been made recently by controlling for history of TBI and by comparing FS to epilepsy. 18,20

However, it remains unclear whether the observed WM abnormalities are specific to FS or
related to psychiatric symptoms in general. NI is well documented in psychiatric disorders

including depression, anxiety, and PTSD. 21,22 Due to the high prevalence of these disorders in
FS, a neuroinflammatory signature specific to FS needs to be defined. 3,23

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Thus, the first aim of the current study was to assess whether FS involves WM

microstructural abnormalities that do not exist in other mental health disorders (MHDs). Based

on the results from the literature, if such differences exist, we expected to observe decreased NDI
and ODI and increased FW in the cingulum bundle, UF, FST, and CST in FS when compared to
a group of patients with depression, anxiety, and PTSD, confirming that previously identified

WM abnormalities are specific to FS rather than psychopathology in general. 19,24 Secondly, we
wanted to assess the relationship between peripheral inflammatory markers and WM

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microstructure in the two groups with a specific focus on the same WM tracts. 11,18 Because
axonal loss and edema can result from NI, we expected to observe significant relationships
between the NDI and F-ISO and the serum biomarkers. 25-27 Lastly, we aimed to assess whether

inflammatory biomarkers are predictive of FS symptoms, hypothesizing that increased levels of
all biomarkers would be associated with more severe symptoms. 21,28-32
To achieve these aims, we first compared NODDI metrics in 23 patients with FS and 27

psychiatric controls (PCs) and then assessed associations between NODDI indices and serum
levels of tumor necrosis factor (TNF)-, TNF receptor 1 (TNF-R1), TNF-related apoptosis-

inducing ligand (TRAIL), interleukin (IL)-6, intercellular adhesion molecule (ICAM)-1, and
monocyte chemoattractant protein (MCP)-1. Finally, we assessed bivariate correlations between

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the serum biomarkers and clinical symptoms.
TRAIL and ICAM-1 were chosen because they were previously found to distinguish FS

from epileptic seizures. 2 Due to their association with FS, we expected both to be higher in the
FS group compared to PCs. TNF- is a pro-inflammatory cytokine that promotes cell

proliferation that was found to be elevated in psychiatric disorders involving inflammation, and
would likely be higher in PCs compared to FS. 32-35 TNF-R1 is a TNF- receptor that is similarly

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elevated in inflammatory psychiatric disorders, particularly bipolar disorder, 36 and likely higher
in PCs than FS. IL-6 is a pro-inflammatory cytokine capable of crossing the blood-brain-barrier
that is potentially elevated in PCs compared to FS. 30 Lastly, MCP-1 is a chemokine that
regulates monocyte and macrophage migration and infiltration into the brain28,37 and is

potentially involved in the pathophysiology of depression. 29 We expected MCP-1 to be elevated

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Methods

in PCs compared to FS.

Design

The current study was a prospective, cross-sectional observational study examining WM

microstructure and serum inflammatory biomarkers in FS and PCs.

Participants

We recruited participants with FS between the ages of 15 and 60 from an outpatient

psychology clinic and from the Epilepsy Monitoring Unit at the University of Alabama at
Birmingham (UAB). A definite diagnosis of FS38 confirmed through video-EEG without

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comorbid epilepsy was required for participation. Age- and sex-matched PCs with MHDs were
also prospectively recruited through an online advertisement on the UAB clinical trials website.
Participants underwent phone screening to confirm inclusion and exclusion criteria, including

medical history, current medications, and MRI contraindications. A partial waiver of consent
was obtained for the phone screening.

The following criteria were exclusionary in both groups: 1. autoimmune, inflammatory,

or neurological conditions (e.g., epilepsy, stroke, multiple sclerosis), 2. pregnancy or lactation, 3.

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MRI contraindications, 4. psychotic illness (e.g., schizophrenia, schizophreniform disorder, brief
psychotic disorder), 5. personality disorder, 6. substance use disorder, and 7. inability to
complete study procedures.

Study Protocol

The study received approval from the UAB Institutional Review Board and written

informed consent was obtained from participants prior to study procedures. Children under 18

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provided assent, and parents or legal guardians provided consent. The work was carried out in
accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).
Pregnancy testing was performed in women of reproductive age.

The following questionnaires were administered: demographics questionnaire, Hospital
Anxiety and Depression Scale (HADS), 39 Dissociative Experience Scale, 2nd edition (DES-II), 40
Brief Trauma Questionnaire (BTQ), 41 Holmes-Rahe Life Stress Inventory/Social Readjustment

Rating Scale, 42 Symptom Checklist 90 (SCL-90), 43 Somatoform Dissociation Questionnaire
(SDQ-20), 44 Quality of Life in Epilepsy (QOLIE-31), 45 and the WHO Disability Assessment

Schedule version 2.0 (WHODAS 2.0). 46 Participants with FS also reported FS over the previous
seven days using a semi-structured interview.

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Ten milliliters of blood were drawn from the antecubital fossa into serum separation
tubes. Serum was separated by centrifugation and stored at -80C at the UAB Center for Clinical

and Translational Sciences. Serum levels of TNF-R1 and TRAIL were determined with R&D
ELISA kits (Minneapolis, MN) in the UAB Metabolism Core Laboratory. IL-1, IL-6, TNF-,

ICAM)-1, and MCP-1 were quantified with multiplex electrochemiluminescence using a Meso
Scale Discovery QuickPlex SQ 120 imager (MSD; Rockville, MD).

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Neuroimage Acquisition

Participants completed neuroimaging on a 3-Tesla Siemens Prisma Magnetom with 20channel head coil (Siemens Healthineers, Erlangen, Germany). We collected two anatomical
sequences. The first one was a T1-weighted high-resolution Magnetization Prepared Rapid

Gradient Echo (MPRAGE) scan acquired with TR 2.4s, TE 2.22ms, 8 flip angle, 0.8mm slice

thickness, 208 slices, 256×256 matrix, 0.8mm3 voxel resolution. The second was a T2-weighted

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scan acquired with TR 3.2s, TE 563ms, GRAPPA acceleration factor 2, 0.8 mm slice thickness,
192 slices, 256×240mm field-of-view, 320×300 matrix, and 0.8 mm3 voxel resolution. Diffusion
scans were two identical multi-shell diffusion-weighted sequences (b-values centered around

1500s/mm2 and 3000 s/mm2). These were acquired in opposite (anterior-posterior and posterioranterior) phase encoding directions with TR 3230ms, TE 89.2ms, multi-band acceleration factor
4, 78 flip angle, 160 refocus flip angle, 1.5mm slice thickness, 92 slices, 210×210mm field-of-

view, 140×140 matrix, 1.5mm3 voxel resolution, 92 repetitions (46 with each of the two b-

repetitions.

values). Seven b0 images were also acquired, which were interleaved with the diffusion-weighted

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Neuroimage Processing

Preprocessing of structural and dMRI data was performed with AFNI version 20.1.18.
The anatomical images were skull-stripped (3dSkullStrip), deobliqued (3dWarp -deoblique),

47,48

and axialized (fat_proc_axialize_anat). Next, the dMRI images were co-registered with the

axialized T2-weighted image (3dAllineate) and then processed with the DIFFPREP module of
the NIH TORTOISE toolbox, version 3.2, 49,50 which consisted of motion correction and eddy

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current removal. Blip-up/blip-down (reversed phase encoding) EPI distortion correction was

performed with the DR-BUDDI module of TORTOISE, 51 which uses geometric averaging to
combine the two dMRI sequences acquired in opposite phase-encoding directions.

A whole-brain mask was created using the T2-weighted image (3dAutomask) and
resampled to match the resolution of the dMRI data (3dresample). Negative values were

removed from the dMRI images, and the images were normalized by dividing them by the first
b0 image in the sequence. The NODDI three-compartment model was fitted to the dMRI data

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using the NODDI toolbox, version 1.05 (available from nitrc.org) for Matlab, version 2020a
(Mathworks, Natick, MA), resulting in whole-brain NDI, ODI, and F-ISO maps. The NDI maps
were thresholded at 0.95 to reduce the impact of EPI distortion artifacts at the frontal, temporal,

and occipital poles. Both the DTI and NODDI maps were smoothed (AFNI’s 3dMedianFilter
with 3mm Gaussian kernel) and transformed into Montreal Neurological Institute (MNI) 2mm
space by first warping the T1-weighted image to standard space (3dQwarp) and then applying

Statistical Analyses

the warp to the diffusion maps (3dNwarpApply).

Group differences in demographic information, questionnaire data, and serum biomarkers

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were tested with independent-samples t-tests (continuous variables) and Chi-Square tests
(categorical variables) in IBM SPSS Statistics for Mac, Version 28.0 (IBM Corp, Armonk, NY).

Statistical significance was assumed at p<0.05 (two-tailed). Voxelwise group differences in
NODDI metrics were assessed with Analyses of Covariance (ANCOVAs) within AFNI’s

3dttest++, with all six serum biomarker levels (IL-6, TNF-, ICAM-1, MCP-1, TNF-R1, and TRAIL) added as
covariates of interest. Because all serum biomarkers were added to the regression model

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simultaneously, the reported associations are the unique variance contributed by each biomarker.
Three participants had undetectable levels (<0.25 pg/ml) of IL-6 and those values were set to

zero for the regressions. No other metabolite levels were undetectable or missing. A mask was
applied to restrict the analyses to the cerebral WM. Cluster thresholds for each outcome were
determined by performing 3dFWHMx on the residuals from the models, which estimates the

spatial noise of the error. The estimate was used with 3dClustSim to determine the following

cluster thresholds for nearest-neighbor 1 clustering and bi-sided thresholding with a voxel-level

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threshold of p=0.01 and cluster threshold of =0.05: NDI >657, ODI >664, and F-ISO >844
voxels. Bi-sided thresholding ensured that adjacent clusters with positive (FS>PCs) and negative
(FS<PCs) group differences were reported as separated clusters. Cluster thresholds reduce the

detect areas of significance.

number of false positives in mass univariate (voxelwise) analyses while retaining sensitivity to

For visualization of interaction effects, mean values in significant clusters were extracted

from each participant’s image with AFNI’s 3dROIstats function, and plotted against
inflammatory biomarker levels.

Lastly, Pearson correlation coefficients were obtained between serum biomarkers and
clinical outcomes. The analyses were corrected for the number of questionnaire scores (29) using

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a false discovery rate of 0.05, yielding a corrected p-value of 0.0126. 52 In the FS group,
associations between serum biomarkers and seven-day FS count were assessed with Spearman

correlations at p<0.05, which are more appropriate for ordinal data.

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Participant characteristics

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Results

Eighty-four individuals with FS were screened and 42 met inclusion criteria. Thirteen
withdrew prior to attending the study visit, five did not tolerate the MRI, and one did not

complete the diffusion imaging protocol. Twenty-three FS participants provided usable data.

Forty-seven PCs were screened, 31 were eligible, four withdrew prior to the study visit, and 27

provided usable data. Twenty-two FS patients and 25 PCs also provided blood and were included

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in the correlation analyses.

Demographic information and questionnaire data are shown in Table 1. The groups did
not differ in demographic variables. FS patients exhibited higher depressive symptoms on the

HADS, worse quality of life (QoL) and higher levels of psychopathology in several domains,
including somatization, more prevalent dissociative experiences, and more severe somatoform
dissociation than PCs. The FS group endorsed higher levels of disability in all domains,

including lower cognitive functioning and higher total disability. A higher proportion of FS
participants compared to PCs met DSM-5 criteria for lifetime exposure to a traumatic event. All

other outcomes, including stressful life events and anxiety, were not different between groups.
Serum levels of IL-6, TNF-, ICAM-1, MCP-1, and TRAIL were not different between

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groups (p>0.05). TNF-R1 levels were higher in the FS group (mean=1359.95 pg/ml) compared

to PCs (mean=1202.94 pg/ml; t(45)=-2.155, p=0.037).

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ep
Pr

23
4

16
1
2.04

2.20

17
2
-

p
0.668
0.507

1.747

0.627

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4.57
4.65

4.74
10.11

3.48
3.92

3.127
1.380

0.003**
0.174

215.17

141.35

194.37

140.03

0.521

0.605

32.66
58.75
45.64
33.41
38.93
64.65
51.20

31.01
16.25
26.09
25.14
29.39
37.03
23.03

-68.89
63.11
44.81
69.37
94.15
89.81

-12.83
15.35
18.27
21.92
18.28
17.76

-2.441
0.018*
†
-2.775
0.009**
-1.838
0.072
†
-4.031 <0.001***
-3.301†
0.002**
-6.627 <0.001***

1.67
1.84
1.27
1.79
1.64
0.94
1.27
1.04
1.03
1.47

1.11
1.16
1.67
1.31
1.19
1.17
1.29
1.16
1.06
1.04

0.46
0.99
0.70
0.93
0.61
0.43
0.37
0.69
0.33
0.66

0.31
0.77
0.53
0.74
0.44
0.48
0.70
0.93
0.42
0.43

5.069† <0.001***
2.991†
0.005**
†
2.147
0.040*
†
2.792
0.009**
†
3.903 <0.001***
1.979†
0.058
†
2.989
0.005**
1.188
0.241
†
2.959
0.006**
3.479†
0.002**

30.19

22.31

14.15

11.73

33.83

10.33

22.37

2.92

5.146† <0.001***

36.36
38.07

22.58
25.87

17.41
8.10

13.33
13.52

3.475†
0.001**
†
4.914 <0.001***

8.30
11.78

t/X2
-0.431
0.440

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PC (N=27)
Mean
SD
38.67 12.78

21
2

Age
Sex
Female (N)
Male (N)
Race
Asian (N)
Black/African American
(N)
White (N)
Other (N)
Weekly FS count
Questionnaires
HADS
Depression
Anxiety
LSI
Total score
QOLIE-31
Seizure Worry
Overall QoL
Emotional
Energy
Cognitive
Medication
Social
SCL-90
Somatization
Obsessive-Compulsive
Interpersonal Sensitivity
Depression
Anxiety
Anger-Hostility
Phobic-Anxiety
Paranoid Ideation
Psychoticism
Global Scale
DES-II
Total Score
SDQ
Total Score
WHODAS 2.0
Cognition
Mobility

FS (N=23)
Mean
SD
37.17 11.48

3.047†

0.005**

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Self-care
18.18 20.62
3.33 10.00
3.094†
0.004**
†
Getting Along
35.98 31.75
16.98 20.35
2.431
0.020*
Life Activities
47.27 33.21
24.44 21.90
2.776†
0.009**
Participation
49.62 27.12
18.36 22.92
4.373 <0.001***
Total Score
39.28 22.16
15.22 13.54
4.459† <0.001***
BTQ
5.571
0.018*
Trauma Exposure (N)
22
21
No Trauma Exposure (N)
0
6
Not Reported (N)
1
0
Serum Biomarker
IL-6 (pg/ml)
1.37
0.93
1.11
0.74
-1.047
0.301
1.61
0.39
1.56
0.52
-0.406
0.687
TNF- (pg/ml)
†
ICAM-1 (ng/ml)
590.23 184.47
520.73 102.99 -1.566
0.127
MCP-1 (pg/ml)
250.55 148.14
234.88
55.73 -0.468†
0.644
TNF-R1 (pg/ml)
1359.95 244.21 1202.94 253.47
-2.155
0.037*
TRAIL (pg/ml)
96.75 20.43
95.91 27.06
-0.119
0.904
†df adjusted for unequal variances. ††higher scores reflect better quality of life. *p<0.05 **p<0.01
***p<0.001

Table 1. Demographic information and questionnaire data for the final sample. BTQ=Brief
Trauma Questionnaire, DES-II=Dissociative Experiences Scale, 2nd edition, HADS=Hospital
Anxiety and Depression Scale, ICAM-1=intercellular adhesion molecule 1, IL=interleukin,

LSI=Life Stress Inventory, MCP=monocyte chemoattractant protein, QoL=Quality of Life,
QOLIE-31=Quality of Life in Epilepsy, 31-item version, SDQ-20=Somatoform Dissociation

Questionnaire, TNF=tumor necrosis factor, TNF-R1=TNF receptor 1, TRAIL= TNF-related

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apoptosis-inducing ligand, WHODAS 2.0=WHO Disability Assessment Schedule, 2nd edition

Main imaging results

The ANCOVAs revealed no significant clusters where NDI, ODI, or F-ISO differed

between groups. However, there were several significant cluster-level interactions between

serum inflammatory biomarkers and group on the NODDI outcomes (Table 2).

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Cluster
size
(mm3)

UF
Ant. corona radiata
Ant. thalamic radiation
Inf. fronto-occipital fasciculus

+11

2111

Ant. limb of internal capsule
Ant. thalamic radiation

Region

cluster 2

cluster 3
cluster 4
cluster 5

-2

870

-24

+26

+11

7557

+25

+32

+10

3462

+18

+30

+44

2102

-32

-13

+39

1744

-17

+12

+54

1084

Ant. thalamic radiation
Inf. fronto-occipital fasciculus
ODI

+44

+41

-10

1349

Post. thalamic radiation
Forceps major
Inf. fronto-occipital fasciculus
Inf. longitudinal fasciculus
Unspecified WM in sup. parietal and
lateral occipital lobe
Cingulum bundle
Body of corpus callosum

-31

-73

1243

-12

-40

+61

1092

-9

-18

+36

705

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UF
Genu of corpus callosum
Ant. limb of internal capsule
Ant. corona radiata
Ant. thalamic radiation
Forceps Minor
Inf. fronto-occipital fasciculus
Genu of corpus callosum
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Unclassified WM adjacent to superior
frontal gyrus
Sup. corona radiata
Sup. longitudinal fasciculus
Unclassified WM adjacent to superior
frontal gyrus

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group x TRAIL
cluster 1

group x TNF-
cluster 1

cluster 2
cluster 3

+27

-21

group x TNFR1
cluster 1

-26

group x MCP-1
cluster 1

group x IL-6
cluster 1

NDI

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Predictor

MNI
coordinates
(peak signal)
Hem. x
y
z

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cluster 2

UF
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Post. thalamic radiation
Forceps major
Inf. fronto-occipital fasciculus
Inf. longitudinal fasciculus

674

+12

-64

+50

666

-25

+32

1080

-73

680

Inf. fronto-occipital fasciculus
F-ISO

+34

+51

-5

1604

Unclassified WM adjacent to inf.
fronto-occipital fasciculus
UF
Ant. corona radiata
Ant. thalamic radiation
Inf. fronto-occipital fasciculus

+29

+48

-10

2670

-27

+26

+10

1684

-27

+28

+12

7206

+25

+32

3427

-32

-13

+39

1823

UF
Genu of corpus callosum
Ant. internal capsule
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
External capsule
Genu of corpus callosum
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Sup. corona radiata
Sup. longitudinal fasciculus

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group x TNFR1
cluster 1

+16

-26

cluster 2

-25

group x TRAIL
group x IL-6
cluster 1

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group x ICAM1
cluster 1

+30

cluster 5

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cluster 4

Splenium of corpus callosum
Post. limb of internal capsule
Retrolenticular part of internal capsule
Post. corona radiata
Post. thalamic radiation
External capsule
Unspecified WM in sup. parietal and
lateral occipital lobe

cluster 2

cluster 3

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Unclassified WM adjacent to sup.
frontal gyrus

group x TRAIL
cluster 1

+13

+22

+55

Unclassified WM adjacent to frontal
R
+22 +61 -14
pole
Table 2. Significant clusters with interactions between group and serum biomarkers.
Note: Only tracts with cluster overlap of >3% are reported.

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cluster 4

1836

Ant.=anterior, F-ISO=fraction of isotropic diffusion, Hem.=hemisphere, ICAM-1=intercellular

adhesion molecule 1, IL-6=interleukin 6, Inf.=inferior, L=left, MCP-1=monocyte

chemoattractant protein 1, NDI=neurite density index, MNI=Montreal Neurological Institute,

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ODI=orientation dispersion index, Post.=posterior, R=right, Sup.=superior, TNF-=tumor
necrosis factor , TNF-R1=tumor necrosis factor receptor 1, TRAIL=tumor necrosis factor-

Serum-by-group interactions

related apoptosis-inducing ligand, WM=white matter

IL-6 was positively associated with NDI in the FS group and negatively in PCs in a large

cluster comprising the left UF and adjacent structures (cluster 1). We also identified negative
correlations in FS and positive correlations in PCs between TNF-R1 and NDI in the UF and

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adjacent WM tracts (cluster 1). Other significant associations between NDI and serum
biomarkers are shown in Tables 2 and 3, and Figure 1.

ODI was differentially associated with several inflammatory biomarkers. Higher levels of
TNF- were associated with lower ODI in FS and higher ODI in PCs in the left cingulum bundle
and adjacent structures (cluster 3). Higher levels of ICAM-1 were associated with higher ODI in

FS and lower ODI in PCs in the left UF and adjacent structures (cluster 1). Other significant
relationships between serum biomarkers and ODI are shown in Tables 2 and 3, and Figure 2.

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F-ISO was differentially associated with IL-6, TNF-R1, and TRAIL. IL-6 was negatively
associated with F-ISO in FS and positively in PCs in the left UF and adjacent WM tracts (cluster
2). Higher TNF-R1 was associated with higher F-ISO in FS and lower F-ISO in PCs in the left

UF and surrounding structures (cluster 1). Other associations between the serum biomarkers and

PCs

IL-6

NDI F-ISO

NDI F-ISO

TNF-

ODI

TNF-R1

NDI F-ISO

NDI F-ISO

TRAIL

NDI ODI

NDI ODI

ICAM-1

ODI

ODI

MCP-1

NDI F-ISO

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Serum biomarker

F-ISO are shown in Tables 2 and 3.

NDI

Table 3. Associations between neurite density (NDI), orientation dispersion (ODI), fraction of

isotropic diffusion (F-ISO), and serum biomarkers of inflammation in non-epileptic seizures (FS)
and psychiatric controls (PCs).

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positive correlation; negative correlation (if both arrows present it indicates the relationship
were either positive or negative depending on the brain region)
ICAM-1=intercellular adhesion molecule 1, IL-6=interleukin 6, MCP-1=monocyte

chemoattractant protein 1, TNF-=tumor necrosis factor , TNF-R1=tumor necrosis factor

receptor 1, TRAIL=tumor necrosis factor-related apoptosis inducing ligand

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Figure 1. Clusters with significant interactions between group and serum biomarkers on neurite

density. Dotted lines indicate linear trendlines. Images are shown in neurological convention
(left=left). FS=functional seizures, IL-6=interleukin 6, MCP-1=monocyte chemoattractant
protein 1, NDI=neurite density index, PC=psychiatric controls, TNF-R1=tumor necrosis factor

receptor 1, TRAIL= tumor necrosis factor-related apoptosis-inducing ligand

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Figure 2. Clusters with significant interactions between group and serum biomarkers on neurite

orientation dispersion. Dotted lines indicate linear trendlines. Images are shown in neurological
convention (left=left). FS=functional seizures, ICAM-1=intercellular adhesion molecule 1,

ODI=orientation dispersion index, PC=psychiatric controls, TNF-=tumor necrosis factor ,
TRAIL= tumor necrosis factor-related apoptosis-inducing ligand

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Correlations specific to FS and PCs

Next, we investigated bivariate associations between serum biomarkers and the NODDI

metrics separately in FS patients and PCs. In FS, ICAM-1 was positively associated with NDI in

the left CST and adjacent structures (cluster 2), and TNF-R1 was negatively associated with NDI

in the left UF and adjacent structures. Other significant findings are listed in Table 4. TNF-,

MCP-1, and TRAIL were not independently associated with NDI in the FS group. In PCs, TNF-

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R1 was positively associated with NDI in the right UF and adjacent structures (cluster 2). TNFR1 was also positively associated with NDI in the left anterior corona radiata, cingulum, forceps
minor, and the genu and body of the corpus callosum (cluster 3). Other significant findings are

listed in Table 4. There were no associations between NDI and ICAM-1, TNF-, or TRAIL in
PCs.

In FS, MCP-1 was positively associated with ODI in the left CST and adjacent structures

(cluster 1) and in the right cingulum bundle and forceps minor (cluster 2). Other significant

associations are reported in Table 4. In PCs, ICAM-1 was positively associated with ODI in the
left CST and adjacent structures (cluster 6) and negatively associated with ODI in the left UF and
adjacent structures (cluster 1). TNF-R1 was negatively correlated with ODI in the left CST and

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adjacent tracts (cluster 4) and positively correlated with ODI in the left UF and adjacent tracts
(cluster 2). Other significant associations are reported in Table 4.

In FS, ICAM-1 was negatively associated with F-ISO in the left CST and adjacent WM

tracts (cluster 2). TNF-R1 was positively associated with F-ISO in the left UF and adjacent WM

tracts (cluster 1). In PCs, TNF-R1 was negatively associated with F-ISO in the right UF and

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adjacent structures (cluster 2), and the cingulum bundle and adjacent structures (cluster 3). Other
significant associations are reported in Table 4.

MNI
coordinates
(peak signal)
Hem. x
y
z

Predictor and
Region
direction of
association (+/-)

TNF-R1 in FS
cluster 1 (-)

MCP-1 in PCs
cluster 1 (-)

2172

+28

+33

+16

1772

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-48

-30

-15

+38

741

-33

+39

735

-33

+32

+15

1990

Unclassified WM adjacent to pre- and
postcentral gyrus

-31

-20

+61

657

Ant. internal capsule
Post. internal capsule
Ant. corona radiata
Sup. corona radiata
Ant. thalamic radiation
External capsule
UF
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Cingulum bundle
Ant. corona radiata

-33

-12

+39

4940

+26

+25

2065

-11

+24

+22

1092

UF
Ant. corona radiata
Ant. thalamic radiation
Inf. fronto-occipital fasciculus

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TNF-R1 in PCs
cluster 1 (+)

+40

cluster 3 (+)

Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Genu of corpus callosum
CST
Sup. corona radiata
Sup. longitudinal fasciculus
Unclassified WM adjacent to middle
frontal and precentral gyrus

cluster 2 (+)

Inf. fronto-occipital fasciculus

IL-6 in FS
cluster 1 (-)
ICAM-1 in FS
cluster 1 (+)

NDI

Cluster
size
(mm3)

cluster 2 (+)

cluster 3 (+)

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ODI

cluster 2 (+)
TRAIL in FS
cluster 1 (+)
TNF- in PCs
cluster 1 (+)
cluster 2 (+)

2395

-19

-50

+20

949

Post. corona radiata
Splenium of corpus callosum

-23

-49

+27

1166

-8

889

CST
Post. internal capsule
Sup. corona radiata
Post. corona radiata
Cingulum bundle
Forceps minor

cluster 2 (+)

cluster 3 (+)

cluster 4 (-)

-3

-23

+11

+44

+25

831

Ant. thalamic radiation
Inf. fronto-occipital fasciculus

+34

+51

-4

738

Sup. longitudinal fasciculus
Splenium corpus callosum
Body of corpus callosum
Forceps major
Sup. longitudinal fasciculus

R
L

+26
-10

-48
-35

+59
+14

954
734

+42

-58

+43

680

UF
Ant. corona radiata
Ant. internal capsule
Ant. thalamic radiation
Inf. fronto-occipital fasciculus
Forceps minor
Post. corona radiata
Sup. corona radiata
Sup. longitudinal fasciculus
Splenium of corpus callosum
Body of corpus callosum
Post. corona radiata
Forceps major
Post. thalamic radiation
Inf. fronto-occipital
Inf. longitudinal fasciculi
Forceps major

-21

+32

1913

+27

-26

+30

1615

-4

-36

1284

-35

-60

-1

915

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cluster 3 (+)
ICAM-1 in PCs
cluster 1 (-)

+49

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MCP-1 in FS
cluster 1 (+)

+38

ICAM-1 in FS
cluster 1 (+)

cluster 2 (+)

Unspecified WM adjacent to inf.
fronto-occipital gyrus
Splenium of corpus callosum
Forceps major

IL-6 in FS
cluster 1 (-)

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Forceps minor
Genu of corpus callosum
Body of corpus callosum

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cluster 2 (+)

cluster 3 (+)

+31
+8

878
833

+16

-62

+52

768

+35

+34

+10

679

Ant. internal capsule
Post. internal capsule
Sup. corona radiata
Sup. fronto-occipital fasciculus
Ant. thalamic radiation
UF
Genu of corpus callosum
Ant. corona radiata
Ant. thalamic radiation
Inf. fronto-occipital fasciculus
Forceps minor
Ant. internal capsule
Sup. corona radiata
Sup. fronto-occipital fasciculus
Ant. thalamic radiation
CST
Post. internal capsule
Retrolenticular internal capsule
Post. corona radiata
Sup. corona radiata
External capsule
F-ISO

-20

-9

+20

1469

-23

+37

1445

+20

+17

+11

1012

-27

-22

+14

805

Unclassified WM adjacent to ant.
thalamic radiation and inf. frontooccipital fasciculus

+21

+60

-14

3308

Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
Genu of corpus callosum
CST
Sup. corona radiata
Sup. longitudinal fasciculus
Ant. corona radiata

+29

+32

+15

1585

-30

-14

+37

919

-32

+28

+15

883

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IL-6 in FS
cluster 1 (-)

cluster 4 (-)

-25
-25

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TNF-R1 in PCs
cluster 1 (+)

+42
-27

cluster 8 (+)

R
L

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cluster 7 (+)

Sup. longitudinal fasciculus
CST
Retrolenticular internal capsule
Post. corona radiata
Unclassified WM adjacent to sup.
lateral occipital cortex and sup.
parietal lobe
Ant. thalamic radiation
Inf. fronto-occipital fasciculus

cluster 5 (-)
cluster 6 (+)

ICAM-1 in FS
cluster 1 (-)

cluster 2 (-)
cluster 3 (-)

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TNF-R1 in FS
cluster 1 (+)

UF
Ant. corona radiata
Ant. thalamic radiation
Inf. fronto-occipital fasciculus

TNF-R1 in PCs
cluster 1 (-)

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Ant. thalamic radiation
Inf. fronto-occipital fasciculus

-32

+28

+15

1936

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Ant. internal capsule
L
-32 -12 +42 3408
Ant. corona radiata
Sup. corona radiata
Ant. thalamic radiation
External capsule
Inf. fronto-occipital faciculus
cluster 2 (-)
UF
R
+21 +36 +9
2109
Ant. corona radiata
Ant. thalamic radiation
Forceps minor
Inf. fronto-occipital fasciculus
cluster 3 (-)
Cingulum bundle
L
-19 +35 +8
1109
Genu of corpus callosum
Body of corpus callosum
Anterior corona radiata
Anterior thalamic radiation
Forceps minor
Table 4. Clusters with significant associations between NDI, ODI, F-ISO and serum biomarkers
in FS and PCs.

Note: Only tracts with cluster overlap of >3% are reported.
Ant.=anterior, CST=corticospinal tract, F-ISO=fraction of isotropic diffusion, FS=functional

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seizures, Hem.=hemisphere, ICAM-1=intercellular adhesion molecule 1, IL-6=interleukin 6,
Inf.=inferior, L=left, MCP-1=monocyte chemoattractant protein 1, NDI=neurite density index,
MNI=Montreal Neurological Institute, ODI=orientation dispersion index, PCs=psychiatric

controls, Post.=posterior, R=right, Sup.=superior, TNF-=tumor necrosis factor , TNFR1=tumor necrosis factor receptor 1, TRAIL=tumor necrosis factor-associated apoptosis-

inducing ligand, UF=uncinate fasciculus, WM=white matter

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Correlations between serum biomarkers and clinical outcomes

Significant correlations between serum biomarkers and clinical outcomes (questionnaire

scores) are shown in Figure 3. There were no significant correlations (at p<0.0126) between IL6, MCP-1, TRAIL, and any of the clinical outcomes.

Higher TNF- was associated with lower medication-related QoL on the QOLIE-31 in

the FS group (r=-0.670, p<0.001). Higher ICAM-1 was associated with higher interpersonal

sensitivity (r=0.603, p=0.003), hostility (r=0.549, p=0.008), and global SCL-90 score (r=0.527,

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p=0.012) in FS. ICAM-1 was also positively associated with the Life Activities (r=0.650,
p=0.001) and Participation subscales (r=0.630, p=0.002) of the WHODAS2.0, and with total
WHODAS2.0 disability scores (r=0.542, p=0.009) in FS, indicating higher levels of disability

with increasing ICAM-1. There were no such correlations in PCs. Finally, TNF-R1 was
positively associated with HADS depression (r=0.550, p=0.008) and anxiety (r=0.554, p=0.007)
and negatively associated with emotion-related QoL (r=-0.647, p=0.001) in FS. Higher TNF-R1

was also associated with greater disability related to cognitive functioning (r=0.641, p=0.001),
getting along with others (r=0.569, p=0.006), participation (r=0.642, p=0.001), and with total

disability (r=0.628, p=0.002) in FS. These associations were not present in PCs.
There were no associations between serum biomarkers and one-week FS count

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(Spearman correlation coefficient) in the FS group. There were no associations with stressful life

events, dissociative experiences, or somatoform dissociation in either group.

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Figure 3. Associations between serum biomarkers and clinical outcomes in functional seizures
(FS, solid circles) and psychiatric controls (PCs, empty circles). Correlations are significant at

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p<0.0126 (adjusted for multiple comparisons) in FS, and not significant in PCs. Trendlines show

linear relationships in FS (solid line) and PCs (dotted line).

Discussion

The main aim of this study was to assess WM microstructure in FS compared to PCs and

its relationship to peripheral biomarkers of inflammation. We did not find differences between

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FS WM microstructure compared to controls with MHDs. However, as expected, we identified
several group differences in the relationship between peripheral inflammatory biomarkers and
NODDI metrics in the UF, cingulum bundle, and CST, as well as significant associations
between serum biomarkers and clinical symptoms.

The lack of group differences in NODDI metrics is the first important finding in this

study. As mentioned above, several studies have compared diffusion-based metrics between FS

and HCs using DTI, and one used NODDI in individuals with FS and history of TBI. In the only

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study that used NODDI in FS, Goodman et al. 18 reported decreased NDI, F-ISO, and FA and
increased MD in the cingulum bundle, UF, FST, and CST, and we expected to replicate these
findings. However, as we did not find the expected differences, we believe this null finding is

very important as it indicates that differences in sample characteristics (individuals with prior
TBI versus general FS population) and lack of control for comorbid MHDs may explain the
discrepant findings. 53

All other studies of WM integrity in FS have focused on comparisons with HCs.
Hernando et al. 15 compared UF integrity between eight individuals with FS and eight matched

HCs. While the FS group showed more rightward asymmetry in the UF than controls as
indicated by the number of streamlines on DTI tractography, FA did not differ between groups.

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Although their sample size was small, their findings are consistent with the current results,
indicating no impaired WM integrity in the UF in FS compared to HCs. Conversely, Lee et al.

reported increased FA in the left UF of FS patients compared to HCs, indicating greater WM
integrity. No psychiatric comorbidities or correlations with clinical symptoms were reported,

thus, it is not possible to determine if the UF differences were related to FS or to underlying
comorbid psychiatric comorbidities. Jungilligens et al. 16 also reported a lack of differences in FA

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values throughout the brain between 20 FS and 20 HCs. Lower FA in the UF, FST, and corpus
callosum was related to impaired performance on an emotion processing task, earlier age of

symptom onset, and higher FS frequency, indicating that UF integrity has important clinical

implications. A leftward as opposed to rightward UF asymmetry on DTI tractography was also

found in FS. The authors acknowledged that because of significant psychiatric comorbidities in

the FS group, it was not possible to determine whether the observed relationships were specific

to FS or to psychopathology in general. Finally, Sone et al. (2019) 54 reported decreased FA and

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increased mean diffusivity (MD), indicating impaired WM integrity throughout the WM in 17
patients with FS. The affected tracts were not further detailed, so it is not possible to compare
their findings to the current study. The authors also recognized that psychiatric comorbidities

were a significant confounding factor in interpreting the findings, although no further attempt
was made to address this. In summary, because of the uncertain contributions from MHDs and
neurological comorbidities in prior FS studies, and our current findings of no WM differences

between FS and PCs, we suspect that previously identified WM abnormalities in FS are
connected to comorbid MHDs or specific psychological symptoms commonly experienced by FS

patients rather than constituting FS-specific pathology. This notion is further supported by the
fact that abnormalities in the UF and other WM tracts have been previously reported in major

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depressive disorder (MDD), 55-57 generalized anxiety disorder, 57 and PTSD, 58-61 with one study
suggesting that UF abnormalities may be related to dissociative symptoms in PTSD. 60 It is clear

that identifying a specific neurological signature of FS must involve comparisons with
psychiatric conditions involving symptoms similar to FS.

When analyzing the relationships between serum biomarkers of inflammation and the

NODDI metrics, the most prominent associations were found in the UF, which is a WM tract that

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connects the medial temporal with the medial frontal lobes. 62 Among other functions, the UF is
thought to allow individuals to draw from prior experience (memory) when making decisions

and it is known to take part in emotion regulation. 62,63 DMRI abnormalities in the UF have been

observed in FS/FNSD studies14,15,19 but, as mentioned above, they may be related to the presence
of comorbid mental health disorders rather than specific to FS. In our FS group, higher IL-6 was

associated with better WM integrity (higher NDI and lower F-ISO) in a largely overlapping
cluster comprising the left UF and adjacent WM (Figure 4). Conversely, in PCs, IL-6 was

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associated with lower NDI and higher F-ISO, as expected based on prior literature that has
pointed to IL-6 in the pathophysiology of depression. 28 IL-6 is a pro-inflammatory cytokine
released by various immune cells in response to injury and infection. It has important immune

functions, such as inducing immune cell growth and differentiation, and potentiating
inflammatory responses. 64,65 Chronic IL-6 overproduction has been linked to the development of

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autoimmune diseases and certain cancers66 and to MDD. 30

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Figure 4. Clusters with significant interactions between group and interleukin-6.

NDI only

Blue=interactions on F-ISO only, Purple=interactions on NDI and F-ISO, Red=interactions on

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Our findings in the PC group are consistent with the inflammatory hypothesis of
depression28,67 and suggest that compromised WM integrity in the UF may be one link between

IL-6 and depressive symptoms. This is plausible because IL-6 can cross the blood-brain barrier
and induce neurodegeneration. 68-70 Our results also indicate that detrimental relationships

between IL-6 and UF integrity may distinguish depression from FS. We note that our
observations are correlative, so it is not possible to determine whether IL-6 caused damage to the

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UF. Further, serum IL-6 was not significantly higher in PCs than FS, and was not associated with
clinical outcomes. Because our control group was partly composed of individuals with anxiety
and trauma-related disorders, existing relationships between IL-6 and clinical symptoms may
have been concealed by the heterogeneity of the control group.

In contrast to the relationship between IL-6 and UF in PCs, TNF-R1 was associated with

UF pathology in FS. In FS, higher levels of TNF-R1 were associated with lower UF NDI and
higher UF F-ISO (Figure 5), with the opposite relationships observed in PCs. TNF-R1 is

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expressed on immune cells including microglia and macrophages and is involved in cellular
apoptosis, survival, differentiation, and inflammation. 71 Because TNF-R1 binding exacerbates
inflammation, elevated serum TNF-R1 is used as a marker of active inflammation. TNF-R1 was

the only inflammatory biomarker that was significantly elevated in the FS group, and increased
TNF-R1 was associated with higher depression and anxiety, and worse QoL, cognitive
functioning, and disability. We initially expected that TNF-R1 would be elevated in PCs

compared to FS because it was previously linked to bipolar disorder. 36 Because in the current
study PCs included individuals with depression but not bipolar disorder, this may explain the

discrepant findings. Together, our results suggest that while both FS and other psychiatric
conditions involve inflammation, the effects of this on WM integrity may be dependent to the

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particular inflammatory pathways activated, with TNF-R1 related to UF pathology in FS, and IL-

6 related to UF pathology in PCs.

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Figure 5. Clusters with significant interactions between group and tumor necrosis factor receptor
1.

Blue=interactions on F-ISO only, Purple=interactions on NDI and F-ISO, Red=interactions on

NDI only

Another WM tract affected by serum biomarker levels was the CST. The CST is an

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efferent WM tract originating in cortical motor areas with a main role in the control of voluntary
motor activities, which may be affected during FS if patients are experiencing convulsions,
stiffness, and weakness. 72 The CST was chosen as a tract of interest for distinguishing FS from

PCs due to the prominent motor symptoms of FS and prior literature showing CST abnormalities
in FS. 18 We found no differences in CST WM microstructure between FS and PCs, which is an

important finding that suggests CST integrity could be a transdiagnostic correlate of motor
symptoms in psychiatric disorders. This is plausible given that motor symptoms such as

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psychomotor slowing and agitation are also prominent in MHDs. 73-76 Despite the lack of overall
differences, the two groups were characterized by different serum biomarker profiles with

respect to CST integrity. In FS, higher ICAM-1 was associated with higher NDI and lower F-

ISO in the CST, indicating higher tract integrity and suggesting that ICAM-1 is not a risk factor

for CST damage in FS. However, higher ICAM-1 was associated with higher levels of disability

and psychological symptoms in FS, suggesting that ICAM-1 may be related to FS symptoms via
other mechanisms.

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Lower MCP-1 was associated with lower ODI in the CST of FS patients. ODI reflects the
complexity of the underlying WM structure; areas with crossing fibers and dendritic
arborizations and the gray matter have higher ODI, whereas areas with parallel axons, such as

major WM bundles, have lower ODI. In disease, increased ODI in the gray matter has been
associated with regrowth and reorganization, 77 so it may be particularly useful for assessing
therapeutic response in longitudinal studies. In the WM, increased ODI indicates axonal

degeneration and demyelination. 13 This means that individuals with FS and lower MCP-1 levels
were more likely to show CST damage in our study. MCP-1 levels did not correlate with any

clinical outcomes.

In PCs, decreased ICAM-1 and increased TNF-R1 were associated with lower CST ODI,

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which points to TNF-R1 as a risk factor for CST abnormalities in this group, although because of
the lack of clinical correlations, the potential effects of such changes are unclear.

The final brain region to be affected by serum biomarkers was the cingulum bundle,
particularly in PCs, where higher TNF-R1 was associated with higher NDI and lower F-ISO. The

cingulum bundle connects the medial temporal with the frontal and parietal lobes and subcortical
nuclei with the cingulate cortex. It supports executive functioning, emotion and pain regulation,

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and memory, and structural and functional abnormalities in the cingulum bundle have been

implicated in depression, 78,79 PTSD, 80,81 and FNSDs. 82 Our findings show that PCs with higher
levels of serum TNF-R1 show higher cingulum bundle integrity than PCs with lower TNF-R1.

Because TNF-R1 levels were not related to clinical symptoms in PCs, their clinical relevance is
unclear, but they are consistent with prior literature implicating the cingulum in disorders

presenting with mood symptoms. 83

We found associations between serum biomarkers and NODDI metrics in many other

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WM tracts. These results are reported to guide the selection of brain regions for further study of
FS neuropathophysiology. The only association with TRAIL in FS was found in the right
anterior thalamic radiation and inferior fronto-occipital fasciculus, which were not tracts of

interest. TRAIL was also not related to any clinical symptoms in the FS group. Because TRAIL
appears to be elevated immediately after FS, 2 this biomarker should remain a target for future
studies.

Finally, we note the relationships between three of the six inflammatory biomarkers
(TNF-, TNF-R1, and ICAM-1) and disability, QoL, and psychological problems including

anxiety and depression in FS. TNF-R1 showed the most prominent associations with clinical
symptoms, including with higher depression, anxiety, lower emotional QoL, and higher levels of

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disability. TNF-R1 was related to decreased F-ISO in the cingulum bundle and to lower NDI and
higher F-ISO in the UF in FS, suggesting that these areas may be particularly relevant to FS

symptoms. Symptoms specific to FS, such as somatoform dissociation, were not related to any
serum biomarkers tested in the current study. We suggest that future studies assess a broader

range of serum biomarkers of inflammation to determine whether any may be related to
dissociative symptoms. Because dissociative symptoms are central to FS, PTSD and dissociative

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disorders, candidate biomarkers for FS should be able to distinguish between patients with these
conditions.

There were no significant associations between serum biomarkers and any clinical
symptoms in PCs. This was unexpected given that elevated levels of several of the tested

biomarkers have previously been identified in MDD. 67,84-88 However, the lack of significant

associations may be due to differences in measures in the current study, which were primarily
selected to capture FS symptomatology, due to heterogeneity of MDDs in both groups, or to

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treatment effects.

The elevated TNF-R1 levels in the FS group alongside the lack of WM abnormalities
when compared to PCs provide support for the two-hit hypothesis of FS, which suggests that

traumatic experiences prime the brain toward NI when trauma is subsequently experienced later
in life. We did observe that higher serum TNF-R1 was associated with UF pathology in FS, but
the overall lack of UF abnormalities when compared to PCs suggests that either inflammation is

not severe enough to damage WM integrity in FS, or that relationships between traumatic life
experiences, inflammation, and WM damage are not unique to FS. The similar levels of trauma

exposure in our two groups suggest that the latter may be the case. However, we also note that
inflammatory biomarkers in serum are not a direct measure of NI, and animal models of FS that

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would allow us to quantify inflammatory cytokines in the brain have not been developed. To test
the two-hit hypothesis in humans more directly, longitudinal monitoring of FS patients with

neuroimaging could be used to determine the relative timing of traumatic experiences, NI, and

FS symptoms.

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Limitations

There are several limitations to the current study that should be considered. First, our FS
group was recruited from an outpatient specialty clinic and all patients were undergoing, or had
recently completed, psychological treatment for FS. Psychotherapeutic interventions such as
mindfulness have been shown to affect immune system functioning, including levels of

circulating cytokines, in patients with anxiety and depression. 89-91 Thus, it is possible that

untreated patients with FS would show different relationships between inflammatory cytokines,

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neuroimaging markers, and clinical symptoms than the current sample. Because most FS
participants were experiencing few FS each week (average=2/week; Table 1), this may also have
affected our ability to detect expected associations between inflammatory biomarkers and FS

frequency. Further, we did not control for the therapeutic approaches used as participants
received personalized interventions as appropriate for their presentation. 92 The specific effects of
these interventions on WM and immunity are not known and need to be investigated.

We did not time the serum collection in relation to FS. It is possible that there is a
relationship between inflammatory biomarkers and time since last FS. For example, Gledhill et

al. 2 described cytokine elevations within 24 hours of FS and epileptic seizures. Future studies
may account for time since the most recent FS when assessing relationships between

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inflammatory biomarkers and neuroimaging outcomes.
Lastly, our control group was comprised of individuals with various MHDs. We recruited

participants with a range of psychological diagnoses to better reflect comorbid symptoms in FS
that could account for neuroimaging and biomarker abnormalities in FS. By increasing the

specificity of our findings for FS, we may have lost some sensitivity for detecting differences
between FS and each of these diagnoses.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4435791

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Conclusion

For the first time, we report relationships between peripheral inflammatory biomarkers

and WM abnormalities and symptom severity specific to FS. The UF, CST, and cingulum bundle
emerged as relevant areas when distinguishing FS from PCs. Once our findings are replicated in

future studies, specific serum biomarkers related to inflammation could be used in clinical

settings to aid FS diagnosis and to quantify treatment response, with TNF-R1 in particular being

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a promising candidate for further study. This may be particularly useful in settings where videoEEG is not available. The lack of group differences in WM microstructure suggests that
previously identified WM abnormalities in FS versus HCs may in part be related to the

psychological symptoms experienced by FS patients.

Acknowledgments

We would like to thank Dr. Christopher Litton and Holly Yazdi for assistance with

recruitment and data collection, respectively.

Declaration of interest

rin

JPS is funded by NIH, DoD, State of Alabama, Shor Foundation for Epilepsy Research,
UCB Pharma Inc., and NeuroPace Inc. Has served on consulting/advisory boards for Greenwich

Biosciences Inc., NeuroPace, Inc., Serina Therapeutics Inc., LivaNova Inc., UCB Pharma Inc.,
iFovea, AdCel Biopharma LLC, and Elite Medical Experts LLC. He serves as an editorial board

member for Epilepsy & Behavior, Journal of Epileptology (associate editor), Epilepsy &

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4435791

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Behavior Reports (Editor-in-Chief), Journal of Medical Science, Epilepsy Currents (contributing
editor), and Folia Medica Copernicana.
Funding

This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors. This study was supported by start-up funds provided to JPS

by the Department of Neurology, UAB Heersink School of Medicine. The sponsor was not

involved in the study design; in the collection, analysis and interpretation of data; in the writing

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of the report; and in the decision to submit the article for publication.
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