BOLD signal variability as potential new biomarker of functional neurological disorders

Abstract

Background
Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored.

Methods
This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months.

Results
The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome.

Conclusion
This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.

Full text

NeuroImage: Clinical 43 (2024) 103625
Available online 31 May 2024
2213-1582/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
BOLD signal variability as potential new biomarker of functional
neurological disorders
Ayla Schneider
a
,
b
,
1
, Samantha Weber
a
,
b
,
c
,
1
, Anna Wyss
a
,
d
, Serafeim Loukas
a
,
e
,
Selma Aybek
a
,
f
,
*
a
Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland
b
Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
c
University of Zurich, Psychiatric University Hospital Zurich, Department of Psychiatry, Psychotherapy and Psychosomatics, 8032 Zurich, Switzerland
d
Graduate School for Health Sciences (GHS), University of Bern, 3006 Bern, Switzerland
e
Institute of Bioengineering, Ecole Polytechnique F´
ed´
erale de Lausanne (EPFL), 1015 Lausanne, Switzerland
f
Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
ARTICLE INFO
Keywords:
Conversion disorders
Longitudinal
Prognostic
Biomarker
Insula
Supplementary motor area
ABSTRACT
Background: Functional neurological disorder (FND) is a common neuropsychiatric condition with established
diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms
remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain
disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks.
However, the relationship between identied brain alterations and disease progression or improvement is less
explored.
Methods: This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients
with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was
computed for each participant and the group-wise difference was calculated. Second, we investigated the po-
tential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who
attended a follow-up measurement after eight months.
Results: The results demonstrated higher BOLD signal variability in key networks, including the somatomotor,
salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed
an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an
improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD
signal variability in the left insula at baseline predicted a worse clinical outcome.
Conclusion: This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic
nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The
insula and SMA emerge as promising regions for further investigation as prognostic and state markers.
1. Introduction
Functional neurological disorder (FND) is a common medical con-
dition (Bennett et al., 2021; Carson and Lehn, 2016;Hallett et al., 2022)
presenting with diverse neurological symptoms and typically motor or
sensory symptom patterns that cannot been explained by an underlying
classical neurological disorder (Aybek and Perez, 2022; Espay et al.,
2018a). Currently there are well established rule-in diagnostic criteria
(American Psychiatric Association, 2022;Varley et al., 2023) and
multimodal treatment options available (Bennett et al., 2021;Hallett
et al., 2022;LaFaver, 2020; Varley et al., 2023), but our understanding
of the neuropathophysiological mechanisms, the underlying develop-
ment and clinical course of the diverse symptoms remains limited
(Drane et al., 2021).
Using diverse neuroimaging techniques, recent research has unveiled
FND as a multi-network brain disorder (Drane et al., 2021;Perez et al.,
2021). This condition has been characterized by alterations in task-
based as well as in resting-state analysis across limbic/salience
* Corresponding author at: Faculty of Sciences and Medicine, University of Fribourg, Ofce 2.106d, Chemin du Mus´
ee 5, 1700 Fribourg, Switzerland.
E-mail address: selma.aybeck@unifr.ch (S. Aybek).
1
These authors contributed equally.
Contents lists available at ScienceDirect
NeuroImage: Clinical
journal homepage: www.elsevier.com/locate/ynicl
https://doi.org/10.1016/j.nicl.2024.103625
Received 5 April 2024; Received in revised form 16 May 2024; Accepted 29 May 2024

NeuroImage: Clinical 43 (2024) 103625
2
(Demartini et al., 2021; Hassa et al., 2021, 2017; Stone et al., 2007; Voon
et al., 2011; Weber et al., 2024), self-agency (Baek et al., 2017; Maurer
et al., 2016; Voon et al., 2010), attentional (Marapin et al., 2020; Stager
et al., 2022) and sensorimotor networks (Aybek et al., 2015;Voon et al.,
2011; Weber et al., 2022b). Disturbed self-agency (Baek et al., 2017;
Marapin et al., 2020) in FND patients seems to be manifested in a
hypoactivation of the right temporal parietal junction (TPJ) and
decreased connectivity of the TPJ with limbic and sensorimotor regions
in task-based (Voon et al., 2010) as well as resting-state (Maurer et al.,
2016) studies that could potentially be reversed by neuromodulation
(Bühler et al., 2024). These ndings go align with a lower sense of
control in a game manipulated agency in children with functional
seizure where also a poorer selective attention and cognitive inhibition
was reported (Stager et al., 2022). In the limbic network, hyper-
activation of the amygdala while exposed to fearful emotional stimuli
(Voon et al., 2010) as well as during emotional stimulation with
simultaneously passive movement of the affected hand (Hassa et al.,
2017) and during cognitive reappraisal (Hassa et al., 2021) was re-
ported. In an earlier fMRI study patients showed hyperactivation of the
left insula while trying to move their affected limb compared to HC who
simulated the weakness (Stone et al., 2007). These ndings go align with
the reporting of higher right amygdala and left anterior insula activa-
tions during internally and externally generated movements (Voon
et al., 2011). In the same study lower activation of the right supple-
mentary motor cortex (SMA) was revealed (Voon et al., 2011). In
contrast a higher activation of the SMA during negative emotions in FND
patients were reported indicating that there might be a link between
emotions and motor dysfunctions (Aybek et al., 2015). This limbic-
motor interaction was also shown in the form of increased connectivity
in a resting-state analysis between cingulo-insular networks and motor
control areas which showed a correlation with symptom severity (Diez
et al., 2019; Li et al., 2015a, 2015b). Likewise, in our previous study on
the same cohort, we detected altered resting-state insular co-activation
patterns with the somatomotor- and default mode networks (DMN),
which was associated with duration of illness (Weber et al., 2024).
Despite these tremendous advances in the understanding of the role
of these brain regions and network connectivity in FND, there is a lack of
knowledge on the links between the identied alterations in brain ac-
tivity and the clinical outcome −the progression or amelioration of the
disorder. In a recent positron emission tomography (PET) study, the
resting state metabolism of left and right subgenual anterior cingular
cortex at inclusion was negatively correlated with improvement of
motor symptoms after three months, suggesting this could represent a
metabolic state marker (Conejero et al., 2022). Similarly, patients with
functional tremor presented not only with a signicant improvement in
tremor severity but also with a decreased activation in the anterior/
paracingulate cortex during a basic-emotion task after 12 weeks of
cognitive behavioural therapy (Espay et al., 2019). Furthermore, a study
in functional movement disorders patients undergoing a one-week
multidisciplinary motor retraining treatment program identied
greater primary motor cortex activation at baseline when the patients
responded to the therapy program, together with a shift in amygdalar
functional connectivity from motor regions towards prefrontal regions
(Faul et al., 2020). These studies not only underscore that functional
alterations in the brain might revert but can also be detected in response
to a clinical improvement. Understanding how brain alterations evolve
over time and parallels clinical course could bring valuable information
into mechanisms but may also ultimately serve clinical purpose in
identifying prognostic biomarkers. Within-group studies are important,
as they can account for the coincidence of other neuropsychiatric dis-
eases (Perez et al., 2021) and longitudinal studies are needed to help
disentangle between state and trait biomarkers (Conejero et al., 2022;
Perez et al., 2021).
Until now, functional alterations in FND were mostly studied using
the averaged time-course of blood oxygen level-dependent (BOLD) sig-
nals. However, even the resting brain is highly variable in its activity and
constantly adapting to its internal and external environment (Brembs,
2021). While in the past particularly this variability of the BOLD signals
was considered as noise, it has attracted the attention of researchers in
recent years as a more dynamic measurement of brain activity (Garrett
et al., 2011). This variability in the BOLD signal is suggested to represent
an important feature of proper brain function (Baracchini et al., 2021;
Garrett et al., 2018, 2013) where an optimal variability encompasses a
balance that provides enough stability but also the necessary exibility
for the execution of brain functionality (Armbruster-Genç et al., 2016).
Therefore, this more dynamic measurement is an option to evaluate the
longitudinal changes in brain functioning, which has never been
explored in FND so far. For example, motor recovery after stroke showed
a correlation with altered temporal variability of the ipsilateral pre-
central gyrus (Hu et al., 2018). In other neuropsychiatric conditions,
there is recent proof of such alterations in brain variability across diverse
regions (Kebets et al., 2021; Li et al., 2019; Wei et al., 2023; Zanella
et al., 2022): For example, a reduction in BOLD signal variability was
associated with improved emotion regulation in with attention/decit/
hyperactivity disorder (ADHD), borderline personality disorder, or bi-
polar disorder (Kebets et al., 2021; Zanella et al., 2022). Also, patients
ADHD presented with overall increased resting-state BOLD signal vari-
ability in the prefrontal cortex (Nomi et al., 2018), as well as the
sensorimotor- and salience networks (Kebets et al., 2021). Likewise,
higher BOLD variability in schizophrenia across diverse brain networks
were positively correlated to severity of symptoms (Wei et al., 2023).
Moreover.
In this exploratory study, by rst examining alterations in BOLD
signal variability between FND patients and healthy controls (HC) we
aimed to uncover potential signatures of disrupted neural dynamics that
may be associated with FND (cross-sectionally) hypothesizing to identify
increased BOLD signal variability in diverse brain networks previously
associated to FND pathology. Second, we investigated alterations in
BOLD signal variability in FND patients over time (longitudinally) to
examine alterations in BOLD signal variability with regards to clinical
course, hypothesizing that an improvement in symptom severity might
align with a reduction in BOLD signal variability in distinct brain re-
gions. Overall, this study aims at identifying both state and prognostic
factors of FND.
2. Methods
2.1. Participants
86 Patients with functional neurological disorder (FND) and 76
healthy controls (HC) participated in a resting-state fMRI study between
june 2020 and february 2022, where clinical data were also acquired.
Cross-sectional structural and functional imaging data of this cohort
(FND patients and HC) have previously been published (Weber et al.,
2024, 2022a). FND patients were recruited through the outpatient clinic
of the department of Neurology, Inselspital, Bern University Hospital,
switzerland. The FND diagnosis was established by a certied neurolo-
gist according to DSM-5 criteria (American Psychiatric Association,
2022) and ICD-10 (World Health Organization, 2004) of FND (motor
FND (F44.4), non-epileptic attack FND (F44.5), sensory FND (44.6) or
mixed FND (44.7)) and using positive signs (Stone et al., 2011). Mixed
FND was only diagnosed if the patients presented with all three symp-
tom types (motor, sensory and non-epileptic attacks). HC comparable in
age and sex were recruited through public advertisement. Inclusion
criteria for patients included a diagnosis of FND and an age of minimum
16 years. Exclusion criteria for both groups included: Persons suffering
from epilepsy, psychosis, severe major depressive disorder, or alcohol/
drug abuse (based on clinical documentation); previously brain surgery;
implanted medical devices (pacemaker, infusion pumps); metallic
foreign bodies in the head region except braces or dental llings; breast-
feeding or pregnant women; women with the intention to get pregnant;
impaired understanding of the task due to language or cognitive
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NeuroImage: Clinical 43 (2024) 103625
3
difculties. Board-certied neurologists performed the screening for
psychosis, major depressive disorder, and/or drug abuse. Regardless of
their clinical status, the patients were invited for a follow-up examina-
tion after eight months, at which 53 patients followed this invitation.
The reasons for the dropouts are shown in the owchart in the Supple-
mentary gure S1. The study was approved by the local ethics com-
mittee of the canton Bern (SNCTP000002289) and conducted according
to the declaration of helsinki.
2.2. Clinical assessment
At inclusion (T1), all participants completed the Beck’s Depression
Inventory (BDI (Beck et al., 1961)) and the State-Trait Anxiety Inventory
(STAI-S and STAI-T (Spielberger et al., 1983)) questionnaires. The State
form (STAI-S) assesses the intensity of anxiety experienced by the
participant during the test, in the recent past, or anticipates their feel-
ings in a hypothetical scenario whereas the Trait form (STAI-T) looks at
individual variations in anxiety predisposition and overall anxiety
levels, providing insight into a person’s enduring anxious tendencies
(Spielberger et al., 1983).
FND patients additionally underwent a clinical examination:
Severity of illness was assessed by the Clinical Global Impressions Scale
(CGI-I) from 1 to 7 (1 =normal, 7 =among the most ill patients (Busner
and Targum, 2007)). In addition, for motor FND symptoms we used the
Simplied Functional Movement Disorders Rating scale (S-FMDRS
(Nielsen et al., 2017)). At follow-up (T2), we repeated CGI-I and S-
FMDRS and additionally assessed the Clinical Global Improvement
Score (CGI-II) from 1 to 7 (1 =very much improved, 7 =very much
worse). Moreover, we assessed the type of therapy patients engaged in
during the four weeks before joining the follow-up measurement.
2.3. Image acquisition
MRI data were acquired using a 3 T scanner (Magnetom Prisma,
Siemens, Germany). To reduce head movement, the head was xed
using foam cushions. Functional data were acquired using a gradient-
echo planar imaging (EPI) sequence with the following parameters:
TR =1.3 s, TE =37 ms, ip angle =52◦, slice thickness =2.2 mm, REF
voxel size =2.2x2.2x2.2 mm, TA =6.39 min and 300 volumes. During
the acquisition of the resting-state fMRI, the participants were instructed
to lay as calm as possible, to stay awake, to xate on a cross shown on
the screen, and to not think of anything in particular. Structural data
were acquired using a T1-weighted MPRAGE sequence with the
following parameters: TR =2.33 s, TE =3.03 ms, ip angle =8◦, FoV
read =256 mm, 1 mm slice thickness and REF voxel size =1.0x1.0x1.0
mm, TA =5.27 min.
2.4. Resting-state preprocessing
Preprocessing was performed using Statistical Parametric Mapping
version 12 (SPM12; https://www.l.ion.ucl.ac.uk/spm/software/
spm12/). Functional data were corrected for b0-eld distortions. The
images were realigned and then co-registered to the anatomical image.
After this, data was linearly detrended and further denoised using white
matter and cerebrospinal uid signals as well as movement parameters
as regressors (including constant, linear, and quadratic trends, average
white matter/cerebrospinal uid time courses, translational and rota-
tional motion time courses upon realignment). Images were normalized
to a MNI (Montreal Neurological Institute) template, resampled to
3.0x3.0x3.0 mm and smoothed with a 6 mm FWHM Gaussian kernel.
Signals were ltered using a bandpass lter between 0.01 and 0.1 Hz.
Functional images were inspected for too high motion artefacts based on
Power’s framewise displacement (FD) criterion at a threshold of FD >
0.5 mm (Power et al., 2014).
2.5. Differences in BOLD signal variability (SD
BOLD
) between FND and
HC
As SD
BOLD
might represent an adjunctive measure of brain dysfunc-
tion in diverse neuropsychiatric disorders (Kebets et al., 2021; Li et al.,
2019; Wei et al., 2023; Zanella et al., 2022), we investigated alterations
in SD
BOLD
in FND patients compared to HC on a whole-brain level.
Timeseries were mean centred aiming to eliminate the effect of the
general activation of the voxels/regions in resting-state (Baracchini
et al., 2021). SD
BOLD
was determined as the voxel-wise standard devi-
ation of the temporal BOLD signal (SD
BOLD
) of each subject, resulting in
a 3-dimensional SD
BOLD
map per subject, as also previously described in
(Kebets et al., 2021).
To evaluate differences in the BOLD signal variability between FND
and HC a voxel-wise t-test was performed using age and gender as var-
iables of no interest. To correct for multiple comparisons, a family-wise
error correction (FWE, P <0.01) was applied at the cluster level. The
analysis was repeated using the BDI and STAI-S as additional covariates
of no-interest to correct for the effect of depression and anxiety, details
are shown in Supplementary Material.
2.6. Longitudinal analysis
In a secondary analysis, we investigated on a potential relationship
between clinical outcome and SD
BOLD
in those clusters that were found
signicantly different between patients and HC. Therefore, we rst
quantied the results on a network-level by overlaying the signicant
clusters with the YEO network atlas (Thomas Yeo et al., 2011). For the
two most overlapping networks, a region of interest (ROI) was selected
based on the AAL2-atlas (Rolls et al., 2015). The average SD
BOLD
of the
voxels in the selected AAL2-regions were extracted from the scans per-
formed at T1 and T2, but only including those voxels from the AAL mask
that overlapped with the initial clusters, creating a more granular ROI
for subsequent analyses.
As a metric for the evolution of the symptoms between T1 and T2, we
calculated the delta-score (Δ) in S-FMDRS and CGI-I by subtracting the
score at T1 from the score at T2.
First, we investigated the relationship between symptom severity
and SD
BOLD
. Using the imaging data and clinical scores from the initial
assessment and the follow-up we calculated the correlation between
ΔSD
BOLD
of the Δof symptom severity scores (ΔCGI-I, ΔS-FMDRS) using
Kendall correlation coefcient, as CGI-I and to some extent S-FMDRS,
are ordinal data.
Second, we built a general linear regression model (GLM) to predict
the evolution of the clinical scores at T2 (dependent variable) using the
SD
BOLD
values of the individual ROI’s at T1 as independent variable.
3. Results
3.1. Clinical and demographical data
We excluded one HC and ve patients due to excessive movement
during the fMRI (N =5), one patient due to an anatomical brain lesion
(N =1) and one patient due to current drug abuse (N =1), and one HC
did not complete the imaging acquisition, resulting in a sample size of 79
FND patients and 74 HC. There were no signicant differences regarding
the demographic data of FND patients and HC (see Table 1). FND pa-
tients reported signicantly higher scores in the BDI and STAI-S (all p-
values <0.001, Table 1).
As not all patients came back at follow-up, we checked for clinical
differences that could represent selection bias. FND patients undergoing
follow-up showed no difference in demographic and clinical data
compared to FND patients who only participated at inclusion (all p-
values <0.05, Supplementary Table S1). Four patients were excluded
for the fMRI analyses due to too high motion artefacts resulting in 49
patients included for the fMRI analysis. From those patients who showed
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NeuroImage: Clinical 43 (2024) 103625
4
up at the follow-up measurement, most of the patients engaged in one or
more kind of therapy (Table 2).
3.2. BOLD variability between patients and controls
Following the exclusion of subjects with too high motion artefacts,
the FND group still differed in terms of number of discarded volumes
from HC (FND 5.68 % versus HC 1.61 %, Z = − 5.1, P<0.001) while no
differences were found terms of total FD. Corrected for age and sex, the
differences in SD
BOLD
between FND and HC revealed six signicant
clusters where FND patients showed higher SD
BOLD
compared to HC
across brain regions including the insula, the hippocampus, the sup-
plementary motor area (SMA), the orbitofrontal cortex and the cere-
bellum (Fig. 1). The results survived correction for family-wise error on
cluster level. Characteristics of these clusters are shown in Supplemen-
tary Table S2. The mapping to the YEO network atlas showed, that the
voxels within these clusters were mostly overlapping with the somato-
motor network (39 %), the attention/salience networks (22 %), and the
limbic network (24 %). These results remained largely stable when
correcting for anxiety and depression (Supplementary Material,Fig. S2,
Table S4).
3.3. Evolution of symptom severity associated with BOLD variability
Patients did not differ between M0 and M8 in terms of number of
discarded volumes or total FD. To investigate the relationship between
BOLD variability and clinical outcomes in FND patients, we extracted
the SD
BOLD
from the main hubs of the three most predominant networks
found to be altered in patients compared to HC. For the somatomotor
network we selected the SMA, and for the attention networks the insula.
The correlation of the Δsymptom severity and ΔSD
BOLD
showed a
signicant negative correlation between ΔCGI-1 and the SMA. With the
CGI-1 ranging from one (no symptoms) to seven (among most extremely
ill patients), the ΔCGI-1 can range from −6 to +6 with a positive ΔCGI-1
meaning a worse general impression was reported at T2 compared to T1,
and a negative ΔCGI-1 meaning an improved general impression was
reported at T2 compared to T1. A positive ΔSD
BOLD
means a higher
SD
BOLD
at T2 compared to T1, and vice versa. Together this indicates
that an improvement of the symptom severity represented by a negative
ΔCGI-1 correlates with an increased SD
BOLD
in the SMA at T2 compared
to T1 represented by a positive ΔSD
BOLD
(Fig. 2). There were no sig-
nicant correlations with ΔS-FMDRS.
In the predictive GLM the SD
BOLD
in the left Insula could predict
ΔCGI-1 (ß=0.1, P=0.041). Thus, a higher SD
BOLD
at T1 was linked to a
positive ΔCGI-1 at T2 indicating a worsening of the symptoms. The were
no signicant predictions for the S-FMDRS. Results were controlled for
age and gender.
4. Discussion
In a cross-sectional as well as longitudinal design, our study aimed to
investigate the spatial patterns of BOLD signal variability in FND that
may be associated with the severity of symptoms and their potential role
as a biomarker for this complex condition. Compared to HC, FND pa-
tients presented higher BOLD signal variability in key brain networks,
including the somatomotor, salience, and limbic networks, covering
regions such as the insula, the hippocampus, and the SMA as well as the
orbitofrontal cortex. These ndings align with previous neuroimaging
research on FND emphasizing aberrant neural activity and disrupted
functional connectivity in static measurements (Drane et al., 2021;Perez
et al., 2021;Pick et al., 2019) adding a novel dynamic dimension to our
understanding of underlying mechanisms.
4.1. Critical interplay between somatomotor, salience and limbic
networks in FND
In FND, altered network connectivity between the somatomotor,
salience and limbic networks have frequently been identied and
brought in context with the symptomatology of FND suggesting inter-
related mechanisms. Functional alterations within these networks were
suggested to interfere with a proper preparation and execution of motor
functions (Cojan et al., 2009) often associated with emotional arousal
(Aybek et al., 2015), combined with an impaired integration of sensory
information (Huys et al., 2021; Pare´
es et al., 2012), thus depicting FND
as a large-scale brain network dysfunction (Perez et al., 2012).
The SMA is a key region of the somatomotor network and is involved
in motor planning and execution and has been implicated in FND
symptom generation (Aybek et al., 2015;Stone et al., 2007). Moreover,
the insula as an important hub of the salience network has been impli-
cated in various cognitive and affective processes, including inter-
oception (Haruki and Ogawa, 2021), emotion regulation (Gasquoine,
2014), and self-awareness (Modinos et al., 2009; Tisserand et al., 2023),
for which alterations within these cognitive processes have been pre-
viously reported in FND (Sojka et al., 2021, 2018). Moreover, our pre-
vious results derived from the same cohort showed altered insular co-
activation patterns with the somatomotor as well as default mode
network in FND, which was associated with duration of illness as well as
stress biomarkers (Weber et al., 2024). Abnormal emotion regulation in
FND was previously directly linked to motor outputs: As such, decreased
activity was found in the insula and motor regions in patients with
functional dystonia in an emotional face fMRI task (Espay et al., 2018b).
Further task-based fMRI studies showed that when asked to maintain a
grip force while pleasant and unpleasant images were shown, patients
showed an amplied force compared to HC towards unpleasant images,
together with increased activity in the hippocampus and the posterior
cingulate cortex (PCC) (Blakemore et al., 2016). The PCC and the hip-
pocampus are thought to be involved in self-reective behaviour
Table 1
Demographic and clinical characteristics between FND patients and healthy
controls.
Characteristic FND, N =79 HC, N =74 p-value
Age 36.94 (14.31) 33.03 (11.00) 0.17
Gender 0.96
Female 59 (75 %) 55 (74 %)
Male 20 (25 %) 19 (26 %)
Depression: BDI; mean (SD) 14.56 (10.25) 3.92 (4.41) <0.001
Anxiety: STAI 1; mean (SD) 37.42 (11.04) 31.36 (6.32) <0.001
SF36: General Health; mean (SD) 48.35 (21.36) 79.93 (13.81) <0.001
S-FMDRS; mean (SD) 7.82 (8.48) NA NA
CGI-1; mean (SD) 2.62 (1.59) NA NA
Duration; in months; mean (SD) 55.84 (69.30) NA NA
Abbreviations: BDI =Beck’s Depression Inventory; STAI =State-Trait Anxiety
Inventory, SF-36 =36-Item Short Form Survey, CGI =Clinical Global impres-
sion Score.
Table 2
Therapy type patients engaged in during four weeks before the follow-up.
Therapy Type
1
Physiotherapy 21 (45 %)
Psychotherapy 27 (57 %)
Occupational therapy 7 (15 %)
Other therapy 15 (32 %)
No therapy 11 (23 %)
Therapies total
0 11 (23 %)
1 10 (21 %)
2 21 (45 %)
3 4 (8.5 %)
4 1 (2.1 %)
1
Patients could have engaged in more than one type of therapy. Data from 47
patients on type of therapy was available.
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NeuroImage: Clinical 43 (2024) 103625
5
(Brewer et al., 2013), and their aberrant activity was attributed to
enhanced evaluation of visual stimuli as emotionally relevant. Similarly,
when recalling traumatic life events during fMRI, FND patients showed
amongst others increased activity of the SMA and the dorsolateral pre-
frontal cortex (dlPFC), together with decreased activity in the hippo-
campus (Aybek et al., 2014). Moreover, increased functional
connectivity between the SMA and the amygdala was identied (Aybek
et al., 2014). Particularly, the dlPFC together with the SMA are involved
in motor planning and selection of actions based on internal and external
cues and emotional states (Dixon et al., 2017; Hoffmann, 2013; Nachev
et al., 2008), while the hippocampus as a key node of the DMN plays an
important role in emotion-associated memory processing (Yang and
Wang, 2017). In summary, it was rstly shown that limbic inuence in
patients might modulate voluntary motor actions, suggesting a tight
interplay between limbic, salience and somatomotor network in FND
symptomatology.
To our knowledge this is the rst study investigating on BOLD signal
variability in patients with FND, which further supports and extends
previous ndings. In general, brain signal variability offers valuable
insights into brain activity unrelated to traditional measures of BOLD
activation measurements (Depue et al., 2010; Garrett et al., 2010).
While optimal brain function necessitates a certain level of variability,
excessively high variability may be detrimental to the efciency of in-
hibition of distractions and cognitive stability (Armbruster-Genç et al.,
2016). In particular, the existence of a connection between symptom
severity and brain variability has been demonstrated in other neuro-
psychiatric disorders. In ADHD increased symptom severity was related
to increased resting-state brain variability in the dorsal and ventral
medial prefrontal cortex (Nomi et al., 2018). Also in depression and
mania a correlation of the clinical scores of symptoms and brain vari-
ability in the DMN and somatomotor network was revealed (Martino
et al., 2016). In schizophrenia higher BOLD variability in the language-,
dorsal attention- and auditory networks and lower BOLD variability in
the DMN, executive control-, somatosensory- and visual networks
showed a positive correlation with the severity of positive and negative
symptoms (Wei et al., 2023). As such, the increased BOLD signal vari-
ability as found particularly in the somatomotor and salience networks
in FND patients might affect cognitive stability leading to over-reactivity
of neural circuits (Kebets et al., 2021) which might further destabilize a
proper planning of motor actions leading to functional neurological
symptoms.
In summary, this study not only supports previous ndings but also
adds another dimension to the study of brain functional alterations in
patients with FND. As such, it highlights the critical interplay between
somatomotor- and salience networks in patients with FND, suggesting
that higher BOLD signal variability in these regions/networks might
contribute to the pathophysiology of FND.
Fig. 1. (A) Differences in SD Bold FND >HC showing increased BOLD variability in the hippocampus, the insula and the supplementary motor area (SMA) (corrected
for age and gender) and (B) Pie charts illustrating the voxel-wise overlap within the 17 resting-state networks according to (Thomas Yeo et al., 2011). Abbreviations:
Cont =Executive control, Default =Default mode DorsAttn =Dorsal attention, Sal/VenAttn =Salience/Ventral attention, SomMot =somatomotor, TempPar =
Temporoparietal, VisCen =Central vision, VisPer =Peripheral Visual, SMA =Supplementary Motor Area.
Fig. 2. Correlation of ΔCGI and ΔSD
BOLD
in SMA. (A) Overlap of SMA with the
contrast of FND >HC (corrected for age and gender). (B) Correlation plot of
ΔCGI and ΔSD
BOLD
in SMA. The ΔCGI-1 ranges from −6 to +6 with a positive
ΔCGI-1 representing a worsening in symptom severity and a negative ΔCGI-1
representing an improvement of the symptom severity. IN COLOUR, 1.5-COL-
UMN FITTING.
A. Schneider et al.

NeuroImage: Clinical 43 (2024) 103625
6
4.2. BOLD signal variability in the supplementary motor area aligns with
clinical outcome
Further evidence arises from our longitudinal data. The results of our
correlation analysis revealed an increase in BOLD signal variability in
the SMA over time in subjects who had an improved clinical outcome
after eight months. In other words, a lower variability might contribute
to the manifestation of FND symptoms, while an increase in variability
in the SMA aligns with the clinical improvement in these patients. This
nding is contrary to previous studies in neuropsychiatric disorders
investigating BOLD signal variability in which a clinical improvement
was most commonly in line with a reduction of BOLD signal variability
(Kebets et al., 2021; Martino et al., 2016; Nomi et al., 2018; Wei et al.,
2023). However, our ndings of an improvement in symptom severity in
line with an increase in BOLD signal variability in the SMA corroborates
the previous notion that both excessive as well as insufcient BOLD
signal variability could detrimentally impact proper brain functioning
(Armbruster-Genç et al., 2016; Baracchini et al., 2021; Garrett et al.,
2018), thereby underscoring the signicance of maintaining a balanced
variability.
Moreover, we found that higher BOLD signal variability at baseline
(T1) in the left insula was associated with the evolution of clinical
symptoms, indicating that patients who in general show higher BOLD
signal variability in the insula were less likely to show an improved
outcome after eight months. The insula with its subregions serves as a
hub for integrating sensory, emotional, and cognitive information,
contributing to the regulation and understanding of emotions (Centanni
et al., 2021). Higher brain signal variability in the sensorimotor and
salience networks −including the insula −and lower variability in the
DMN (Kebets et al., 2021) were associated with a better emotion regu-
lation in ADHD disorder, bipolar disorder and borderline personality
disorder. Another study found that higher BOLD variability in the
sensorimotor and salience network could predict the use of reappraisal
strategy compared to the prediction of using emotion suppression by a
decreased variability in the salience network (Zanella et al., 2022). In
the context of FND, where impaired emotion regulation is recognized as
a key pathophysiological mechanism (Kr´
amsk´
a et al., 2020), the
heightened variability of the insula may signify a dysregulation of these
processes. This dysregulation could potentially contribute to the
persistence or worsening of symptoms over time.
Up to date, only a few studies investigated whether brain functional
alterations in FND patients are directly linked to the dynamic of func-
tional symptoms (state marker), how it parallels uctuations and/or
maintenance of symptoms and if these alternations can represent
prognostic factors. Using stepwise FC analyses on resting-state data of
FND patients, Diez and colleagues identied enhanced functional
propagation from primary motor areas to the amygdala, the insula, the
cingulate cortex, as well as the temporo-parietal junction. Moreover,
functional propagation proles of the insula and the amygdala corre-
lated with symptom severity and could predict clinical improvement
after a six-months follow-up (Diez et al., 2019). Closely aligning with
our results, a previous study using PET-imaging identied a hypo-
metabolism in the SMA in FND patients which disappeared after a three-
month follow-up in patients with an improved clinical outcome (Con-
ejero et al., 2022). Likewise, the metabolism in the anterior cingulate
cortex (ACC) at inclusion strongly correlated with clinical improvement
after three months (Conejero et al., 2022). The authors thus suggested
the existence of a metabolic state and prognostic marker for FND asso-
ciated with motor symptoms and recovery.
These ndings together with ours, represent a novel way to consider
neuroimaging as potentially useful in clinical settings for FND. This
search for biomarkers is needed not for diagnostic purposes as clinical
signs have been found reliable and diagnostic criteria are established but
because it is still difcult to predict which patient will have a favourable
outcome (Gelauff et al., 2019). Having a prognostic biomarker at disease
onset helping predict outcome may become useful when delivering
targeted treatment and triaging patients into care pathways (Finkelstein
et al., 2023), having a state marker of disease improvement may become
useful in research setting for clinical trials. Our results suggest that dy-
namic changes in BOLD variability in the SMA may be a state marker of
disease progression while the increased variability in the insula may
serve as a prognostic marker for clinical outcome. Both warrant further
replication in independent samples before they could be used in the
advancing eld of clinical research developing targeted intervention
(Conejero et al., 2022;Perez et al., 2021).
5. Limitations
It is important to note that our study has some limitations. First, as
this is the rst study looking at BOLD variability in FND patients it is
important to consider other reasons for a higher BOLD variability. FND
patients show a high comorbidity with depression and anxiety disorder
(Butler et al., 2021) which may inuence the results. However, when
including depression and anxiety as covariates of no-interest to our
model (see Supplementary Material), our results remained largely sta-
ble. In generalized anxiety disorder patients presented with a decreased
brain signal variability in widespread networks and regions such as the
visual-, sensorimotor-, and frontoparietal networks, the limbic system,
and the thalamus (Li et al., 2019). As these ndings are contrary to ours
it is unlikely that anxiety is a major confounder in our study. However,
the different direction of these results might represent another under-
lying mechanism. Moreover, it needs to be acknowledged that anxiety is
a common comorbidity in FND which might add another level of
complexity to the underlying pathophysiological mechanisms (Ludwig
et al., 2018). Despite the board-certied neurologists screened the pa-
tients for concomitant psychiatric disorders, no systematic psychiatric
evaluation was performed. Similarly, psychotropic medication was only
assessed at T1, thus the longitudinal results could not be corrected for
psychotropic medication intake. Second, while the here reported results
provide important information of potential state marker of disease
improvement, it must be acknowledged that the reported correlation
between change in symptom severity and change in SD
Bold
in the SMA is
weak (R=-0.24), albeit signicant. Moreover, as this represents a novel
approach in FND, these ndings necessarily must be replicated and
validated in other cohorts and potential subgroups. Third, cardiovas-
cular factors such as heart rate may also inuence the variability of
BOLD signal, together with neural factors they can explain the changes
of BOLD signal variability in ageing (Tsvetanov et al., 2021). As we had a
control group comparable in age and sex, these effects should be
negligible. As this method has not been explored in FND these ndings
need to be interpreted with caution and further studies using the same
approach are necessary to conrm our results. Fifth, there exits several
methods (Waschke et al., 2021) analysing brain signal variability, and
using a different approach could lead to different results. Likewise, a ROI
approach was applied for the longitudinal analyses. While including
only those voxels that overlapped in the ROI and the cluster, a more ne-
grained ROI or creating seeds based on peak differences in the group-
level analyses might provide different results. Lastly, the between-
group analysis (FND versus HC) baseline ndings were used to narrow
the search window for the longitudinal analyses. Despite of the whole-
brain analyses did not bear signicant results (Supplementary Mate-
rial), such an approach might cause that important ndings may not be
well accounted for. While our cohort represents patients with mixed
FND symptoms, stratifying patients into different symptom types or
different outcome groups could have informed the results differently.
6. Conclusion
In conclusion, our study provides evidence of altered BOLD signal
variability in specic brain networks in FND encompassing the soma-
tomotor, limbic and salience networks. These ndings add to previous
literature supporting the notion that FND might be depicted as a large-
A. Schneider et al.

NeuroImage: Clinical 43 (2024) 103625
7
scale brain network dysfunction in which salience and limbic networks
tightly interplay with somatomotor networks potentially affecting motor
planning and execution. Moreover, the SMA variability may serve as a
state marker, given that a change in BOLD signal variability corre-
sponded to a clinical improvement. Furthermore, the insula demon-
strates its potential as a prognostic marker, at which patients with higher
BOLD signal variability at inclusion (T1) were less likely to show a
clinical improvement. These nding contribute to the growing under-
standing of FND pathophysiology and highlight the potential for BOLD
signal variability as a promising method for further research.
7. Funding/support
This work was supported by the Swiss National Science Foundation
(SNF Grant PP00P3_176985 for SA) and the University Hospital Insel-
spital Bern, Switzerland.
CRediT authorship contribution statement
Ayla Schneider: Writing –review &editing, Writing –original draft,
Visualization, Methodology, Formal analysis, Conceptualization.
Samantha Weber: Writing –review &editing, Writing –original draft,
Validation, Supervision, Software, Project administration, Methodology,
Investigation, Formal analysis, Data curation, Conceptualization. Anna
Wyss: Data curation, Validation. Serafeim Loukas: Software, Method-
ology, Formal analysis. Selma Aybek: Writing –review &editing, Su-
pervision, Resources, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
We want to thank all patients and healthy controls for their partici-
pations. We thank the reviewers for their constructive feedback and
helpful comments.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.nicl.2024.103625.
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