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@ARTICLE{Hoheisel:1025092,
author = {Hoheisel, Linnea and Kambeitz-Ilankovic, Lana and Wenzel,
Julian and Haas, Shalaila S. and Antonucci, Linda A. and
Ruef, Anne and Penzel, Nora and Schultze-Lutter, Frauke and
Lichtenstein, Theresa and Rosen, Marlene and Dwyer, Dominic
B. and Salokangas, Raimo K. R. and Lencer, Rebekka and
Brambilla, Paolo and Borgwardt, Stephan and Wood, Stephen J.
and Upthegrove, Rachel and Bertolino, Alessandro and
Ruhrmann, Stephan and Meisenzahl, Eva and Koutsouleris,
Nikolaos and Fink, Gereon R. and Daun, Silvia and Kambeitz,
Joseph and Betz, Linda and Erkens, Anne and Gussmann, Eva
and Haas, Shalaila and Hasan, Alkomiet and Hoff, Claudius
and Khanyaree, Ifrah and Melo, Aylin and
Muckenhuber-Sternbauer, Susanna and Köhler, Janis and
Öztürk, Ömer and Penzel, Nora and Popovic, David and
Rangnick, Adrian and von Saldern, Sebastian and Sanfelici,
Rachele and Spangemacher, Moritz and Tupac, Ana and Urquijo,
Maria Fernanda and Weiske, Johanna and Wosgien, Antonia and
Blume, Karsten and Gebhardt, Dominika and Kaiser, Nathalie
and Milz, Ruth and Nikolaides, Alexandra and Seves, Mauro
and Vent, Silke and Wassen, Martina and Andreou, Christina
and Egloff, Laura and Harrisberger, Fabienne and Lenz,
Claudia and Leanza, Letizia and Mackintosh, Amatya and
Smieskova, Renata and Studerus, Erich and Walter, Anna and
Widmayer, Sonja and Day, Chris and Iqbal, Mariam and Pelton,
Mirabel and Mallikarjun, Pavan and Stainton, Alexandra and
Lin, Ashleigh and Denissoff, Alexander and Ellilä, Anu and
From, Tiina and Heinimaa, Markus and Ilonen, Tuula and Jalo,
Päivi and Laurikainen, Heikki and Luutonen, Antti and
Mäkela, Akseli and Paju, Janina and Pesonen, Henri and
Säilä, Reetta-Liina and Toivonen, Anna and Turtonen, Otto
and Solana, Ana Beatriz and Abraham, Manuela and Hehn,
Nicolas and Schirmer, Timo and Altamura, Carlo and Belleri,
Marika and Bottinelli, Francesca and Ferro, Adele and Re,
Marta and Monzani, Emiliano and Sberna, Maurizio and
D’Agostino, Armando and Del Fabro, Lorenzo and Perna,
Giampaolo and Nobile, Maria and Alciati, Alessandra and
Balestrieri, Matteo and Bonivento, Carolina and Cabras,
Giuseppe and Fabbro, Franco and Garzitto, Marco and Piccin,
Sara},
title = {{A}lterations of {F}unctional {C}onnectivity {D}ynamics in
{A}ffective and {P}sychotic {D}isorders},
journal = {Biological psychiatry / Cognitive neuroscience and
neuroimaging},
volume = {9},
number = {8},
issn = {2451-9022},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Inc.},
reportid = {FZJ-2024-02679},
pages = {765-776},
year = {2024},
note = {This work was funded by the Deutsche Forschungsgemeinschaft
(DFG, GermanResearch Foundation) Project-ID 431549029, SFB
1451, and 491111487.},
abstract = {Psychosis and depression patients exhibit widespread
neurobiological abnormalities. The analysis of dynamic
functional connectivity (dFC), allows for the detection of
changes in complex brain activity patterns, providing
insights into common and unique processes underlying these
disorders.MethodsIn the present study, we report the
analysis of dFC in a large patient sample including 127
clinical high-risk patients (CHR), 142 recent-onset
psychosis (ROP) patients, 134 recent-onset depression (ROD)
patients, and 256 healthy controls (HC). A sliding
window-based technique was used to calculate the
time-dependent FC in resting-state MRI data, followed by
clustering to reveal recurrent FC states in each diagnostic
group.ResultsWe identified five unique FC states, which
could be identified in all groups with high consistency
(rmean = 0.889, sd = 0.116). Analysis of dynamic parameters
of these states showed a characteristic increase in the
lifetime and frequency of a weakly-connected FC state in ROD
patients (p < 0.0005) compared to most other groups, and a
common increase in the lifetime of a FC state characterised
by high sensorimotor and cingulo-opercular connectivities in
all patient groups compared to the HC group (p < 0.0002).
Canonical correlation analysis revealed a mode which
exhibited significant correlations between dFC parameters
and clinical variables (r = 0.617, p < 0.0029), which was
associated with positive psychosis symptom severity and
several dFC parameters.ConclusionsOur findings indicate
diagnosis-specific alterations of dFC and underline the
potential of dynamic analysis to characterize disorders such
as depression, psychosis and clinical risk
states.IntroductionPsychotic and affective disorders are
both prevalent and highly disruptive to patients’ quality
of life, making them some of the most important contributors
to global disease burden [1]. Understanding the
pathophysiology underlying these disorders through
neuroimaging might facilitate the development of tools for
early diagnosis or the identification of novel interventions
[2,3]. The analysis of connectivity between brain regions,
particularly dynamic functional connectivity (dFC), has
proven an effective method of characterizing brain
alterations in health and disease [4,5]. Studying dFC
abnormalities in patients with psychiatric disorders could
reveal important information on brain changes associated
with psychiatric symptoms, and provide indications of their
mechanisms.The discovery of the behaviourally meaningful
network structure of brain functional connectivity at rest
[6,7] spurred numerous investigations of FC in patients with
a range of brain disorders, including psychosis and
depression [8,9]. Several more recent studies have also
examined changes in FC in patients at clinical high risk for
psychosis (CHR) [10, 11, 12], a prodromal stage that often
precedes a full psychotic disorder. This population is
particularly interesting because pathophysiological
processes can be investigated before potential effects of
treatment, hospitalisation and disability are consolidated
[13]. Since there is significant clinical overlap between
depression, psychosis and CHR patients [2,14], comparing
brain changes between these groups might provide insights
into diagnosis-specific disease processes.Studies of static,
or time-averaged, functional brain connectivity indicate
robust alterations in patients with depression [15,16] and
psychosis [17,18]. Aberrant connectivity patterns
particularly in the default mode (DMN), central executive
(CEN) and salience networks (SN) have been identified in
both affective and psychotic disorders, but the specific
patterns of abnormalities differ between diagnoses [19,20].
While psychosis patients exhibit reduced FC both within the
DMN and between the DMN and SN [21], studies show an
increase in these FCs, and a decrease in connectivity
between the DMN and CEN, in depression [16]. Communication
between cortical and subcortical areas is also disturbed in
both disorders, with alterations in FC commonly found
between subcortical structures such as the striatum and
areas in the prefrontal cortex [22,23]. The same brain
networks are likewise affected in CHR patients [24],
although a differentiation between psychotic, affective and
CHR-specific changes is lacking.However, analyses of static
FC are limited as they neglect the time-dependent
variability of brain network connectivity. These techniques
cannot uncover alterations in FC in patients with
psychiatric disorders that occur only in the temporal
domain. Since research suggests that dynamic properties of
FC change in depressive and psychotic disorders [25, 26, 27,
28], their examination might reveal symptom-related and
transdiagnostic brain abnormalities. One powerful approach
to detecting temporal alterations of brain connectivity is
based on computing FC within sliding windows. This allows
for the identification of recurrent FC states, which are
characterised by specific patterns of correlated activity
between brain regions or brain networks [29,30]. The
characteristics of such FC states are promising potential
biomarkers of psychotic and affective disorders, and reveal
information about changes in transient brain activity and
mechanisms that cannot be gained from static FC alone
[5,31].The analysis of dynamic connectivity has so far been
limited to studies with small sample size and provided
heterogenous findings [5,32,4,33]. Some initial findings
suggest an overall decrease in temporal variability in
depression [34], with patients spending longer in a
weakly-connected state [4]. In contrast, patients with
psychosis spend less time in states characterised by high
connectivity within and between sensory areas, and more time
in states with high connectivity within the DMN [25].
Moreover, other studies indicate that psychosis is
associated with temporal disconnectivity [35,36]. The
limited data on dynamic functional connectivity (dFC)
changes in CHR patients available indicates some overlap of
abnormalities with psychosis patients in the connectivity
pattern of a dominant FC state, but also variations specific
to the prodromal state [28]. It is still unclear, however,
to what extent those findings are related to psychotic
symptoms, rather than to a general burden of disease or
affective symptoms that both CHR and psychosis patients
commonly experience. Due to the wide variety of
methodologies employed in dFC analyses [29], comparing
results across studies remains challenging, which makes it
particularly important to contrast the CHR, psychotic and
affective patients with each other as well as with healthy
control participants.In the present work, we provide first
results from a large-scale neuroimaging study of CHR,
recent-onset psychosis patients (ROP), patients with
recent-onset depression (ROD) and healthy control
individuals (HC). We investigated dFC changes by combining
the sliding window method with a consensus clustering
approach to identify a set of FC states. We then compared
the dFC features of these states, specifically lifetimes,
frequencies and transition frequencies, across diagnostic
groups. We investigated their relationship with clinical
variables such as symptom severity, level of functioning,
and cognitive scores with the aim of identifying specific
and transdiagnostic alterations in dFC.},
cin = {INM-3},
ddc = {610},
cid = {I:(DE-Juel1)INM-3-20090406},
pnm = {5251 - Multilevel Brain Organization and Variability
(POF4-525) / DFG project 491111487 -
Open-Access-Publikationskosten / 2022 - 2024 /
Forschungszentrum Jülich (OAPKFZJ) (491111487) / DFG
project 431549029 - SFB 1451: Schlüsselmechanismen normaler
und krankheitsbedingt gestörter motorischer Kontrolle
(431549029)},
pid = {G:(DE-HGF)POF4-5251 / G:(GEPRIS)491111487 /
G:(GEPRIS)431549029},
typ = {PUB:(DE-HGF)16},
pubmed = {38461964},
UT = {WOS:001296529400001},
doi = {10.1016/j.bpsc.2024.02.013},
url = {https://juser.fz-juelich.de/record/1025092},
}
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