Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

Abstract

In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.

Full text

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MINI REVIEW
published: 20 June 2019
doi: 10.3389/fnins.2019.00657
Edited by:
Roberto Esposito,
A.O. Ospedali Riuniti Marche Nord,
Italy
Reviewed by:
Abraham Z. Snyder,
Washington University in St. Louis,
United States
Darya Chyzhyk,
Inria Saclay - Île-de-France, France
*Correspondence:
Massimo Filippi
filippi.massimo@hsr.it
Specialty section:
This article was submitted to
Brain Imaging Methods,
a section of the journal
Frontiers in Neuroscience
Received: 05 April 2019
Accepted: 07 June 2019
Published: 20 June 2019
Citation:
Filippi M, Spinelli EG, Cividini C
and Agosta F (2019) Resting State
Dynamic Functional Connectivity
in Neurodegenerative Conditions:
A Review of Magnetic Resonance
Imaging Findings.
Front. Neurosci. 13:657.
doi: 10.3389/fnins.2019.00657
Resting State Dynamic Functional
Connectivity in Neurodegenerative
Conditions: A Review of Magnetic
Resonance Imaging Findings
Massimo Filippi1,2,3*, Edoardo G. Spinelli1, Camilla Cividini1and Federica Agosta1,3
1Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific
Institute, Milan, Italy, 2Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy, 3Vita-Salute San Raffaele
University, Milan, Italy
In the last few decades, brain functional connectivity (FC) has been extensively assessed
using resting-state functional magnetic resonance imaging (RS-fMRI), which is able
to identify temporally correlated brain regions known as RS functional networks.
Fundamental insights into the pathophysiology of several neurodegenerative conditions
have been provided by studies in this field. However, most of these studies are based
on the assumption of temporal stationarity of RS functional networks, despite recent
evidence suggests that the spatial patterns of RS networks may change periodically over
the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity
(dFC) analysis has been recently implemented and proposed in order to consider the
temporal fluctuations of FC. These approaches hold promise to provide fundamental
information for the identification of pathophysiological and diagnostic markers in the vast
field of neurodegenerative diseases. This review summarizes the main currently available
approaches for dFC analysis and reports their recent applications for the assessment
of the most common neurodegenerative conditions, including Alzheimer’s disease,
Parkinson’s disease, dementia with Lewy bodies, and frontotemporal dementia. Critical
state-of-the-art findings, limitations, and future perspectives regarding the analysis of
dFC in these diseases are provided from both a clinical and a technical point of view.
Keywords: dynamic functional connectivity, fMRI, neurodegeneration, dementia, Alzheimer’s disease,
Parkinson’s disease, Lewy bodies, frontotemporal dementia
INTRODUCTION: FROM STATIC TO DYNAMIC FUNCTIONAL
CONNECTIVITY
Neurodegenerative diseases are characterized by a progressive loss of neurons associated
with deposition of aberrant proteins, leading to alterations of the structural and functional
properties of the brain (Kovacs, 2017). In the last decades, the non-invasive resting-
state (RS) functional magnetic resonance imaging (RS-fMRI) technique has been widely
applied in these clinical populations, to investigate more in depth the spatial topology and
strength of interactions between brain networks (Smitha et al., 2017;Hohenfeld et al., 2018).
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Functional magnetic resonance imaging uses the blood-
oxygenation-level dependent (BOLD) signal, which is sensitive
to spontaneous neural activity. In particular, low-frequency
oscillations (<0.1 Hz) of the BOLD signal are analyzed to
obtain functional information of brain networks. Functional
connectivity (FC) quantifies the temporal correlation of
functional activation in different brain regions and can be
expressed in terms of pairwise Pearson’s correlation coefficients,
covariance, or mutual information between time series, revealing
specific networks (Smitha et al., 2017). FC has been recognized
as an important biomarker for better understanding the
pathophysiological mechanisms of numerous neurodegenerative
diseases, including Alzheimer’s disease (AD) (Filippi et al., 2017),
Parkinson’s disease (PD) (Baggio et al., 2015;Filippi et al., 2019),
and frontotemporal dementia (FTD) (Filippi et al., 2017).
So far, the implicit hypothesis on which FC analysis has
been based is the assumption of temporal stationarity of the
functional interaction between connections. Considering the
dynamic nature of brain activity, a novel approach is provided
by dynamic functional connectivity (dFC), which considers
the temporal fluctuations of functional connections in faster
timescales (Hutchison et al., 2013). Unlike conventional static
FC, which is obtained from the correlation within an entire time
series, dFC refers to the brain activity within sub-portions of
time series (Menon and Krishnamurthy, 2019). Major efforts have
been made to identify and analyze time-varying, but recurring,
FC sub-patterns of coupling among brain regions, constituting
the brain “chronnectome” (Calhoun et al., 2014).
The aim of this review is to describe the contribution of
dFC studies in RS conditions for a better understanding of
neurodegenerative diseases. We are going to focus on the most
common approaches to analyze dFC and review recent findings
in this field concerning AD, PD and other parkinsonisms, and
FTD. We conclude this work summarizing caveats, limitations
and future perspectives regarding dFC analysis.
METHODOLOGICAL OVERVIEW
Several computational strategies have been implemented to
characterize temporal and spatial variations of BOLD signal (Wee
et al., 2016;Jie et al., 2018;Liu et al., 2018). The most common
approach is provided by the sliding-window technique (Chen
et al., 2016, 2017;de Vos et al., 2018;Diez-Cirarda et al., 2018),
characterized by the selection of a time window – shorter than
the whole-scan time – whose data points are used to calculate FC
metrics. The window is shifted in time by a fixed number of data
points, referred as step, which defines the overlap between two
successive windows. The step duration ranges from one single
data point to the length of the window (i.e., non-overlapping
windows) (Jie et al., 2018;Park et al., 2018).
In combination with the sliding-window approach, several
studies have applied clustering methods to identify reproducible,
transient patterns and to evaluate the commonly used graph
metrics, the dwell time, defined as the number of consecutive
windows in a specific state, and the number of transitions
between states (Allen et al., 2014).
Since a subject could be in more than one state at a given
point, the concept of “meta-states” and meta-state measures has
been introduced to intuitively characterize the dynamic fluidity
in FC (Miller et al., 2016;Premi et al., 2019). Meta-state measures
include the number of occupied meta-states, number of switches
between meta-states, greatest distance between two meta-states
and overall distance (Premi et al., 2019). Furthermore, the
sliding-window approach can be integrated with the application
of independent component analysis (ICA) to identify spatial
maps in the windowed BOLD signal and assess variability or
graph theoretical metrics (Jones et al., 2012;de Vos et al.,
2018;Premi et al., 2019). The sliding-window approach can
also be used jointly with classification algorithms to exploit the
information resulting from patterns of dFC (Chen et al., 2016;
Guo et al., 2017;Figure 1).
Alternative approaches to evaluate dFC are represented by
time-frequency analysis, dynamic connectivity regression (DCR)
and dynamic connectivity detection (DCD), which are data-
driven techniques for detecting FC change points within RS fMRI
time series, and derive dynamic information from such points
(Xu and Lindquist, 2015).
ALZHEIMER’S DISEASE
Alzheimer’s disease is the most common neurodegenerative
cause of dementia (Alzheimer’s, 2016) and has been extensively
studied by means of advanced MRI techniques. The inclusion
of RS fMRI into imaging protocols in AD has been particularly
advantageous, as the difficulty to obtain subjects’ cooperation
could influence task-related fMRI results. Conspicuous evidence
has shown decreased FC of the default mode network (DMN)
across the AD continuum, including patients with full-blown
AD dementia and amnestic mild cognitive impairment (MCI)
(Greicius et al., 2004;Bai et al., 2008;Agosta et al., 2012;Koch
et al., 2012). Decreased connectivity within the DMN – consisting
of the posterior cingulate, inferior parietal, inferolateral temporal,
anterior cingulate, prefrontal, and hippocampal regions – is often
accompanied by increased connectivity in the attentional fronto-
parietal and salience networks, likely mirroring compensatory
mechanisms (Agosta et al., 2012;Badhwar et al., 2017).
Disconnection between posterior (i.e., posterior cingulate and
parietal regions) and anterior DMN nodes (i.e., anterior
cingulate and prefrontal regions) was found to cause relative
decreased connectivity within the posterior DMN and increased
connectivity within the anterior DMN (Jones et al., 2011).
Functional rearrangements have demonstrated clinical usefulness
for predicting conversion to AD in MCI patients (Bai et al., 2011;
Petrella et al., 2011;Li et al., 2016).
Given the high consistency of these findings across static
FC studies, AD represents a good candidate to apply dFC
approaches to the field of neurodegenerative disorders, since
capturing the evolving architecture of brain networks over
short periods of time might provide further pathophysiological
insights into these conditions, and eventually better diagnostic
or prognostic indicators. The first study investigating dFC of
AD patients examined changes over time of a modularity
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FIGURE 1 | Framework for construction of high-order functional connectivity (FC) network. (1) Partition of the RS-fMRI time series into multiple overlapping
segments of subseries applying sliding-window technique; (2) collection of low-order FC matrices, one for each subseries; (3) stack of all matrices of all subjects
together to obtain correlation time series for each element; (4) application of the clustering algorithm to group all the correlation time series; (5) construction of
high-order FC network, considering the mean correlation time series for each cluster as vertex and the pairwise Pearson’s correlation coefficient between each pair
of vertices as weight; (6) calculation of local clustering coefficients; (7) selection of a discriminative feature subset from the local clustering coefficients;
(8) implementation of support vector machine (SVM) model for classification. RS-fMRI, resting-state functional magnetic resonance imaging; FC, functional
connectivity (reproduced with permission from Chen et al., 2016).
metric using a sliding-window analysis (Jones et al., 2012). The
non-stationary nature of the brain modular organization was
demonstrated and related with significant variations of the dwell
time within different sub-network configurations of the DMN
in subjects with AD dementia compared with healthy controls;
specifically, AD patients spent less time in brain functional
states with strong posterior DMN region contribution and more
time in states with greater anterior DMN region contribution
(Jones et al., 2012). A subsequent study investigated the
evolution of dFC disruptions across the AD spectrum, showing
alterations in patients with dementia compared to MCI and
subjective cognitive decline (SCD) in terms of local dFC within
the temporal, frontal-superior and default-mode networks;
decreased global metastability between functional states was also
found, supporting the hypothesis that oscillatory patterns are
progressively altered over the AD continuum, eventually leading
to a shrinkage of the “dynamic repertoire” (i.e., a smaller set of
functional configurations) in the brain of AD patients (Cordova-
Palomera et al., 2017). Consistently, another study showed a
progressive loss of whole-brain metastability according to the
severity of cognitive impairment along the AD continuum,
reaching statistical significance only in patients with dementia,
when compared with healthy controls (Demirtas et al., 2017).
Researchers have also aimed to identify dFC alterations that
may represent candidate non-invasive diagnostic biomarkers
in the early stages of AD. A sparse temporal network-based
framework has been tested for the classification of patients
with early MCI by means of support vector machine (SVM)
algorithms, yielding an accuracy of approximately 80% in the
discrimination from healthy controls, compared with accuracies
ranging 62–72% using FC static approaches (Wee et al., 2016).
Another recent study aimed to integrate both temporal and
spatial properties of dFC networks for the classification of
early and late MCI patients (Jie et al., 2018). Accuracies of
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FIGURE 2 | Functional connectivity state results. (A) Group-specific cluster centroid for each state, averaged across subject-specific median cluster centroids of
each group [percentage of total occurrences for stage I and II: 83.4% and 16.6% in the healthy controls (HC) and 70.8% and 29.2% in the Parkinson’s disease (PD)
group, respectively]. (B) Functional connectivity in each state is shown for healthy controls and Parkinson’s disease groups, representing the 5% of the functional
connectivity network with the strongest connections. BG, basal ganglia; AUD, auditory; SMN, sensorimotor; VIS, visual; CEN, cognitive executive; CB, cerebellar
network (reproduced with permission from Kim et al., 2017).
approximately 78% were obtained when SVM algorithms were
trained based on the dFC patterns of components of the DMN
and temporal and cerebellar regions (Jie et al., 2018). An
attempt to combine multiple dFC parameters for an automated
classification of early MCI patients was recently proposed by
applying a tensor model of spatio-temporal BOLD signal to
each voxel of the white matter (WM), to be integrated with the
information provided by the FC of grey matter (GM) regions
(Chen et al., 2017). Such combined GM and WM approach
yielded an accuracy of almost 79% in discriminating early MCI
patients from healthy subjects, compared with 74% provided by
GM dFC measures alone (Chen et al., 2017). Another promising
approach is the clustering of standard-correlation time series
for all pairs of brain regions (i.e., the classic “low-order” FC
networks) into a smaller set of “high-order” dFC networks
according to their intrinsic common patterns. The combination
of high-order dFC with the conventional low-order analysis
allowed SVM-based discrimination of early MCI patients from
healthy subjects with an accuracy of 88%, outperforming other
previous approaches (Chen et al., 2016;Figure 1).
PARKINSON’S DISEASE
Parkinson’s disease (PD) is the second most common
neurodegenerative disorder and is characterized by dopamine
depletion in the nigro-striatal system leading to progressive
functional impairment (Poewe et al., 2017). Widespread
functional rearrangements related to the development of motor
and non-motor symptoms occur over the clinical progression in
PD patients (Filippi et al., 2019). Several RS fMRI studies have
identified alterations of the cerebello-thalamo-cortical circuit as
a key hallmark of PD (Helmich et al., 2010;Hacker et al., 2012;
Agosta et al., 2014;Akram et al., 2017), with reduced activation
of the posterior putamen correlating with motor impairment
as the most consistent finding (Herz et al., 2014). Furthermore,
disrupted FC in the DMN, fronto-parietal, salience and
associative visual networks has been linked to the development
of cognitive deficits in PD (Amboni et al., 2015;Baggio et al.,
2015;Putcha et al., 2015;Zhan et al., 2018). Particularly, normal
decoupling between the DMN and fronto-parietal networks
was reduced in PD patients with MCI (PD-MCI) (Baggio et al.,
2015;Putcha et al., 2015), and FC alterations within the DMN
were able to predict subsequent cognitive decline in cognitively
unimpaired PD patients (van Eimeren et al., 2009;Tessitore
et al., 2012;Putcha et al., 2015).
The first study assessing the dynamic functional properties
of RS networks in PD patients identified two main FC
configurations: a more frequent and strongly segregated state
(defined as “State I”) and a less frequent, more integrated
“State II” (Kim et al., 2017). Compared with healthy subjects,
PD patients showed a significant decrease of dwell time in
State I, with a proportional increase of dwell time in State
II that was correlated with the severity of motor symptoms,
indicating that the loss of functional segregation between brain
networks might represent a key element in PD pathogenesis
(Kim et al., 2017;Figure 2). To eliminate the possible influence
of long-term dopaminergic therapy, dFC alterations were also
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assessed in early stage, drug-naïve PD patients, who showed
decreased switching rate between dynamic states correlating with
disease severity, supporting the view that a limited dynamic
range of whole-brain FC might represent an early PD marker
(Cordes et al., 2018;Zhuang et al., 2018). Another recent study
focused on the spatial configuration of dFC alterations within
two homogeneous subunits of the putamen of PD subjects,
demonstrating a degradation of subregional specificity between
the anterior and posterior putaminal subunits that was related
with disease severity (Liu et al., 2018).
Along with motor impairment, dFC has also provided
significant information regarding the underpinnings of cognitive
symptoms in PD. Decreased dwell time in a more segregated
state and increased state transitions have been demonstrated in
PD-MCI patients compared with healthy controls, configuring
a pattern that PD patients with normal cognition lacked (Diez-
Cirarda et al., 2018). Increased dFC within the dorsal-attention
network was found to predict attention performance in PD
patients (Madhyastha et al., 2015), whereas a positive correlation
between dFC of the DMN and performance on a visuospatial
memory task has also been recently reported (Engels et al., 2018).
OTHER NEURODEGENERATIVE
CONDITIONS
To date, most dFC studies assessing patients with
neurodegenerative conditions have focused on the two most
common diseases, i.e., AD and PD. Considering also the novelty
of these approaches, evidence regarding other pathological
entities is currently scarce, but in rapid development.
Dementia with Lewy bodies (DLB) is among the most
common causes of dementia after AD, and is characterized by
cognitive fluctuations, parkinsonism, and visual hallucinations
(McKeith et al., 2017). Classic RS-fMRI static studies have
shown FC reductions in widespread brain networks in DLB
subjects, with desynchronization of cortical and subcortical
areas within the attention-executive networks correlating with
cognitive fluctuations (Lowther et al., 2014;Peraza et al., 2014).
Considering the transient nature of some of the main features
of DLB (i.e., cognitive fluctuations and hallucinations), dFC
studies are expected to provide fundamental insights into the
pathophysiology of this disease. Indeed, dFC has demonstrated
significant differences in DLB patients compared with healthy
subjects in visual (i.e., the occipito-parieto-frontal and medial
occipital networks) and attentional networks (i.e., the right
fronto-parietal control network), which also showed decreased
mutual dependency, suggesting that temporal disconnection
between these networks might be relevant for DLB pathogenesis
(Sourty et al., 2016).
Frontotemporal dementia is another frequent
neurodegenerative condition encompassing a wide range of
clinical presentations, including behavioral, executive, language
and motor deficits (Olney et al., 2017). To our knowledge,
no study has assessed dFC alterations in patients with FTD.
However, a recent work focused on presymptomatic carriers
of FTD-causing mutations (Premi et al., 2019), being FTD an
inherited autosomal disorder in 30–40% of cases (Rohrer and
Warren, 2011). Mutation carriers showed lower number of
meta-states, decreased switching rate between meta-states and
shorter meta-state total distance compared with healthy controls,
demonstrating that a reduced dynamic fluidity and restricted
dynamic range in brain functional “chronnectome” is an early
event in the development of FTD (Premi et al., 2019). The
assessment of such alterations in the phase which is closest to
clinical conversion might be a promising research field for the
development of biomarkers to be used in clinical trials of FTD.
CAVEATS, LIMITATIONS AND FUTURE
DIRECTIONS
Based on the evidence here reviewed, dFC studies have shed
new light on the pathophysiological alterations underlying the
most common neurodegenerative diseases. However, important
concerns remain regarding the possible influence of vigilance
fluctuations during the fMRI scan. Although patients are
routinely instructed to stay awake for the whole scan duration,
sleep disturbances are frequent clinical features of dementias
and parkinsonian syndromes (Malhotra, 2018), and fluctuating
alertness will affect FC (Tagliazucchi et al., 2012;Haimovici
et al., 2017). This important issue would be overcome
by simultaneous EEG-fMRI acquisition (Tagliazucchi and
Laufs, 2014;Allen et al., 2018), although this approach
is technically challenging and has not been explored in
neurodegenerative conditions yet.
Some technical caveats also need to be considered. The sliding-
window technique has been repeatedly applied because of its
analytical simplicity and easy implementation: in most studies,
the number of BOLD signal subseries K is decided based on the
window length W, the number of temporal image volumes N
and the step, according to the formulation K = [(N – W) / s]
+1 (Chen et al., 2016;Wee et al., 2016;Guo et al., 2017).
However, the window length, as well as the step parameter,
are matter of debate: choosing a short window could increase
the risk of misleading spurious fluctuations, while choosing a
long window could fail to identify state transitions (Preti et al.,
2017). A trade-off must be reached: at present, different studies
suggested a window length of 30–60 s as sufficient for detecting
dFC changes (Jones et al., 2012;Diez-Cirarda et al., 2018;Premi
et al., 2019), even though some studies opted for longer windows
(Chen et al., 2016;Wee et al., 2016). An alternative way to
find the optimum window length is represented by the time-
frequency analysis (Cordes et al., 2018). The step parameter is
also chosen arbitrarily, commonly ranging one to two volumes
for overlapping windows (Quevenco et al., 2017;Liu et al., 2018),
although a few studies adopted non-overlapping windows (Jie
et al., 2018;Park et al., 2018). Another crucial point is the
choice of window shape: rectangular, modulated rectangular or
tapered windows are the most used (Diez-Cirarda et al., 2018;
Premi et al., 2019).
Beyond these methodological aspects, there are still
some unsolved questions regarding dFC. Neurobiological
underpinnings and mechanisms of dynamic states have to be
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clarified (Smith et al., 2011), as the possibility that reported
changes may be driven by signal noise or sampling variability
needs to be considered (Laumann et al., 2017). Indeed, the
fluctuations in the sliding-window correlation time series can
be associated to dFC or simply generated by random noise,
so more complex statistical models are required to deal with
this issue (Hindriks et al., 2016). The artifact problem that
applies to conventional resting-state fMRI is also a crucial
aspect for dFC analysis (Nalci et al., 2019), as the BOLD signal
is sensitive to non-stationary physiological processes, such as
head motion (Power et al., 2014) and blood partial pressure
of carbon dioxide (pCO2) due to respiration (Power et al.,
2018). Moreover, important factors to take into account for
a correct interpretation of dFC results are the selection of
the a priori atlas or ICA algorithm used to obtain regions of
interest and the assessment of specific FC metrics. Finally, the
reliability and reproducibility of dFC patterns are still a challenge,
although some efforts have been made on solving this issue
(Abrol et al., 2017).
CONCLUSION
Assessing dFC is a promising way to better understand
neurodegenerative processes and investigate novel disease
diagnostic and prognostic biomarkers. However, future
developments are needed to rule out the influence of vigilance
fluctuations, overcome the limitations of the sliding-window
approach – possibly using other methods as time-frequency
analysis –, identify the most informative dFC metrics, and
minimize artifacts by means of adequate preprocessing, so as
to be more confident in the description and interpretation
of these findings.
AUTHOR CONTRIBUTIONS
MF contributed to the study concept and acted as study
supervisor. All authors contributed to writing, reading, and
approving the final version of the manuscript.
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Conflict of Interest Statement: MF is Editor-in-Chief of the Journal of Neurology;
has received compensation for consulting services and/or speaking activities
from Biogen Idec, Merck-Serono, Novartis, Teva Pharmaceutical Industries; and
has received research support from Biogen Idec, Merck-Serono, Novartis, Teva
Pharmaceutical Industries, Roche, Italian Ministry of Health, Fondazione Italiana
Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). FA
is Section Editor of NeuroImage: Clinical; has received speaker honoraria from
Biogen Idec and Novartis; and receives or has received research supports from the
Italian Ministry of Health, AriSLA (Fondazione Italiana di Ricerca per la SLA),
and the European Research Council.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2019 Filippi, Spinelli, Cividini and Agosta. This is an open-access article
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original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academicpractice. No
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