1
Subcortical Anatomy of the Default Mode Network: a functional and structural
connectivity study
Pedro Nascimento Alves
a,b,c,d*
, Chris Foulon
a,b,e
, Vyacheslav Karolis
a,b
, Danilo Bzdok
f,g,h,i
,
Daniel S. Margulies
a,b
, Emmanuelle Volle
a,b
, Michel Thiebaut de Schotten
a,b,j,k*
a
Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris France.
b
Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U
1127, CNRS UMR 7225, Paris, France.
c
Department of Neurosciences and Mental Health, Neurology, Hospital de Santa Maria,
CHLN, Lisbon, Portugal.
d
Language Research Laboratory, Faculty of Medicine, Universidade de Lisboa, Lisbon,
Portugal.
e
Computational Neuroimaging Laboratory, Department of Diagnostic Medicine, The
University of Texas at Austin Dell Medical School
f
INRIA, Parietal Team, Saclay, France
g
Neurospin, CEA, Gif-sur-Yvette, France
h
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen
University, Aachen, Germany
i
JARA-BRAIN, Jülich-Aachen Research Alliance, Germany
j
Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris,
France.
k
Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR
5293, CNRS, CEA University of Bordeaux, Bordeaux, France
* Corresponding authors pedronascimentoalves@gmail.com
and michel.thiebaut@gmail.com
Competing interests:
The authors declare that they have no competing interests
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Abstract
Most existing research into the default-mode network (DMN) has taken a corticocentric
approach. Despite the resemblance of the DMN with the unitary model of the limbic system,
the anatomy and contribution of subcortical structures to the network may be
underappreciated due to methods limitation. Here, we propose a new and more
comprehensive neuroanatomical model of the DMN including the basal forebrain and anterior
and mediodorsal thalamic nuclei and cholinergic nuclei. This has been achieved by
considering functional territories during interindividual brain alignment. Additionally,
tractography of diffusion-weighted imaging was employed to explore the structural
connectivity of the DMN and revealed that the thalamus and basal forebrain had high
importance in term of values of node degree and centrality in the network. The contribution of
these neurochemically diverse brain nuclei reconciles previous neuroimaging with
neuropathological findings in diseased brain and offers the potential for identifying a
conserved homologue of the DMN in other mammalian species.
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certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
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1. Introduction
For the first time in 1979, David Ingvar used Xenon clearance to investigate resting
wakefulness (Ingvar, 1979). When aligned by scalp and skull markers, the 11 brains examined
indicated an evident increase of the blood flow levels in the frontal lobe interpreted as a
surrogate for undirected, spontaneous, conscious mental activity. Later, Positron Emission
Tomography (PET) was used to map more systematically task-related activation in the brain,
often with resting wakefulness as a control task. The contrast between task-related and resting
wakefulness led to the description of deactivation (i.e. active at rest more than during the task)
in a set of regions including retrosplenial cortex, inferior parietal cortex, dorsolateral frontal
cortex inferior frontal cortex, left inferior temporal gyrus, medial frontal regions and
amygdala (Mazoyer et al., 2001; Shulman et al., 1997a) that quickly bore the name of default
mode network (DMN) (Raichle et al., 2001). In these studies, skull landmarks or structural
Magnetic Resonance Imaging (MRI) were used to align PET images in Talairach stereotaxic
or in Montreal Neurological Institute (MNI) templates (Mazoyer et al., 2001; Shulman et al.,
1997a). The advent of functional Magnetic Resonance Imaging (fMRI), particularly of
methods for analysing functional connectivity, led to the allocation of new structures to this
network, such as the hippocampal formation (Buckner et al., 2008; Greicius et al., 2004;
Vincent et al., 2006).
Today, the DMN has largely been a cortically-defined set of network nodes. Consisting of
distinct regions/nodes distributed across the ventromedial and lateral prefrontal,
posteromedial and inferior parietal, as well as lateral and medial temporal cortex, the DMN is
considered a backbone of cortical integration (Andrews-Hanna et al., 2010; Kernbach et al.,
2018; Lopez-Persem et al., 2018; Margulies et al., 2016). Its subcortical components are,
however, less well characterized. Studies of whole-brain network organization reveal
subregions of the cerebellum (Buckner et al., 2011; Stoodley and Schmahmann, 2009) and
striatum (Choi et al., 2012) that are functionally connected with the cortical regions of the
DMN. Seed-based functional connectivity studies further demonstrate additional DMN-
specific connectivity to several subcortical structures, including the amygdala (Bzdok et al.,
2012; Roy et al., 2009), striatum (Di Martino et al., 2008), thalamus (Fransson, 2005). These
studies are important as a cleaner characterization of the anatomy of the DMN is an essential
step towards understanding its functional role and its involvement in brain diseases.
Particularly, an increased activity characterises the regions that compose DMN during tasks
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involving autobiographical, episodic and semantic memory, mind wandering, perspective-
taking or future thinking (Shapira-Lichter et al., 2013; Shulman et al., 1997b; Bendetowicz et
al. 2018). Conversely, DMN regions show a decreased neural activity during attention-
demanding and externally-oriented tasks (Shulman et al., 1997b; Spreng et al., 2009). Finally,
altered connectivity in the DMN has been observed in a large variety of brain diseases,
including Alzheimer’s disease, Parkinson’s disease, schizophrenia, depression, temporal lobe
epilepsy, attention deficit and hyperactivity disorder, drug addiction, among others (Broyd et
al., 2009; Geng et al., 2017; Tessitore et al., 2012; Voets et al., 2012; Zhu et al., 2017). Hence,
while prior research provides first hints towards a broader definition of the DMN system,
further research is necessary to articulate the anatomical extent of specific subcortical
contributions, and to understand the independent contribution of these structures in DMN
function and pathologies.
Nevertheless, since the DMN has repeatedly been characterized as a cohesive functional
network (Buckner et al., 2008), an average of brain images relying exclusively on anatomical
references and landmarks may be suboptimal (Brett et al., 2002; Thiebaut de Schotten and
Shallice, 2017) whether the method employed is a surface-based or volume-based registration
(Brett et al., 2002; Despotovic et al., 2015). Small structures of the brain may be particularly
susceptible to this misalignment, especially when MRI lacks contrast. Besides morphology,
cytoarchitecture and function are poorly overlapping, especially in the DMN(Bzdok et al.,
2015; Eickhoff et al., 2016). Consequently, functional areas present in every subject may not
overlap after averaging all structurally aligned brain images in a group analysis (Braga and
Buckner, 2017; Brett et al., 2002). This biological misalignment can be particularly
problematic for revealing significant small regions of the DMN (figure 1). A better alignment
is also essential for the subcortical structures of the brain as their variability is still
considerable (Amunts et al., 2005, 1999; Croxson et al., 2017; Zaborszky et al., 2008).
Specifically, cytoarchitectonic studies have shown that only one-quarter of the volume of
cholinergic nuclei overlaps in at least half of the individuals studied (Zaborszky et al., 2008).
Similarly, structures such as mammillary bodies, nucleus basalis of Meynert, or anterior
thalamic nuclei, can vary in size, morphology and locations, and are particularly prone to
misalignment with the current methods of structural registration (Despotovic et al., 2015; Liu
et al., 2015; Möttönen et al., 2015; Tagliamonte et al., 2013). To address several of these
challenges, we propose to revisit the anatomical scaffold of the DMN using a coregistration
based on a functional alignment, rather than on exclusively structural landmarks. This
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certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
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approach has already been used to overcome the high interindividual variability of the
morphology of some areas of heteromodal association cortex and led to a more accurate
mapping of resting-state functional connectivity (Langs et al., 2015; Mueller et al., 2013).
We hypothesised that using a functional alignment will reveal structures of basal forebrain
and the Papez’s circuits, namely anterior and mediodorsal thalamic nuclei and mammillary
bodies, as constituent nodes of the DMN for several reasons. First, all these regions are highly
interconnected which suggest they belong to the same functional system (Yakovlev, 1948;
Yakovlev and Locke, 1961). Second, the current conceptualization of DMN anatomy
resembles the unitary model of the limbic system (Figure 1c) which, through the coordination
of its subregions, subserves the elaboration of emotion, memories and behaviour (Catani et
al., 2013; MacLean, 1952, 1949; Papez, 1937). Third, the basal forebrain comprises a group
of neurochemically diverse nuclei, involved in dopaminergic, cholinergic and serotoninergic
pathways, that are crucial in the pathophysiology of the aforementioned diseases that affect
the DMN connectivity. Finally, recent electrophysiological evidence has shown that in rats
the basal forebrain exhibits the same pattern of gamma oscillations than DMN and that it
influences the activity of anterior cingulate cortex (Nair et al., 2018).
In this study, we used a functional alignment of rs-fMRI-based individual DMN maps to build
a more comprehensive DMN model. To provide a complete window into the anatomy of the
DMN, we explored the structural connectivity of our new model of the DMN using
tractography imaging techniques. We combined the tractography results with graph measures
to corroborate the essential contribution of the new regions reported to the DMN.
2. Material and Methods
2.1. Subjects and MRI acquisition
MRI images of subjects without neurological or psychiatric disease were obtained (age
mean±SD 29±6 years, range 22-42 years; 11 female, 9 male) with a Siemens 3 Tesla Prisma
system equipped with a 64-channel head coil.
An axial 3D T1-weighted imaging dataset covering the whole head was acquired for each
participant (286 slices, voxel resolution = 0.7 mm3, echo time (TE) = 2.17 ms, repetition time
(TR) = 2400 ms, flip angle = 9°).
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