Subcortical Anatomy of the Default Mode Network: a functional and structural connectivity study

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.


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 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, 1952MacLean, , 1949Papez, 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.

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.
A diffusion-weighted imaging (DWI) acquisition sequence, fully optimised for tractography, provided isotropic (1.7 × 1.7 × 1.7 mm) resolution and coverage of the whole head with a posterior-anterior phase of acquisition, with an echo time (TE) = 75 msec. A repetition time (TR) equivalent to 3500ms was used. At each slice location, six images were acquired with no diffusion gradient applied (b-value of 0 sec mm−2). Additionally, 60 diffusion-weighted images were acquired, in which gradient directions were uniformly distributed on the hemisphere with electrostatic repulsion. The diffusion weighting was equal to a b-value of 2000 s/mm−2. This sequence was fully repeated with reversed phase-encode blips. This provides us with two datasets with distortions going in opposite directions. From these pairs, the susceptibility-induced off-resonance field was estimated using a method similar to that described in (Andersson et al., 2003) and corrected on the whole diffusion-weighted dataset using the tool TOPUP and EDDY as implemented in FSL (Smith et al., 2004).

Individual DMN maps in the structural space
Individual subject-tailored/fitted DMN maps were obtained by correlation with seed regions of interest of a functional parcellated brain template. The regions used for the seed-based functional connectivity analysis were those defined as DMN regions in the resting-state parcellation map by Gordon and collaborators (Gordon et al., 2016). Gordon and collaborators created these parcellations according to abrupt changes in resting state's time course profile, each parcel having a homogenous time course profile. This general-purpose atlas provides a total of 40 DMN nodes (20 in each hemisphere), which were used as seeds for building the correlation maps (http://toolkit.bcblab.com; Foulon et al., 2018). This produced a set of 40 maps per subjects.
The individual DMN map was obtained by calculating the median of the 40 seed-based correlation maps of each individual using FSL (Jenkinson et al., 2012).
To obtain a group DMN map in the structural space, the median of the twenty individual maps was derived.

Individual DMN maps in the "functional space"
To achieve the proposed optimized map of the DMN, the same twenty individual maps were functionally aligned with each other in a new "functional space" using the following steps: Individual DMN maps (obtained in 2.2.1.) were aligned with each other using ANTs' script buildtemplateparallel.sh, defining cross-correlation as the similarity measure and greedy SyN as the transformation model (Avants et al., 2011(Avants et al., , 2008Klein et al., 2009). This approach consists in an iterative (n=4) diffeomorphic transformation to a common space. The group map was obtained by calculating the median of all DMN maps after functional alignment.
The resulting maps correspond to alignment of the 20 individual DMN maps in a "functional space" (figure 2).

Functional connectivity comparison of the two DMN
For the functional and structural-based DMN maps, time-series of rs-fMRI of each individual in the different regions of interest identified in the DMN maps were extracted (Jenkinson et al., 2012). Correlation and partial correlation coefficients were calculated as measures of functional connectivity.
Two matrices, representing the median correlation values in the MNI152 space and in the functional space, were created using BrainNet Viewer (Xia et al., 2013 (Desikan et al., 2006;Edlow et al., 2012;Lancaster et al., 2007;Talairach and Tournoux, 1988;Zaborszky et al., 2008). A percentage of volume overlap between the DMN map and each nucleus of interest was subsequently calculated for each subject.
Whole-brain tractography was performed on the software StarTrack using a deterministic approach (https://www.mr-startrack.com). A damped Richardson-Lucy algorithm was applied for spherical deconvolutions (Dell'Acqua et al., 2010). A fixed fibre response corresponding to a shape factor of α = 1.5x10-3 mm2/s was adopted. The defined number of iterations was 150 and the geometric damping parameter was 8. The absolute threshold was defined as 3 times the spherical fibre orientation distribution (FOD) of a grey matter isotropic voxel and the relative threshold as 8% of the maximum amplitude of the FOD (Thiebaut de Schotten    Regarding the hypothesized areas, basal forebrain had stronger partial correlations with midbrain, ventromedial prefrontal cortex, amygdala and temporal pole, while thalamus had higher partial correlations with caudate nucleus and dorsal prefrontal cortex (figure 3b).
3.3 Anatomical validation in thalamic, basal forebrain and mesencephalic areas Figure 4 illustrates the intersection of the new DMN map after its translation to the MNI group space using individual inverse transformation matrices specific for each individual. All subjects' DMN spatially overlapped with the templates of the left anterior thalamic nucleus, mediodorsal thalamic nuclei, medial septal nuclei and left nucleus accumbens (Table 3) (Desikan et al., 2006;Edlow et al., 2012;Lancaster et al., 2007;Talairach and Tournoux, 1988;Zaborszky et al., 2008). The number of subjects with an intersection with the right anterior thalamic nucleus, right nucleus accumbens and ventral tegmental area was also very high (95%, 95% and 90% respectively), while the intersection with the other basal forebrain nuclei occurred in approximately half of the subjects, possibly due to their very small size.   , table 1). Hence, thalamus and basal forebrain make part of a high fraction of shortest paths in the network, that is, the shortest connections between two nodes.

Discussion
In this study, we revisited the constituent elements of the default mode network ( The difference between alignment in the functional space and the structural space was characterised by an increase of the connectivity strength across the brain as well as in many subcortical areas classically not considered to be constituent nodes of the DMN. As previously reported, this confirmed that registration in the functional space provides a more accurate interindividual anatomical description and is recommended when doing functional connectivity analyses (Langs et al., 2015;Mueller et al., 2013).
The maps of the DMN registered in the functional space revealed previously underappreciated parts of this network such as basal forebrain and anterior and mediodorsal thalamic nuclei. studied functional gradients along cortical surface and found that DMN areas were at the opposite end of primary motor/sensory areas in a spectrum of connectivity differentiation and that DMN areas exhibit the most considerable geodesic distance at the cortical level, being equidistant to the unimodal cortical areas (Margulies et al., 2016). These investigators suggested that the DMN acts as a neural relay for transmodal information. We speculate that thalamus and basal forebrain may follow the same model at a subcortical level, integrating functional networks related to primary functions and brainstem inputs to the associative areas (Dringenberg and Olmstead, 2003;Mease et al., 2016). The involvement of the anterior and mediodorsal thalamic nuclei as well as the basal forebrain are concordant with the role of the DMN in memory processes (Andrews-Hanna et al., 2010;Schacter et al., 2007), as all these regions are relays of the unitary model of the limbic system (Catani et al., 2013;MacLean, 1952MacLean, , 1949. Previous reports of engagement of the mediodorsal thalamic nucleus and the DMN during memory tasks and the memory deficits provoked by lesions of the anterior and the mediodorsal thalamic nuclei also support this claim (Rabin et al., 2010;Spreng and Grady, 2010;Child and Benarroch, 2013;Danet et al., 2015). At a neurochemical level, the basal forebrain is also a principal actor in the production of acetylcholine (Zaborszky et al., 2015). Acetylcholine has a physiological and a neuropharmacological effect on memory processes. For instance, cholinergic system mediates rhythmic oscillation in the hippocampus that facilitates encoding (Hasselmo, 2006;Zaborszky et al., 2008). By providing evidence of the involvement of medial septal cholinergic nucleus and its structural connection to the hippocampus in our DMN model, the present work indicates a match between connectivity, neurochemistry and cognition.
The same correspondence between connectivity, neurochemistry and cognition applies to the relation between DMN and emotional modulation (Alcalá-López et al., 2017;Bzdok et al., 2013;Mars et al., 2012;Raichle, 2015;Spies et al., 2017;Zhao et al., 2017). The nucleus accumbens is a central output for the dopaminergic projections and is involved in emotion regulation and affect integration (Floresco, 2015;Laviolette, 2007). The nucleus accumbens also receives glutamatergic inputs from the hippocampus and the prefrontal cortex (Britt et al., 2012) belonging to the DMN. Surprisingly, our analysis also revealed the ventral tegmental area, which is also a dopaminergic nucleus with projections to the nucleus accumbens and the medial prefrontal cortex (Morales and Margolis, 2017). This association with the mesolimbic dopaminergic pathway is reinforced by our results of functional connectivity, since ventromedial prefrontal cortex and midbrain were among the structures with highest partial correlations with basal forebrain. Hence, integrating present and previous findings, it appears that the DMN, as defined by functional connectivity, is at the interplay between a cholinergic and a dopaminergic system dedicated to memory and emotion.
The new DMN's subcortical structures identified in the current work have cognitive and neurochemical roles that open a new window to the understanding of distinct brain pathologies affecting DMN connectivity (Table 4) apparently related to significant changes of the DMN (He et al., 2007;Persson et al., 2008).
The high centrality of the basal forebrain in the DMN network may explain this early link between DMN and Alzheimer's disease. In schizophrenia, also associated with changes in the 1 5 DMN connectivity (Bluhm et al., 2007;Pomarol-Clotet et al., 2008), neuropathological evidence suggests an abnormal glutamatergic-dopaminergic interaction at the level of nucleus accumbens (McCollum and Roberts, 2015). Additionally, the ventral tegmental area is connected to the nucleus accumbens through the mesolimbic system, the classical dopaminergic pathway associated with schizophrenia, and functional data shows a decrease of connectivity between VTA and several brain regions, including the thalamus, in unmedicated schizophrenic patients (Hadley et al., 2014). The pathophysiology of others diseases, such as drug addiction, depression, temporal lobe epilepsy and attention deficit and hyperactivity disorder involve modifications in the nucleus accumbens, medial septal nuclei or the thalamic nuclei that connect limbic regions as well as dysfunctional connectivity of the DMN (Table 4; Butler et al., 2013;Dinkelacker et al., 2015;Ivanov et al., 2010;Scofield et al., 2016;Vialou et al., 2010;Voets et al., 2012;Volkow et al., 2011;Yamamura et al., 2016;Zhu et al., 2016).
Hence, the involvement of the basal forebrain and the thalamic nuclei in the DMN appears to bridge the gap between the subcortical anatomical differences and the global differences in the DMN previously reported.
Interestingly, the thalamus and the basal forebrain are phylogenetically older than many cortical structures and especially those that compose the DMN (Butler, 2008;Karten, 2015;Yamamoto et al., 2017). The inclusion of these structures in the anatomical model of the DMN can open a window to the exploration of DMN in other mammalian species as well (Buckner and Margulies, 2018;Lu et al., 2012;Rilling et al., 2007;Vincent et al., 2007). For instance, the medial thalamus is very involved in the rat and mouse's DMN (Gozzi andSchwartz 2016, Bertero et al. 2018).
One limitation of the present work is that the overlap between the DMN map and the nuclei studied is based on comparison with templates or variability maps, and not with the individual location of the nucleus in the explored subjects. The limited capacity of structural MRI to differentiate these small nuclei does not allow such comparison. Besides, the reverse transformation of the DMN from the functional space to the MNI space may not be exact, due to inherent limitations of inverse transformations. Although these limitations may decrease the accuracy of the intersection quantification with discrete nuclei, they did not alter the apparent overlap with basal forebrain and with the thalamus.