Amyotrophic lateral sclerosis (ALS) is classically described as a disease of upper and lower motor neurons (Ravits et al., 2007). However, postmortem studies have demonstrated that pathological hallmarks of ALS (such as TDP-43 inclusions) appear in motor cortex as well as in multiple cortical and subcortical structures during disease progression, depicting a propagation pattern (Brettschneider et al., 2013; Braak et al., 2013). Thus, ALS has been growingly conceptualized as a disease of the motor subnetwork or, in more advanced stages, a multi-network disease affecting motor, premotor and sensory areas. To which extent these networks are damaged by the neurodegeneration and remodel themselves during the disease progression, it is a subject of active investigation.

Diffusion-tensor imaging (DTI) studies have revealed that the degeneration of axons is detected in more and more white-matter tracts affecting different structures (corticospinal, corticorubral, corticostriatal and perforant tracts) at different stages of the propagation scheme (Neuroimaging Society in ALS (NiSALS) DTI Study Group et al., 2016; Kassubek et al., 2014; Kassubek et al., 2018) as well as in interhemispheric connections (Filippini et al., 2010) and in spinal cord (Cohen-Adad et al., 2013). However, analysis of resting-state networks by functional magnetic resonance imaging (fMRI) has revealed increased functional connectivity has been described in symptomatic ALS patients in the sensorimotor (Menke et al., 2016; Agosta et al., 2018) somatosensory (Agosta et al., 2011) and cortico-striatal networks (Fekete et al., 2013). Notably, an increase in functional connectivity in the motor subnetwork was already detectable in presymptomatic and early-stage ALS patients (Schulthess et al., 2016).

Although these data indicate that a significant architectural remodeling of the motor subnetwork takes place in ALS patients in the face of ongoing neurodegeneration, the extent of such remodeling and the structural changes underlying this effect in the motor network are unclear. Of particular interest is the extent of remodeling of the input network to pMO: in fact, the type and amount of projection to pMO determines not only the output from MO, which is strongly related to the execution of movements; in addition, the synaptic inputs are strong regulators of neuronal activity pattern and may influence activity-dependent pathogenic factors (Roselli and Caroni, 2015; Bading, 2017). To date, connectivity maps have been obtained at mesoscale (Oh et al., 2014; Mitra, 2014) or at cellular resolution (Mao et al., 2011; Wertz et al., 2015) only for wild-type models.

In order to address the remodeling of the subnetwork in an experimentally tractable condition, we have selected to quantitatively map the projections to the primary motor cortex network in the SOD1(G93A) ALS mouse model using a newly developed AAV variant endowed with retrograde tracing abilities. We have demonstrated that selected cortical areas display an increased projection to pMO already at presymptomatic stage and the increase in projection expands during disease progression to involve additional cortical and subcortical structures. We show that the pattern observed in the mouse model bears significant similarities with fMRI functional connectivity data gathered from human ALS patients. Thus, we have identified one structural component of the early and selective remodeling of the large-scale architecture of the motor subnetwork, which may contribute to explain the functional connectivity changes observed in patients.

In the present work, we have demonstrated, using viral-tracing approaches, that the motor subnetwork in an ALS mouse model undergoes distinct remodeling during disease progression with stage and structure-dependent effects: projection from SS is increased already in presymptomatic mice and remains stable, whereas projections from AUD and cMOs and cMOp either appear or increase further in the symptomatic stage. We show that this pattern displays similarities with the network remodeling in the form of increase functional connectivity changes observed by fMRI in ALS patients. Thus, we provide cellular-resolution data that contribute to analysis of large-scale architecture of motor circuits in ALS mouse model, which may provide entry points to understand changes occurring in human patients.

In order to map the input to the MO in mSOD and WT mice, a retrograde viral-tracing strategy was considered. We have introduced a new variant of AAV9 which displays significantly higher capacity for retrograde infection of neurons in vivo. Although retrograde tracing can be achieved with non-viral approaches, these methods suffer from limited sensitivity, stability and do not provide genetic access to the target neurons (Tervo et al., 2016). Retrograde tracing has been previously achieved in vivo using rabies-derived vectors (Wickersham et al., 2007), herpes simplex virus (Ugolini et al., 1987) or canine adenovirus (Soudais et al., 2001). These vectors, however, have been plagued by the intrinsic toxicity of the expressed viral proteins (as in the case of rabies - Ginger et al., 2013 - although new rabies variants may have overcome this issue, see Ciabatti et al., 2017) or by the tendency to activate inflammation (Muruve, 2004). Recently, the development of retrograde AAV vectors have been undertaken to allow retrograde tracing with genetic access and low toxicity; in particular, a retrograde AAV2 variant has been reported (rAAV2; Tervo et al., 2016). Our AAV9 variant extends and complements the existing toolset of AAV vectors endowed with retrograde infectivity. Although rAAV2 belongs to the AAV2 serotype and may strongly bind heparan sulphate, our AAV9-derived retrograde AAV variant has the advantage of reduced binding to heparin sulfate (Shen et al., 2011) and therefore it is less affected by changes in brain extracellular matrix (e.g. due to neuroinflammation associated with neurodegeneration); AAV9 is only infrequently targeted by pre-existing immunity to AAVs (Zhang et al., 2011; Saraiva et al., 2016) and may provide the entry point to enhanced vectors for circuit-specific gene therapy in humans since AAV9 has been already successfully tested for this application (Boutin et al., 2010).

We first validated the tool by mapping the input connectivity to pMO and sMO at cellular resolution; notably, the discreet pattern of regions containing retrogradely labelled neurons separated by others containing or no labeled cell, in a reproducible pattern and matching the list of brain structures projecting to pMO (e.g. Hooks et al., 2013) and the Allen Brain Connectivity atlas) suggest that the infection of distant neurons is not an artifact of passive viral diffusion or infection via blood supply (which would have resulted in a continuous or erratic distribution of labelled neurons). We further validated the reliability of our approach in the context of the mSOD1 mouse, verifying that viral-tracing results on SS and pMO could be reproduced by non-viral tracing (CTb) and that no abnormalities could be found in the projections to visual cortex, an area spared the pathogenic process. Nevertheless, AAV vectors relie on retrograde transport to reach the cell body (where expression of the genome takes place; Castle et al., 2014b) in process dependent on dynein/dynactin (Castle et al., 2014a) and one may speculate that the transport slow-down and dysfunction reported in ALS (Bilsland et al., 2010; Marinkovic et al., 2012) may bias the quantitative evaluation of the tracing experiments. However, the slowing of the transport would result in the decrease or delayed arrival of the AAV to the neuronal cell body, actually decreasing the chances of Cre expression and possibly resulting in decreased number of projecting neurons; therefore, this mechanism is unlikely to underlie the observed increase in the number of projecting neurons.

We exploited this new retrograde AAV9 to investigate in ALS models the qualitative and quantitative changes in the structure of the motor subnetwork, in particular of the projections to the pMO. A major finding of the present work is the increase in the numbers of neurons projecting from several cortical and subcortical regions to pMO; the increase is selective for some cortical areas only and the number of regions involved increases over time. The increase in the number of neurons projecting to a fixed, small volume of pMO is most likely to be interpreted as due to the expansion of the axonal arborizations of these neurons in pMO as disease progresses and it is in agreement with the increased frequency of EPSPs observed early in disease course (Fogarty et al., 2015). What may be the driving force of this expansion? One may hypothesize that a pathogenic process ongoing in projecting neurons may drive the expansion; however, neither the burden of misfolded SOD1 nor the signs of structural disturbances (i.e. the decrease in spine density) were detectable in the areas displaying increased projection to pMO. On the other hand, it has been demonstrated in several models (e.g. in the neuromuscular unit), that silencing of either the pre- or post-synaptic site is sufficient to induce axonal sprouting (Caroni et al., 1994; Caroni, 1997). Since reduced firing of vulnerable spinal MN has been shown to precede degeneration (Martínez-Silva et al., 2018; Roselli and Caroni, 2015), it is tempting to speculate that the expansion of the projections may be driven by abnormal activity of vulnerable post-synaptic neurons in the pMO (including vulnerable corticospinal MN; Ozdinler et al., 2011). Indeed, in mSOD mice excitability changes appear to affect a larger set of neurons in pMO than previously thought (Kim et al., 2017).

When the progressive increase in projections to pMO detected in the tracing experiments are compared with the changes in connectivity reported here and by others (Agosta et al., 2018; Menke et al., 2016; Schulthess et al., 2016), several similarities emerge: (i) both in human and mouse data, we observe a progressive remodeling of the motor subnetwork, (ii) the remodeling of the motor subnetwork is detectable before the appearance of overt clinical or behavioral abnormalities, (iii) the remodeling of the motor subnetwork involves the primary motor cortex and more rostral, homolateral or contralateral structures (in the frontal lobe, prefrontal cortex or secondary motor cortex in human and mouse, respectively) and projecting to pMO, and (iv) both in human and mouse data the primary somatosensory cortex appears involved in the motor subnetwork remodeling (either in terms of increased projection to pMO in the mouse, or in terms of connectivity between left and right SS, SS, sensory-motor or SS to other cortical areas; Douaud et al., 2011; Agosta et al., 2011). Nevertheless, thalamocortical connectivity does not appear to be altered in the functional connectivity studies.

Based on these similarities, might the increase in projection to pMO be a leading mechanism underlying the functional hyperconnectivity in ALS patients? Although the relationship between structural and functional connectivity is not precise, severals studies have demonstrated that, at large-scale level, structural connectivity is a strong predictor of functional connectivity (Honey et al., 2009; Honey et al., 2007; Damoiseaux and Greicius, 2009; Hermundstad et al., 2013) and maps of functional and structural connectivity display high degrees of similarity (van den Heuvel et al., 2009). Moreover, studies in non-human primates comparing functional connectivity with anatomical tracing have revealed that anatomical connectivity strongly (although not exclusively) shape the functional connectivity (Shen et al., 2012; Shen et al., 2015). On the other hand, deducing structural connectivity from functional connectivity data can be fraught with inaccuracies, since indirect connections may contribute to it (Adachi et al., 2013). Thus, on the assumption that the observations made on the remodelling of the motor subnetwork in mouse models could be extrapolated to humans, the expansion of projections to pMO may contribute a structural substrate of the functional hyperconnectivity observed in fMRI studies. Notably, alternative models have been proposed to explain the hyperconnectivity phenotype. Changes in functional connectivity could also result from an alteration in the local excitation/inhibition balance due to the dysfunction of inhibitory interneurons (Douaud et al., 2011): in fact, pathological studies have found that calbindin-positive GABAergic interneurons undergo degeneration in ALS (Maekawa et al., 2004), lending support to the hypothesis that loss of inhibitory tone contributes to clinical dysfunction in ALS (Turner and Kiernan, 2012). The two models are actually not mutually exclusive: the pattern of either increased either decreased connectivity in resting-state (Jelsone-Swain et al., 2010; Mohammadi et al., 2009; Zhou et al., 2013; Agosta et al., 2013; Douaud et al., 2011) and task-based fMRI (Heimrath et al., 2014; Konrad et al., 2002; Lulé et al., 2009) may be the result of ongoing degeneration and loss of axons (Dadon-Nachum et al., 2011; Sarica et al., 2014) accompanied by potentially compensative phenomenon (Bernier et al., 2017). The observed increase in projection from cortical and subcortical areas might be an early adaptive response to ongoing cell dysfunction up to the point where a critical cell loss is reached and a disconnection syndrome begins to emerge (Gorges et al., 2017).

In addition to the study of large-scale patterns of projections to vulnerable cortical areas, the use of retrograde viral vectors has allowed the access to morphological features of a distinct population of neurons selected by connectivity rules (i.e. all neurons projecting to pMO). Our analysis has revealed a stage-dependent loss of spines in all neurons projecting to pMO (first in layer V, then in layer II/III), which appears independent of misfSOD burden and involves neuronal populations in areas whose vulnerability was previously unrecognized, such as AUD neurons. Spine loss has been detected as a morphological counterpart of the early stages of the degenerative process ongoing in the layer V of the motor cortex of mSOD mice (Fogarty et al., 2015). The longitudinal study of multiple brain areas in Golgi-staining preparations (Fogarty et al., 2016) has revealed the early and progressive spine loss affecting the basal dendrites of pyramidal neurons from MO, medial pre-frontal (MPFC) cortex; notably, SS neurons appeared to be affected only very late in disease progression (P120; Fogarty et al., 2016). By identifying a neuronal subpopulation of SS based on connectivity rules (i.e. only the neurons projecting to pMO), we report an earlier loss of spines than previously though (P50 vs P120). This finding not only underscores the importance of assessing disease manifestations in homogeneous (and characterized by connectivity rules) populations but also suggests that spine loss may be a phenomenon occurring in a larger set of neurons, possibly across the whole motor subnetwork.

Our study displays some limitations. Firstly, in order to maintain tractability while still addressing the core question of the large-scale remodeling of the structure of projections in the motor subnetwork, we decided to limit the analysis to the forebrain, avoiding the analysis of brainstem nuclei and cerebellum. Although these regions provide relevant input to the motor subsystem, they are not usually identified in fMRI studies of ALS and therefore their contribution to the definition of the hyperconnectivity phenotype may be more limited. Second, although it is desirable to have large number of biological replicates, the need to maintain the genotype blinding and to manually identify and anatomically annotate each neuron (more than 150,000 for the present work) did not allow for a n > 3–6, thus underpowering the study for the comparison of structures with fewer number of projecting neurons. Although the present study is in line with the numerosity currently manageable in manual reconstructions (Hooks et al., 2013; Yamawaki et al., 2016; DeNardo et al., 2015; Tervo et al., 2016; Kohl et al., 2018), the advancement of machine learning and automated neuronal annotation (Fürth et al., 2018) may allow a more comprehensive quantitative analysis of input network to pMO.

Third, we did not consider ipsilateral sMO and pMO for cell counting in our analysis. Due to the proximity of pMO and sMO, we could not have conclusively excluded the possibility that infected neurons in the ipsilateral sMO were derived from retrograde infection or virus diffusion, introducing ambiguity to the connectivity map. Further refinement of the retrograde-tracing approach is ongoing to provide a whole brain, full resolution map of the connectivity of the motor subsystem in ALS.

Taken together, our circuit-tracing data in mice and imaging data in humans converge to show that a significant and progressive remodeling of cortical circuits takes places in ALS and that this phenotype may be amenable to investigation in animal models. The mechanistic drivers of this remodeling are currently unclear, although it is possible to speculate that homeostatic mechanisms may be activated by alterations in excitability of pMO neurons, including corticospinal motor neurons (Kim et al., 2017). In fact, pathological decrease or increase in the firing properties of vulnerable neuronal subpopulations appears to be a shared phenotype of multiple neurodegenerative conditions (Roselli and Caroni, 2015) and may be one of the driving forces of adaptive cortical remodeling in neurodegeneration (LANDSCAPE Consortium et al., 2015; Rosskopf et al., 2017; Rosskopf et al., 2018). Thus, the combination of the analysis of large-scale projection patterns (enabled by retrograde viral tracing) with chronic manipulation of excitability and firing (such as chemiogenetics; Saxena et al., 2013) may prove fundamental to link functional and structural cortical phenotypes.

All the murine data generated or analysed during this study are included in the manuscript and supporting files. The raw images are deposited on the Dataverse database (https://doi.org/10.7910/DVN/5VNSXE)

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Author details

1. Barbara Commisso

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

2. Lingjun Ding

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Data curation, Investigation, Writing—original draft

   Competing interests

   No competing interests declared

3. Karl Varadi

   Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany

   Contribution

   Resources, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

4. Martin Gorges

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Resources, Data curation, Investigation, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

5. David Bayer

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

6. Tobias M Boeckers

   Department of Anatomy and Cell biology, University of Ulm, Ulm, Germany

   Contribution

   Resources, Supervision, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

7. Albert C Ludolph

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

8. Jan Kassubek

   Department of Neurology, University of Ulm, Ulm, Germany

   Contribution

   Conceptualization, Resources, Supervision, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

9. Oliver J Müller

   Department of Internal Medicine, University of Kiel, Kiel, Germany

   Contribution

   Resources, Supervision, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

10. Francesco Roselli

    1. Department of Neurology, University of Ulm, Ulm, Germany
    2. Department of Anatomy and Cell biology, University of Ulm, Ulm, Germany

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    francesco.roselli@uni-ulm.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9935-6899

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