2.1. Mouse brain connectivity network

We use data from the Allen Institute for Brain Science’s Mouse Connectivity Atlas (MCA) to create the mouse connectivity network. This network is derived from viral tracing studies and contains fully directional connectivity intensity information from 426 regions across both hemispheres; more information on the MCA can be found at the Allen Institute’s website and in the citation (Oh et al., 2014). The network we use here can be obtained either on the Allen Institute’s website in the ‘Mouse Connectivity Atlas’ section or in Supplemental Materials attachment #4 from the above cited paper (Oh et al., 2014).

2.2. Mouse experiments and data collection for original synucleinopathy dataset (propagation of synucleinopathy from the olfactory bulb)

We injected C57/Bl6 wild type mice unilaterally into the olfactory bulb with αsyn pre-formed fibrils (PFFs) made of recombinant wild-type mouse αsyn PFFs (mPFFs) or wild-type human αsyn PFFs (huPFFs). The mice were sacrificed via transcardial perfusion with 4% paraformaldehyde in groups at either 1, 3, 6, 9, 12, or 18 months post-injection. The entire brains were sectioned coronally on a microtome into 30 μm thick sections in 8 series. Free-floating sections were stored in cryoprotectant until use. We stained every 8th section of the entire brain with Ab51253 (Abcam) for αsyn phosphorylated on Serine 129, a marker of pathological αsyn (Beach et al., 2009; Rey et al., 2016a, 2018a, 2016b). Endogenous peroxidase were blocked by incubation with 3% H₂O₂ for 20 min. Sections were then blocked in 10% normal goat serum, 0.3% triton x100, 0.4% Bovine serum albumin; followed by overnight incubation with rabbit anti pser129 antibody (1/10000 in 2% normal goat serum, 0.3% triton X100). We incubated the sections with secondary antibody goat anti-rabbit biotinylated (1:500, Vector lab) in blocking solution. Sections were then incubated for 1 h in ABC (ABC Kit, Vector lab) and revealed using DAB kit (Vector lab).

These sections were blind coded and were analyzed at x20 using Nikon eclipse NieU microscope. We semi-quantitatively rated αsyn inclusions on a 0–4 scale (one score per brain region) based on relative abundance of pser129 inclusions: 0 = no aggregates; 1 = sparse: very few neurites, max 1 soma; 2 = mild inclusions; 3 = dense inclusions; 4 = very dense inclusions) for 4 mice per group at 1 month post-injection, 4 mPFF and 3 huPFF injected mice at 3 months, 5 mPFF and 4 huPFF injected mice at 6 months, 3 mPFF and 5 huPFF injected mice at 9 months, 7 mPFF and 5 huPFF injected mice at 12 months, and 5 huPFF and 3 mPFF injected mice at 18 months. Mouse brain regions were manually identified using the Paxinos and Franklin mouse brain atlas (Paxinos and Franklin, 2012). The list of the brain regions where αsyn inclusions were detected can be found in the original publications (Rey et al., 2018a; Rey et al., 2016b). Importantly, the staining conditions used would detect only dense pser129-positive inclusions and we have shown previously (Rey et al., 2018a; Rey et al., 2016b) that these inclusions shared the markers for pathological αsyn aggregates (colo-calization with p62, ubiquitin and Thioflavin-S positive). During the scoring, any weak homogenously stained cell bodies were excluded from analysis to semi-quantify only dense inclusions.

Further details on mouse experiment methodology for this data can be found in the same original publications (Rey et al., 2018a; Rey et al., 2016b). It should be noted that data from timepoints up to 12 months post-injection (Rey et al., 2016b) and the dataset obtained at 18 months post-injection (Rey et al., 2018a) were quantified separately and some subregions were grouped differently. Thus, the datasets were not directly comparable, and so r, Δr, and p-values cannot be directly compared. Instead we observe that the pattern of results produced by comparing the 18-month dataset with our modeled predictions remains consistent with the suggested pattern of increasingly heavy involvement of anterograde transit along fiber tracts as a driver of αsyn inclusions propagation. We still include the 18-month data with other quantified timepoints in Fig. 5 as this is meant to be illustrative of the pattern of results we see. Our more in-depth analyses separate the data up to 12-months (Figs. 2 & 3) and at 18-months (Fig. 4) post-injection into different figures.

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Fig. 2.

Synuclein inclusion spread is better modeled by retrograde DNT at early timepoints, and anterograde DNT at later timepoints. (a) βt-curve showing NT models’ r-value with mPFF data at 3-months post-injection at different diffusion time constants. (b) Scatterplot of the NT models at their peak β-diffusion time constant, as in (a) to the left, versus data. (c) Anatomical illustrations of mPFF synuclein inclusion data and DNT-retrograde simulation. (d) βt-curve showing NT models’ r-value with mPFF data at 12-months post-injection at different diffusion time constants. (e) Scatterplot of the NT models at their peak β-diffusion time constant, as in (d) to the left, versus data. (f) Anatomical illustrations of mPFF induced synuclein inclusions at 12 months post injection and the corresponding anterograde-DNT simulation. AMY = amygdala, AOB = accessory olfactory bulb, AON = anterior olfactory nucleus, DG = dentate gyrus, ENT = entorhinal cortex, MOB = main olfactory bulb, PIR = piriform cortex, SUB = subiculum, TT = tenia tecta. Please see Fig. 1 for color legend for the major region color scheme of the balls denoting regional αsyn inclusions. The red arrow points toward the MOB where the PFFs were injected. Significance level: + p < .01, * p < .02. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 3.

Synuclein inclusion spread is better modeled by retrograde DNT at early timepoints, and anterograde DNT at later timepoints. (a) βt-curve showing NT models’ r-value with huPFF data at 3-months post-injection at different diffusion time constants. (b) Scatterplot of the NT models at their peak β-diffusion time constant, as in (a) to the left, versus data. (c) Anatomical illustrations of huPFF synuclein inclusion data and DNT-retrograde simulation. (d) βt-curve showing NT models’ r-value with huPFF data at 12-months post-injection at different diffusion time constants. (e) Scatterplot of the NT models at their peak β-diffusion time constant, as in (d) to the left, versus data. (f) Anatomical illustrations of huPFF induced synuclein inclusions at 12 months post injection and the corresponding anterograde-DNT simulation. All data and NT simulation values are log-transformed prior to both statistics and anatomical visualizations. AMY = amygdala, AOB = accessory olfactory bulb, AON = anterior olfactory nucleus, DG = dentate gyrus, ENT = entorhinal cortex, MOB = main olfactory bulb, PIR = piriform cortex, SUB = subiculum, TT = tenia tecta. Please see Fig. 1 for color legend for the major region color scheme of the balls denoting regional αsyn inclusions. The red arrow points toward the MOB where the PFFs were injected. + p < .01, * p < .02. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4.

At 18-months post injection, we find the same phenomenon of strong inclusion pattern recreation by our anterograde DNT model at timepoints far from injection, and in contrast with findings at earlier measured timepoints. (a) βt-curve showing NT models’ r-value with huPFF data at 18-months post-injection at different diffusion time constants. (b) Scatterplots for each model’s prediction at the βt diffusion rate constant that maximizes r-values with regional αsyn inclusion data from huPFF injected mice. (c) An anatomical illustration of regional αsyn inclusion pattern data and anterograde and retrograde DNT model predictions for huPFF injected mice. (d) βt-curve showing NT models’ r-value with mPFF data at 18-months post-injection at different diffusion time constants. (e) Scatterplots for each model’s prediction at the βt diffusion rate constant that maximizes r-values with regional αsyn inclusion data from mPFF injected mice. (f) An anatomical illustration of regional αsyn inclusion pattern data and anterograde and retrograde DNT model predictions for mPFF injected mice. AMY = amygdala, AOB = accessory olfactory bulb, AON = anterior olfactory nucleus, DG = dentate gyrus, ENT = entorhinal cortex, MOB = main olfactory bulb, NAcc = nucleus accumbens, PIR = piriform cortex, SUB = subiculum, TT = tenia tecta. Please see Fig. 1 for color legend for the major region color scheme of the balls denoting regional αsyn inclusions. The red arrow points toward the MOB where the PFFs were injected. + p < .01, * p < .02. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5.

DNT retrograde models are better predictors of synuclein inclusions closer to initial pathological injection, but anterograde DNT is a better predictor of synuclein inclusions as more time elapses following injection. (a) mPFF synuclein injected mice and (b) huPFF synuclein injected mice both show spatiotemporal synucleinopathy development such that at times closer to the initial injection, retrograde DNT is the best predictor, but at times farther from the injection, anterograde DNT better recapitulates αsyn inclusion spread than does retrograde DNT. (c) Calculated directional bias in the fit with data between anterograde and retrograde DNT models shifts toward anterograde DNT over time. A reference line at 0, the value at which there should be no directional bias, is added to the plot in (c). All data and NT simulation values are log-transformed prior to running statistics.

2.3. Mouse experiments and data collection for original dataset from intrastriatal injections of PFFs

For striatal injections, human alpha-synuclein was purified and assembled into PFFs in house following previously published protocols (Luk et al., 2012; Volpicelli-Daley et al., 2015). PFFs were thawed and sonicated as previously published (Rey et al., 2018a; Rey et al., 2016b). 4 week old OF1-Swiss mice were injected stereotactically 2ul of PFFs unilaterally into the dorsal striatum as previously described (striatum coordinates from bregma: AP; +0.2 mm, ML:−2.0 mm, DV: −2.6 mm). Mice were euthanized 8 weeks post injection by deep anesthesia with sodium pentobarbital and perfused transcardially with 0.9% saline, then 4% paraformaldehyde (PFA) in phosphate buffer. We collected the brains and post-fixed them for 24 h in 4% PFA. Brains were then stored in 30% sucrose in phosphate buffer at 4°C until use. Brains were sectioned coronally into 40 um thick sections on a freezing microtome. Free floated sections were stored in cryoprotectant.

The following staining protocol and semiquantitative analyses were employed: Pser129 inclusions was semi-quantified across almost 40 regions, at one timepoint 8 weeks post-injection with in house recombinant human PFFs. The semi-quantification in this study was based on immunohistochemistry and followed the same procedure for converting IHC signal to semi-quants as in (Rey et al., 2018a, 2016b). While this dataset is only cross-sectional rather than longitudinal, as it only contains one timepoint, it contains data from each individual mouse. With this dataset we accordingly assessed whether there was an identifiable directional predilection, according to our DNT modeling, in αsyn spread. The citation for this dataset can be found in (Chatterjee et al., 2019).

2.4. Mouse synucleinopathy datasets obtained from other sources (intranigral injections of fibrils)

To further investigate the propagation of abnormal inclusions in regards to theoretical propagation along axonal network, we chose to apply our connectivity model to another published dataset: Masuda-Suzukake, et al., 2013 (Masuda-Suzukake et al., 2013). Similar to our previous work (Rey et al., 2018a; Rey et al., 2016b), this study used immunohistochemistry as the basis for all quantifications or semi-quantifications and wild-type mice. In the study by Masuda-Suzukake et al., (Masuda-Suzukake et al., 2013) mice were injected into the substantia nigra with recombinant αsyn fibrils, and at least 40 separate brain regions were quantified for αsyn inclusions. The investigators semi-quantitatively assessed αsyn pathology by grading regional severity out to 15 months post-injection using 3 months and 6 months as intermediary timepoints.

2.5. Connectivity metrics, NT, and DNT

Connectivity metrics are calculated based on the topology of the mouse brain network derived from the MCA, where connectivity is represented as “outgoing” along the rows and “incoming” along the columns (Oh et al., 2014). We generated a directed and weighted connectivity measure with seed regions by summing the weighted row-wise or column-wise values in the MCA at the seed nodes. We could then correlate each selected region’s measured αsyn inclusions with its weighted connectivity, in both anterograde and retrograde directions, with the seed nodes in each study. Further information on using the MCA and its connectivity data for graph analyses can be found at the original citation (Oh et al., 2014) and in our prior paper using this atlas (Mezias et al., 2017).

NT and DNT modeling is similarly dependent on the MCA in this study, and on regional graph adjacency to the seed nodes or nodes already containing inclusions. We first calculate the Normalized Adjacency or Laplacian Matrix, L, given by the equation below:

where C is the connectivity matrix from the MCA, I is the identity matrix, and D_R and D_C are the row and column wise diagonal matrices from the MCA. Previous graph theoretic work has characterized diffusion over a network from defined seed points and has been shown to be predictive for both volumetric loss (Freeze et al., 2018; Raj et al., 2012) and metabolic dysfunction (Raj et al., 2015) in patients with AD or other tauopathic dementia, as well as for mouse models of tauopathy and amyloidopathy (Mezias et al., 2017; Mezias and Raj, 2017):

here L is the Laplacian Matrix, βt is a constant representing diffusion time determined by when it maximizes ΔR, which is the change in R-value from βt = 0 to maximum, X(0) is a vector with n number of nodes length representing seeding, and X(t) is a vector with n number of nodes length representing the final pattern of network diffusion. Further information on the derivation of the above equation and graph diffusion modeling can be found at the aforementioned citations.

In the present study, we use a modified version of the above NT model equation so that we could both capture the directionality information from the MCA allowing us to represent synucleinopathy as not only a process of spread but also of accumulation, analogous to a prion-like disorder. We captured the directionality information from the MCA by calculating different networks for each direction, where C represents the standard connectivity network, C^T represents the network in the retrograde direction, and the summed connectivity of both, C + C^T, represents the undirected network derived from the MCA. Here we use an integrative or summative approach to capture accumulation and so the equation can be represented in two manners:

More information on the mathematics behind this approach can be found in our previous work (Mezias et al., 2017; Mezias and Raj, 2017; Raj et al., 2015). The Spatial diffusion model (SPD) uses the same Laplacian and model equation, (2), from above; the difference between NT or DNT models and the SPD model is that the SPD model, as its network, instead utilizes the Cartesian distances, in voxels, between the centers of mass of any given region pair, rather than their axonal connectivity.

3.1. Interregional α-syn propagation initially mirrors retrograde direction spread along fiber tracts, but increasingly involves anterograde direction transit as time elapses

Using Eq. (2) and a seed signal placed at the injection site in the olfactory bulb, we evolved the DNT model at all model “diffusion times” βt. We first selected 3 and 12 months as timepoints representing respectively short and long amounts of time post-injection, and then assessed interregional αsyn inclusions propagation. The peak R-value between the model evaluated at all model times βt, and empirical regional αsyn inclusions data at 3 months post-injection, both retrograde DNT, R = 0.51, p < .01 and undirected NT, R = 0.44, p < .01, trend toward significantly matching the data of mice injected with mPFFs (Fig. 2a–b). We anatomically illustrate the data and our retrograde DNT (DNT-Ret) model’s predictions in Fig. 2c using a “glass brain” rendering whereby spheres represent brain regions and their size is proportional to the regional value of interest. Anterograde DNT (DNT-Ant) and Spatial diffusion (SPD) models do not significantly recreate empirical inclusion pattern at 3 months post-injection. However, by 12 months following αsyn fibrils injection, anterograde DNT, R = 0.63, p < .002, along with retrograde DNT, R = 0.60, p < .002, and undirected NT, R = 0.68, p < .002 (Fig. 2d–e) all produce significant recreations of empirical regional αsyn inclusions data in mice injected with mPFFs. We anatomically illustrate our anterograde DNT model’s regional inclusions predictions and empirical data in Fig. 2f. The SPD model continues to fail to produce any pattern that significantly matches empirical data.

We observe a remarkably similar pattern in our datasets between injection at 3 and 12-months post injection in mice injected with huPFFs. Retrograde DNT, R = 0.65, p < .002, and undirected NT, R = 0.59, p < .002 significantly recreate empirical regional αsyn inclusions patterns at 3-months post-injection, while anterograde DNT does not (Fig. 3a–b). We anatomically illustrate the strong match between retrograde DNT and empirical data in Fig. 3c. However, by 12-months post-injection anterograde DNT, R = 0.72, p < .002, significantly, and most faithfully, recreates empirical regional patterns of αsyn inclusions (Fig. 3d–e). Retrograde DNT, R = 0.57, p < .01, trends toward, and undirected NT, R = 0.67, p < .002 significantly, recreate empirical inclusions patterns (Fig. 3d–e). We anatomically illustrate the data and the anterograde DNT model’s recreation in Fig. 3f. The SPD model does not recreate spatiotemporal αsyn inclusions patterns in mice injected with huPFFs.

We further analyzed the 18-month post-injection data and observed the same phenomenon of implied heavy involvement of anterograde direction fiber tract transit in mice injected with both mPFFs and huPFFs. As referred to in our note above, the R, ΔR, and p-values at 18 months are not directly comparable with those from the other timepoints in our dataset, so we analyze and present the 18-months data separately. We found that anterograde DNT produces a significant, and the strongest, recreation of regional inclusions patterns in both mice injected with huPFFs, R = 0.64, p < .002 (Fig. 4a–b) and mice injected with mPFFs, R = 0.70, p < .002 (Fig. 4d–e), compared to retrograde DNT, non-directional NT or spatial diffusion. Data at 18-months post-injection from both mice injected with huPFFs and mPFFs also trends toward or does produce significant, albeit not as faithful, recreations by retrograde DNT, R = 0.49, p < .01 (huPFFs), R = 0.60, p < .002 (mPFFs), and undirected NT, R = 0.55, p < .01 (huPFFs), R = 0.63, p < .002 (mPFFs) (Figs. 4a–b & 4d–e). We anatomically illustrate data as well as predictions from our anterograde and retrograde DNT models in Fig. 4c (huPFFs) and Fig. 4f (mPFFs). We again find the consistent pattern, across injectate types, of implied heavy involvement of anterograde direction fiber tract travel as a driver of αsyn pathology spread.

We next computed ΔR-values as described in Data Analysis subsection of Methods. We repeated this for anterograde, retrograde and bidirectional DNT models, as well as the non-network spatial diffusion model. We repeated above analysis by comparing the model against each individual time point of the empirical mouse data. We found that the DNT model of retrograde spread of αsyn along fiber tracts, as modeled by (DNT-Ret), fits better the in vivo αsyn propagation pattern, as measured by ΔR-value against empirical αsyn inclusion data at 3, 6, and 9 months post-injection than does anterograde spread, modeled by (DNT-Ant.). However, by 12 months (the last numerically directly comparable timepoint in these data – please refer to material and methods), this pattern is reversed and anterograde DNT gives a better fit for spatiotemporal αsyn inclusions propagation. Furthermore, despite being slightly different in terms of number and location of regions quantified, the data obtained from 18-months post-injection shows the best fits with modeled synucleinopathy spread proceeding in the anterograde, as opposed to retrograde, direction, consistent with the 12-months results. This result is consistent regardless of whether mice were initially injected with mouse- (Fig. 5a) or human- (Fig. 5b) PFFs. Furthermore, bias toward anterograde synucleinopathy propagation, as measured by anterograde DNT ΔR-value minus retrograde DNT ΔR-value, consistently increases over time in both human and mouse PFF datasets (Fig. 5c).

4.1. Transsynaptic and fiber tract-based spread model replicates the αsyn inclusions spread observed in vivo

Axonal transport followed by transsynaptic spread have been proposed as the predominant mechanisms underlying αsyn pathology propagation between brain regions (Angot et al., 2010; Brettschneider and Del Tredici, 2015; Brundin et al., 2008; Brundin and Melki, 2017; Dehay et al., 2016; Rey et al., 2016a; Steiner et al., 2011; Ubeda-Bañon et al., 2014). Some studies have utilized cell cultures models (Freundt et al., 2012; Volpicelli-Daley et al., 2011) that do not reflect the complexity of brain connectivity and also lack several of its cellular components (e.g. different glia). Other researchers question the transsynaptic spread hypothesis entirely, arguing that the spread of αsyn pathology does not follow a synaptic connectivity rule (Surmeier et al., 2017a; Surmeier et al., 2017b). We explicitly test the transsynaptic and connectome based spread hypothesis using our current DNT model, with connectome data obtained from the Allen Brain Institute mesoscale mouse connectome [http://connectivity.brain-map.org; 28]. This atlas provides detailed anatomy of 426 different nodes (corresponding to brain regions), and their connections are based on extensive tracing experiments. The connectome takes into account the direction of transport of the tracers and the weight of the different connections. It is, therefore, a very powerful tool to investigate the theoretical patterns of propagation based on anterograde, retrograde or diffuse spreading of pathological seeds. To use this connectome with our DNT modeling, we had to make several assumptions. We assumed that there are no brain-region specific biases in the transfer or uptake of their adenovirus tracer (Oh et al., 2014) and that uptake is therefore even, allowing for some noise, across all parts of the mouse brain. We also assumed that there is a directly proportional relationship between fluorescent signal from the viral tracer and the total number or volume of axonal tracts from the seeded to the signal region. As discussed in the ABI paper introducing the MCA (Oh et al., 2014), these assumptions are unlikely to be true across the board, but are likely accurate enough at the coarse parcellation of a brain into 426 regions. Importantly, making these assumptions allows our model to simulate spread over the entire connectome.

We demonstrate clear correspondence between regions most likely (based on neuronal connections) to exhibit pathology from the region where the initial seeding takes place (the olfactory bulb, substantia nigra, or striatum) and the regions that we found to contain αsyn inclusions in in vivo experiments (Masuda-Suzukake et al., 2013; Rey et al., 2018a; Rey et al., 2016b) (Figs. 2–5). To confirm that the fitting of the theoretical propagation pattern with the empirical in vivo data is not dependent on site of pathology-initiation, we analyzed previously published datasets using two different injection sites (Masuda-Suzukake et al., 2013; Rey et al., 2018a; Rey et al., 2016b). We found the same correspondence between our theoretical modeling of propagation and published data from (Masuda-Suzukake et al., 2013) using with the substantia as initiation site. We further demonstrate retrograde DNT recreates spatial αsyn patterns (Chatterjee et al., 2019) (Fig. 6d). We also tested whether a pattern of spread based on spatial, rather than connectome proximity to the seed region, would recapitulate αsyn patterns, implying potential active or passive extracellular spread of pathogenic protein aggregates. This analysis showed that spatial diffusion is not a predictor of αsyn inclusions localization in the two datasets we examined (Figs. 2–6), and provides further support for the transsynaptic αsyn pathology spread hypothesis.

Importantly, recently published work from Virginia L.Y. Lee’s group investigated also the spread of αsyn pathology through the mouse brain using connectome modeling (Henderson et al., 2019). In this study, the authors investigated the propagation of αsyn inclusions from several injection sites in a time window of 1 to 6 months post injection. In this study, inclusions were also detected with an antibody directed against hyperphosphoraylated alpha-synuclein and stained sections were analyzed quantitatively in an automated way. The authors also implemented in their model the expression level of αsyn at the injection site. Despite different methods used, both our work and Henderson et al.’s demonstrate that early propagation is occurring preferentially in a retrograde direction, and that the preferential retrograde propagation is a generalized mechanism to all the brain regions each lab tested. In addition, the authors observed that the regional level of expression of αsyn influences strongly the vulnerability of a given brain region to develop αsyn inclusions. The study by Henderson et al. explores only short injection delays (up to 6 months), and thus do not investigate the long delays post injections where we observed a switch to a preferential anterograde propagation direction after 9 months, both in our OB-injection dataset and in data from (Masuda-Suzukake et al., 2013).

4.2. Directional switching of αsyn propagation might explain divergent spatiotemporal pathology development patterns between patients

One advantage of our paradigm is the possibility to test transregional spread along fiber tracts in both anterograde (pre-to-postsynaptic) and retrograde (post-to-presynaptic) directions. Interestingly, we found that both anterograde and retrograde directions can successfully recreate transregional αsyn inclusions spread, consistent with prior cell culture (Freundt et al., 2012; Volpicelli-Daley et al., 2011) and in vivo (Rusconi et al., 2018; Ulusoy et al., 2017; Ulusoy et al., 2013) studies showing both anterograde and retrograde transport of αsyn. In fact, our results suggest that the directional bias of αsyn spread may in fact change over time.

What cellular mechanisms might contribute to change biases in directional spread over time? Prior work on transsynaptic αsyn propagation suggests that spread through a neural network in an anterograde direction might involve release of exosomes into the synaptic cleft (Steiner et al., 2011). However, retrograde spread can also occur, via release into the synaptic cleft from dendrites or soma, and possibly enter the presynaptic side via tunneling nanotubes or even receptor mediated internalization (Ubeda-Bañon et al., 2014). For network spread to occur bidirectionally, both transsynaptic propagation and axonal transport/diffusion of αsyn must be bidirectional, assumptions that are supported by cell culture and microfluidic studies (Saha et al., 2004; Tang et al., 2012; Utton et al., 2005). Interestingly, αsyn can interfere with the net directionality of axonal transport by its interaction with kinesin and dynein motors (Saha et al., 2004; Utton et al., 2005), and thereby affect directional bias.

We speculate that biased spread influences spatiotemporal development of αsyn pathology and partly explains why some regions exhibit αsyn inclusions while others do not. The enteric nervous system and/or the olfactory system represent two possible locations where the accumulation of misfolded αsyn species overwhelm the cellular protein degradation mechanisms in PD and DLB. In PD, αsyn inclusions development in the central nervous system is generally first observed in dorsal motor nucleus of the vagus nerve (the origin of enteric nerves) and in olfactory areas such as the olfactory bulb and anterior olfactory nucleus (Braak et al., 2003a; Braak et al., 2003b; Braak and Del, 2017; Braak and Del, 2016; Braak et al., 2004; Del Tredici and Braak, 2016; Rey et al., 2018b; Ubeda-Bañon et al., 2014). In DLB, αsyn aggregates are frequently seen in olfactory structures as well as limbic system (Beach et al., 2009).

Our work posits not only directional biases in αsyn spread, but a directional switch in these biases (Figs. 2–6). αSyn can interact with both anterograde direction kinesin and retrograde direction dynein axonal transport protein families (Bieri et al., 2018). Importantly, αsyn misfolding appears to decrease its anterograde transport by kinesin proteins, and increase its retrograde transport by dynein proteins (Volpicelli-Daley, 2017) by at latest 8 weeks post-aggregation initiation. This could explain an early retrograde bias in αsyn propagation that shifts to either bidirectional or anterograde αsyn spread as αsyn inclusions progress. Because both the substantia nigra and olfactory areas have dense connections with the striatum (Oh et al., 2014), the relative sparsity of αsyn aggregates in the striatum in PD might be viewed as unexpected. However, if transsynaptic αsyn spread is predominantly in the retrograde direction early in disease course (Figs. 2–6), we would indeed not expect early pronounced αsyn inclusions accumulation in the striatum because most of the connections between the injected olfactory bulb and the striatum are afferents to the striatum (Oh et al., 2014).

4.3. Assumptions and limitations

There are limitations of our model and of our datasets. First, our DNT modeling rely on several assumptions so we could model the spread over the entire connectome (we assumed that the uptake of adenovirus tracer is even in the brain, and assume that the tracers fluorescent signal is proportional to the volume of axonal tracts containing fluorescent signal). As mentioned earlier and as discussed in previous work (Oh et al., 2014), our assumptions are likely accurate enough for the parcellation in 426 brain regions we are using here.

In addition, the model does not consider region-specific factors that may influence the susceptibility of regions to become ‘infected’ by pathogenic αsyn. Therefore, our model does not consider region-specific factors which can influence an area’s susceptibility to develop inclusions, regardless of spread, a concern other recent work investigating αsyn inclusion spread notes (Henderson et al., 2019). Our model also does not reflect that neurons might die, due to aggregates forming, in some brain regions, and therefore neither updates the connectome to account for loss of white matter nor updates the model to account for loss of αsyn that dead neurons once contained. Uneven antibody penetrance between mice could potentially confound data. There are also limitations regarding our dataset because it is only semi-quantitative (Rey et al., 2018a; Rey et al., 2016b). However, in our long-term study (Rey et al., 2018a), we performed densitometry analysis of pser129 inclusions on a restricted number of brain regions, in addition to our semi-quantitative analysis, and the results from both method were consistent indicating that our semi-quantitative analysis using scores reflects well the density of inclusions present in a given brain region. Finally, cellular inflammatory and excocytosis responses that occur in response to injection with α-synuclein fibrils could affect spread processes for α-synuclein inclusions at timepoints closest to injection. The fit of the DNT model at 3-months post-injection is worse than at any other timepoint, potentially indicating injection-response cellular processes as a limitation for how well a spread or diffusion model can fit data at timepoints close to injection.

Additionally of note, some technical aspects differs between the different datasets. We performed injections into the olfactory bulb in 7–8 weeks old C57/Bl6 mice, while Masuda-Suzukake and coworkers (Masuda-Suzukake et al., 2013) injected older mice of the same strains. Regarding our intrastriatal injections, we injected 4 week-old mice of a different background strain (OF1mice). The fibrils used for each dataset were different or produced by different labs: for olfactory bulb injections, mPFFs and huPFFs were produced by K. Luk while huPFFs used for striatal injections were produced in our lab (following K Luk’s published protocol) (Volpicelli-Daley et al., 2015); and Masuda-Suzu-kake’s fibrils were produced following a different protocol in their own lab (Masuda-Suzukake et al., 2013). Thus, it is possible that these different assemblies have different seeding and propagation capacities. Despite these differences, our results demonstrate similar propagation characteristics in the three different datasets.

We acknowledge the Van Andel Research Institute and the many individuals and corporations that financially support research into neurodegenerative disease at the Institute. N.L.R.’s work is supported by the Peter C. and Emajean Cook Foundation. P.B. is supported by grants from the National Institutes of Health (1R01DC016519-01, 5R21NS093993-02 and 1R21NS106078-01A1). P.B. reports additional grants from Office of the Assistant Secretary of Defense for Health Affairs (Parkinson’s Research Program, Award No. W81XWH-17-1-0534), The Michael J Fox Foundation, National Institutes of Health, Cure Parkinson’s Trust, which are outside but relevant to the submitted work.

We would also like to acknowledge the Weill Cornell Neuroscience and Radiology Departments, as well as the Feil Family Brain & Mind Research Institute and the many people who generously support Weill Cornell. AR and CM are supported by grants from the National Institutes of Health (R01NS092802). AR and CM would also like to acknowledge our former and present colleagues from the IDEAL lab.

We also thank Luk, KC, Trojanowski, JQ, Lee, VM, George, S, Steiner, JA, Madaj Z, Maroof, N, Chatterjee N, Saiz Sanchez D, Quansah E, Becker K, Ma J, Escobar Galvis ML, Kordower JH for the fruitful collaboration which led us to the three prior publications from which we have extracted data for the current study.