Brown JA, Deng J, Neuhaus J, Sible IJ, Sias AC, Lee SE, Kornak J, Marx GA, Karydas AM, Spina S, Grinberg LT, Coppola G, Geschwind DH, Kramer JH, Gorno-Tempini ML, Miller BL, Rosen HJ, Seeley WW. Patient-Tailored, Connectivity-Based Forecasts of Spreading Brain Atrophy. Neuron. 2019 Dec 4;104(5):856-868.e5. Epub 2019 Oct 14 PubMed.

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  1. This is very elegant work from Jesse Brown and colleagues. I found two aspects of the study particularly noteworthy. The first is the idea of a personalized prediction model of longitudinal brain atrophy based on the trans-neuronal spreading hypothesis. The second is the introduction of a concept called “nodal hazard,” which is a regional risk measure of future atrophy based on the degree of baseline atrophy in regions that are highly functionally connected. Compared with previous group-level approaches, an individualized metric of rate and directionality of imminent brain atrophy has important potential ramifications for clinical practice and clinical trials. For example, since brain atrophy is intimately linked to clinical disease progression, this connectivity-based method may prove useful for the prognosis of various neurodegenerative disorders. Also, placebo and treatment groups could be carefully matched for expected atrophy rates in clinical trials. Moreover, identifying a group of “fast-progressors” may allow more-efficient screening of potential drug candidates (i.e., shorter duration and fewer persons needed).

    A major advantage of this method is that it only requires a baseline MRI scan, coupled with graph-theory-derived information on intrinsic functional connectivity properties from fMRI scans obtained in healthy subjects. This study thus represents an important first step toward prediction of biological disease progression at the individual level. Yet, further refinements are needed.

    First, the method seems to work better for svPPA (a focal and neuropathologically homogeneous disorder [mostly TDP-43 Type C]) than for bvFTD (affecting more widespread neocortical areas and caused by a myriad of brain pathologies). Second, the predictive power decreased when baseline atrophy levels were mild. This might indicate that the method needs substantial information on the emerging neurodegenerative pattern, which may hamper application in early disease stages. Third, although highly correlated, atrophy does not equal the underlying pathology. Thus, some of the direct effects of pathology on cognition and/or behavior (i.e., not mediated by atrophy) may not be captured. Unfortunately, there are currently no selective PET tracers available that bind TDP-43 or FTLD-tau aggregates, but it would be very interesting to test this method using Aβ and tau PET data in individuals with Alzheimer’s disease.

    Finally, some caution about the accuracy of the disease epicenter location is warranted based on the retrospective nature of its definition in this study. Overall, akin to previous publications from this group (Seeley et al., 2009; Zhou et al., 2012), the current work will most likely be guiding many future scientific studies, and will hopefully result in an individualized prediction model that directly benefits patients.

    References:

    Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009 Apr 16;62(1):42-52. PubMed.

    Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron. 2012 Mar 22;73(6):1216-27. PubMed.

    View all comments by Rik Ossenkoppele

  2. I find the work by Brown et al. to be important and interesting in that they provide a novel framework for single-subject outcome measures based on network physiology. This provides more evidence that the key biology at play in neurodegenerative diseases of the brain involves the functional physiology of large-scale brain networks that support mental functioning.

    As in previous studies, the methods employed in this study cannot differentiate competing models of network-based neurodegeneration. Reductionist models involving protein spreading along large-scale networks are not distinguishable from complex systems-based models that predict network collapse based in part on functional properties of the large-scale networks themselves.

    View all comments by David Jones

  3. Jesse Brown and colleagues combined healthy functional connectome with patient baseline MRI to predict individual patterns of longitudinal gray-matter atrophy in patients with behavioral variant frontotemporal dementia (bvFTD) and semantic variant primary progressive aphasia (svPPA). Patient-tailored biomarkers represent a critical step forward for the field. Indeed, they can provide core information on the clinical evolution of patients with neurodegenerative disorders, play a crucial role in monitoring disease progression in clinical trials, and guide future prevention programs and treatments. Among many analyses presented in this excellent paper, I found three of them particularly interesting.

    First, the authors extensively explored the topographical heterogeneity in gray-matter atrophy in bvFTD and svPPA and used functional connectivity maps (derived from healthy controls) to define patient-tailored epicenters (i.e., site of the onset of the disease for each patient). In line with previous studies (Whitwell et al., 2009; Ranasinghe et al., 2016), their results support a certain within-group heterogeneity in the pattern of atrophy, which may result from distinct epicenters and disease severity. Most patient-tailored epicenters either overlapped across subjects or appeared to involve regions belonging to similar functional brain networks. This suggests that bvFTD and svPPA may arise from neurodegeneration that initially started in different brain regions belonging to the same syndrome-specific network.

    Second, the authors identified three region-wise metrics that, together with baseline volume, independently predicted future atrophy in patients: 1) the shortest path length to the patient-tailored epicenter (SPE), and the 2) nodal and 3) spatial hazard, which reflect the regional risk of subsequent atrophy based on the atrophy of most connected areas (nodal) or most spatially neighboring areas (spatial). Importantly, high nodal hazard scores led to volume loss over and above those associated with SPE or baseline atrophy. This means that a high degree of atrophy in most connected regions is a crucial factor to predict regional neuronal loss. Altogether, these results represent a major contribution to the network-based neurodegeneration framework by showing that 1) each atrophied brain region plays an active role in neurodegeneration spreading even if not primarily targeted by the disease, and 2) SPE remains a significant contributor to regional loss even when neurodegeneration has progressed. Results also imply that 3) neurodegeneration propagates not only into connected brain areas but also into spatially close regions. Further work is needed to determine if these two types of propagation are underlined by similar biological mechanisms.

    Finally, the use of a nonlinear generalized additive model very nicely evidenced that longitudinal changes in gray-matter volume are nonlinear in FTDs, as previously shown in AD (Sabuncu et al., 2011). Brain regions with high degrees of atrophy are less prone to show longitudinal changes than regions with intermediate degrees of atrophy. This has a fundamental implication for clinical trials that would use imaging-derived indexes as an outcome: The best regions to assess the effect of disease-modifying drugs in demented patients should not be those primarily targeted by the disease, nor the most atrophied, but those most connected to these areas.

    Together with previous investigations (Iturria-Medina et al., 2014; Raj et al., 2015), this study paves the way for patient-tailored approaches. Yet, there are still several challenges to translate these results into clinical practice, as individual predictions are still moderate (27/152 scans were “inaccurately” predicted [r2<0.06] and median r2=0.42 for the remaining scans) and the remaining unexplained variance might be even more challenging to predict. As mentioned by the authors, future models likely will be refined by including the patient’s functional and structural connectome. Additional model improvement may also imply to adjust the nodal and spatial hazard scores by the time interval between scans, to account for the presence of other pathological lesions (e.g., white-matter lesions), and incorporate other baseline neuroimaging, CSF, and genetic data.

    Overall, this study provides important insights into the mechanisms underlying the progression of neurodegeneration and offers promising perspectives to translate imaging-based method into clinical applications and individualized approaches.

    References:

    Iturria-Medina Y, Sotero RC, Toussaint PJ, Evans AC, Alzheimer's Disease Neuroimaging Initiative. Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders. PLoS Comput Biol. 2014 Nov;10(11):e1003956. Epub 2014 Nov 20 PubMed.

    Raj A, LoCastro E, Kuceyeski A, Tosun D, Relkin N, Weiner M, for the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. Cell Rep. 2015 Jan 14; PubMed.

    Ranasinghe KG, Rankin KP, Pressman PS, Perry DC, Lobach IV, Seeley WW, Coppola G, Karydas AM, Grinberg LT, Shany-Ur T, Lee SE, Rabinovici GD, Rosen HJ, Gorno-Tempini ML, Boxer AL, Miller ZA, Chiong W, DeMay M, Kramer JH, Possin KL, Sturm VE, Bettcher BM, Neylan M, Zackey DD, Nguyen LA, Ketelle R, Block N, Wu TQ, Dallich A, Russek N, Caplan A, Geschwind DH, Vossel KA, Miller BL. Distinct Subtypes of Behavioral Variant Frontotemporal Dementia Based on Patterns of Network Degeneration. JAMA Neurol. 2016 Sep 1;73(9):1078-88. PubMed.

    Sabuncu MR, Desikan RS, Sepulcre J, Yeo BT, Liu H, Schmansky NJ, Reuter M, Weiner MW, Buckner RL, Sperling RA, Fischl B, . The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch Neurol. 2011 Aug;68(8):1040-8. PubMed.

    Whitwell JL, Przybelski SA, Weigand SD, Ivnik RJ, Vemuri P, Gunter JL, Senjem ML, Shiung MM, Boeve BF, Knopman DS, Parisi JE, Dickson DW, Petersen RC, Jack CR, Josephs KA. Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. Brain. 2009 Nov;132(Pt 11):2932-46. PubMed.

    View all comments by Alexandre Bejanin

  4. This paper builds nicely on the realization that the spread of neurodegenerative diseases within the human brain is confined to specific, functionally defined networks. In the case of the behavioral variant of frontotemporal dementia, this involves the brain’s salience network, which explains the social-emotional features of the illness. In the case of Alzheimer’s disease, the featured network is the brain’s default mode network or, more generally, the brain’s hippocampal-cortical memory network. Again, this explains nicely the severe memory deficits encountered in persons with Alzheimer’s disease.

    The reason why these diseases originate and progress within specific brain networks is a critical unanswered question, but that does not preclude using this information to predict disease progression. However, one of the challenges in using this information is our ability to relate it to individual patients. Most brain-imaging studies with fMRI are based on large group averages. While this provides valuable data, those data are not easily extended to individual patients, where individual differences become an important consideration.

    This paper presents and convincingly defends a novel approach to monitoring changes within specific brain networks over time in individual subjects. The value of this approach is that it provides a means of monitoring the effect of interventions in individual subjects using an approach that is generally available. With such a tool in hand, our challenge is to identify effective interventions!

    View all comments by Marcus Raichle

  5. The paper by Brown et al. tackles one of the biggest challenges in neurodegenerative disease: resolving the incredible heterogeneity of disease trajectories in dementia, both across and within syndromes. No two patients will show the same disease trajectory, making accurate diagnosis difficult and prognosis anyone’s guess. Jesse Brown and colleagues elegantly combined well-established neuroimaging approaches to develop a predictive model of atrophy spread.

    Importantly, their patient-tailored approach is a significant step forward compared with previous group-level analyses, and adds to recent investigations of a similar scope (Schmidt et al., 2016; Raj et al., 2012; Weickenmeier et al., 2018). Informed by the network degeneration theory and the epicenter model, the authors test the hypothesis that disease pathology in FTD spreads through functionally connected brain regions, derived from the healthy rs-fMRI connectome. The model performed well overall but failed to predict trajectories of atrophy in a moderate proportion of cases (27/152). Most of these cases were not severe or did not conform to prototypical disease presentations (i.e., C9ORF72 mutations carriers). As noted by the authors, further refinements to this model are needed to reduce the heterogeneity and explain the variance associated with brain trajectories in atypical cases.

    An alternative approach to the healthy rs-fMRI connectome is to use patient-derived anatomical connectivity measures. Indeed, recent developments in DWI analysis, such as track-weighted imaging methods (Calamante, 2017; Raffelt et al., 2017), provide a powerful means to study structural and functional connectivity simultaneously. Structural MRI methods require less abstraction, have clearer biological correlate and can be measured against the gold standard of molecular pathology in postmortem tissue. The epicenter model as a starting point of the disease does not fully address the cascade of molecular, metabolic, vascular, and functional changes that begins years before emergence of clinical syndromes. Once atrophy reaches a significant magnitude, the disease may have progressed too far for disease-modifying treatments to be effective.

    This work has the major advantage of only requiring a baseline MRI scan, which is routinely acquired as part of a clinical assessment. Further, since most neurodegenerative conditions show progressive brain atrophy, the method presented here has promising applications beyond FTD. Although atrophy provides a good proxy of underlying pathology, the precise nature of their relationship is yet to be uncovered. In particular, FTD is associated with incredibly heterogeneous pathologies, often coexisting. Therefore, mapping atrophy alone (even at the individual level), though important for disease monitoring, cannot effectively inform treatment development. As such, while this paper provides important avenues for development of clinical trials, further research, including other biomarkers of pathology, is needed.  

    In my opinion, where this paper makes a significant contribution is in informing disease staging and prognosis for individual patients. The emergence of particular clinical syndromes follows closely the location and temporal properties of underlying brain atrophy. While we are still far from adapting these neuroimaging approaches into clinical practice, this paper represents a significant step forward and a proof of concept of patient-tailored prediction of disease progression.

    References:

    Schmidt R, de Reus MA, Scholtens LH, van den Berg LH, van den Heuvel MP. Simulating disease propagation across white matter connectome reveals anatomical substrate for neuropathology staging in amyotrophic lateral sclerosis. Neuroimage. 2015 Apr 11;124(Pt A):762-769. PubMed.

    Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron. 2012 Mar 22;73(6):1204-15. PubMed.

    Weickenmeier J, Kuhl E, Goriely A. Multiphysics of Prionlike Diseases: Progression and Atrophy. Phys Rev Lett. 2018 Oct 12;121(15):158101. PubMed.

    Calamante F. Track-weighted imaging methods: extracting information from a streamlines tractogram. MAGMA. 2017 Aug;30(4):317-335. Epub 2017 Feb 8 PubMed.

    Raffelt DA, Tournier JD, Smith RE, Vaughan DN, Jackson G, Ridgway GR, Connelly A. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage. 2017 Jan 1;144(Pt A):58-73. Epub 2016 Sep 14 PubMed.

    View all comments by Yuichi Higashiyama

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  1. Imaging Model Locates Epicenter of Disease, Predicts Atrophy