Abstract

Recent studies suggest a framework where white matter (WM) atrophy plays an important role in fronto-temporal dementia (FTD) pathophysiology. However, these studies often overlook the fact that WM tracts bridging different brain regions may have different vulnerabilities to the disease and the relative contribution of GM atrophy to this WM model, resulting in a less comprehensive understanding of the relationship between clinical symptoms and pathology. Using a common factor analysis to extract a semantic and an executive factor, we aimed to test the relative contribution of WM and GM of specific tracts in predicting cognition in the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI). We found that semantic symptoms were mainly dependent on short-range WM fiber disruption, while damage to long-range WM fibers was preferentially associated to executive dysfunction with the GM contribution to cognition being predominant for local processing. These results support the importance of the disruption of specific WM tracts to the core cognitive symptoms associated with FTD. As large-scale WM tracts, which are particularly vulnerable to vascular disease, were highly associated with executive dysfunction, our findings highlight the importance of controlling for risk factors associated with deep white matter disease, such as vascular risk factors, in patients with FTD in order not to potentiate underlying executive dysfunction.

Data availability

All data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) and are available through the LONI portal (http://adni.loni.usc.edu). FTLDNI is a multicentric longitudinal database, collecting MRIs, PET and CSF biomarkers in FTD patients and age-matched controls.

The following previously published data sets were used
    1. Howard Rosen
    (2010) FTLDNI
    http://4rtni-ftldni.ini.usc.edu/.

Article and author information

Author details

  1. Melissa Savard

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  2. Tharick A Pascoal

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Stijn Servaes

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Thijs Dhollander

    Developmental Imaging, Murdoch Children's Research Institute, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3088-3636
  5. Yasser Iturria-Medina

    Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9345-0347
  6. Min Su Kang

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  7. Paolo Vitali

    Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  8. Joseph Therriault

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  9. Sulantha Mathotaarachchi

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9391-4503
  10. Andrea Lessa Benedet

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  11. Serge Gauthier

    Department of Psychiatry, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  12. Pedro Rosa-Neto

    Translational Neuroimaging Laboratory, McGill University, Montreal, Canada
    For correspondence
    pedro.rosa@mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9116-1376

Funding

National Institutes of Health (R01 AG032306)

  • Pedro Rosa-Neto

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI), through the LONI portal (http://adni.loni.usc.edu). FTLDNI is a multicentric longitudinal database, collecting MRIs, PET and CSF biomarkers in FTD patients and age-matched controls. The investigators at NIFD/FTLDNI contributed to the design and implementation of FTLDNI and/or provided data, but did not participate in the analysis or writing of this report.

Copyright

© 2022, Savard et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 750
    views
  • 108
    downloads
  • 9
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Melissa Savard
  2. Tharick A Pascoal
  3. Stijn Servaes
  4. Thijs Dhollander
  5. Yasser Iturria-Medina
  6. Min Su Kang
  7. Paolo Vitali
  8. Joseph Therriault
  9. Sulantha Mathotaarachchi
  10. Andrea Lessa Benedet
  11. Serge Gauthier
  12. Pedro Rosa-Neto
(2022)
Impact of long- and short-range fiber depletion on the cognitive deficits of fronto-temporal dementia
eLife 11:e73510.
https://doi.org/10.7554/eLife.73510

Share this article

https://doi.org/10.7554/eLife.73510

Further reading

    1. Neuroscience
    Paul I Jaffe, Gustavo X Santiago-Reyes ... Russell A Poldrack
    Research Article

    Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.

    1. Neuroscience
    Aneri Soni, Michael J Frank
    Research Article

    How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here, we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such ‘chunking’ strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinson’s disease, ADHD, and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.