Abstract

We mapped the distribution of atrophy in Parkinson's Disease (PD) using MRI and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.

Article and author information

Author details

  1. Yashar Zeighami

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  2. Miguel Ulla

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Yasser Iturria-Medina

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Mahsa Dadar

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Yu Zhang

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  6. Kevin Michel-Herve Larcher

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  7. Vladimir Fonov

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  8. Alan C Evans

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  9. Douglas Louis Collins

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  10. Alain Dagher

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    For correspondence
    alain.dagher@mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. David C Van Essen, Washington University in St Louis, United States

Ethics

Human subjects: For the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data)Each participating PPMI site received approval from a local research ethics committee before study initiation, and obtained written informed consent from all subjects participating in the study.For the resting state fMRI data collected in our lab, We acquired resting state fMRI in 51 healthy, right-handed volunteers.The experimental protocol was reviewed and approved by Research Ethics Board of Montreal Neurological Institute. All subjects gave informed consent.

Version history

  1. Received: April 30, 2015
  2. Accepted: September 5, 2015
  3. Accepted Manuscript published: September 7, 2015 (version 1)
  4. Version of Record published: October 8, 2015 (version 2)

Copyright

© 2015, Zeighami 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

  • 6,897
    views
  • 1,338
    downloads
  • 184
    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. Yashar Zeighami
  2. Miguel Ulla
  3. Yasser Iturria-Medina
  4. Mahsa Dadar
  5. Yu Zhang
  6. Kevin Michel-Herve Larcher
  7. Vladimir Fonov
  8. Alan C Evans
  9. Douglas Louis Collins
  10. Alain Dagher
(2015)
Network structure of brain atrophy in de novo Parkinson's Disease
eLife 4:e08440.
https://doi.org/10.7554/eLife.08440

Share this article

https://doi.org/10.7554/eLife.08440

Further reading

    1. Neuroscience
    Hao Li, Jingyu Feng ... Jufang He
    Research Article

    Cholecystokinin (CCK) is an essential modulator for neuroplasticity in sensory and emotional domains. Here, we investigated the role of CCK in motor learning using a single pellet reaching task in mice. Mice with a knockout of Cck gene (Cck−/−) or blockade of CCK-B receptor (CCKBR) showed defective motor learning ability; the success rate of retrieving reward remained at the baseline level compared to the wildtype mice with significantly increased success rate. We observed no long-term potentiation upon high-frequency stimulation in the motor cortex of Cck−/− mice, indicating a possible association between motor learning deficiency and neuroplasticity in the motor cortex. In vivo calcium imaging demonstrated that the deficiency of CCK signaling disrupted the refinement of population neuronal activity in the motor cortex during motor skill training. Anatomical tracing revealed direct projections from CCK-expressing neurons in the rhinal cortex to the motor cortex. Inactivation of the CCK neurons in the rhinal cortex that project to the motor cortex bilaterally using chemogenetic methods significantly suppressed motor learning, and intraperitoneal application of CCK4, a tetrapeptide CCK agonist, rescued the motor learning deficits of Cck−/− mice. In summary, our results suggest that CCK, which could be provided from the rhinal cortex, may surpport motor skill learning by modulating neuroplasticity in the motor cortex.

    1. Neuroscience
    Ivan Tomić, Paul M Bays
    Research Article

    Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.