Network structure of brain atrophy in de novo Parkinson's disease

Zeighami, Yashar; Ulla, Miguel; Iturria-Medina, Yasser; Dadar, Mahsa; Zhang, Yu; Larcher, Kevin; Fonov, Vladimir S.; Evans, Alan C.; Collins, D. Louis; Dagher, Alain

doi:10.7554/elife.08440

Cited by 207 publications

(309 citation statements)

References 74 publications

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“…Multiple neurodegenerative diseases, including Alzheimer's and Parkinson's diseases, are increasingly conceptualized as arising from trans-neuronal spread of pathogenic misfolded proteins [36,74]. As a result, patterns of neurodegeneration resemble the underlying structural and functional architecture, and are often centered on one or more specific epicenters [60,77,80].…”

Section: Introductionmentioning

confidence: 99%

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Shafiei

Markello

Talpalaru

et al. 2019

Preprint

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∀These authors contributed equally to this work. AbstractBackground. There is growing recognition that connectome architecture shapes cortical and subcortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown. Methods. Here we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 ± 12.9 years) and N = 113 controls (64 female, 23.5 ± 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI. Results. We find that regional deformation is correlated with the deformation of structurally-and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex. Conclusions. Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation. Keywords. connectome | schizophrenia | intrinsic networks | disease epicenter | anterior cingulate | ventral attention network Running title. Network-driven tissue volume loss in schizophrenia

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Section: Introductionmentioning

confidence: 99%

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Shafiei

Markello

Talpalaru

et al. 2019

Preprint

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“…Later, as pathology involves the neocortices including higher association areas, individuals with PD and "normal" cognition (PD-N) may cross a barrier of barely detectable cognitive problems and develop neuropsychologically classifiable mild cognitive impairment (PD-MCI) and eventually the full picture of dementia (PDD) (Braak et al, 2003;Braak & Del Tredici, 2017). A large body of in vivo evidence supports the staging system (Zarei et al, 2013;Zeighami et al, 2015), although it is not without controversy (Surmeier, Obeso, & Halliday, 2017;Walsh & Selkoe, 2016). By definition, longitudinal studies from postmortem material are not possible, hence, tracing the temporal dynamics of pathology patterns in vivo together with clinical features becomes increasingly important (Fereshtehnejad, Zeighami, Dagher, & Postuma, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…By definition, longitudinal studies from postmortem material are not possible, hence, tracing the temporal dynamics of pathology patterns in vivo together with clinical features becomes increasingly important (Fereshtehnejad, Zeighami, Dagher, & Postuma, 2017). The regional pattern of cortical brain atrophy in PD remarkably resembles the spatial distribution of cognition-related "resting-state" fMRI networks (Zeighami et al, 2015), and the atrophy distribution appears to be predicted by hyperconnective pathways (Yau et al, 2018). Following longitudinal studies in PD patients that demonstrated a regionspecific accelerated cortical thinning (Mak et al, 2015), we were encouraged to test the hypothesis whether the rate of volumetric changes and cortical thinning over time allows for the definition of a cognitive status-dependent pattern in advanced PD.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal brain atrophy distribution in advanced Parkinson's disease: What makes the difference in “cognitive status” converters?

Gorges

Kunz

Müller

et al. 2019

Human Brain Mapping

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We investigated the brain atrophy distribution pattern and rate of regional atrophy change in Parkinson's disease (PD) in association with the cognitive status to identify the morphological characteristics of conversion to mild cognitive impairment (MCI) and dementia (PDD). T1‐weighted longitudinal 3T MRI data (up to four follow‐up assessments) from neuropsychologically well‐characterized advanced PD patients (n = 172, 8.9 years disease duration) and healthy elderly controls (n = 85) enrolled in the LANDSCAPE study were longitudinally analyzed using a linear mixed effect model and atlas‐based volumetry and cortical thickness measures. At baseline, PD patients presented with cerebral atrophy and cortical thinning including striatum, temporoparietal regions, and primary/premotor cortex. The atrophy was already observed in “cognitively normal” PD patients (PD‐N) and was considerably more pronounced in cognitively impaired PD patients. Linear mixed effect modeling revealed almost similar rates of atrophy change in PD and controls. The group comparison at baseline between those PD‐N whose cognitive performance remained stable (n = 42) and those PD‐N patients who converted to MCI/PDD (“converter” cPD‐N, n = 26) indicated suggested cortical thinning in the anterior cingulate cortex in cPD‐N patients which was correlated with cognitive performance. Our results suggest that cortical brain atrophy has been already expanded in advanced PD patients without overt cognitive deficits while atrophy progression in late disease did not differ from “normal” aging regardless of the cognitive status. It appears that cortical atrophy begins early and progresses already in the initial disease stages emphasizing the need for therapeutic interventions already at disease onset.

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“…Interestingly, a recent structural connectivity study based on voxel deformation reported a basal ganglia structural network deficiency in PD in which the degree of GM loss was linked to motor severity [Chou et al, ]. More recently, large structural covariance network [Zeighami et al, ] and functional connectivity analyses [O'Callaghan et al, ] were successfully applied to characterise striatal and cerebellar network change in PD. No studies of PD related limbic structural connectivity alterations are available, despite the early pathophysiological involvement of the limbic system and the putative link with non‐motor symptoms.…”

Section: Introductionmentioning

confidence: 99%

Limbic grey matter changes in early Parkinson's disease

Xing

Schwarz

et al. 2017

Human Brain Mapping

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The purpose of this study was to investigate local and network-related changes of limbic grey matter in early Parkinson's disease (PD) and their inter-relation with non-motor symptom severity. We applied voxel-based morphometric methods in 538 T1 MRI images retrieved from the Parkinson's Progression Markers Initiative website. Grey matter densities and cross-sectional estimates of age-related grey matter change were compared between subjects with early PD (n 5 366) and age-matched healthy controls (n 5 172) within a regression model, and associations of grey matter density with symptoms were investigated. Structural brain networks were obtained using covariance analysis seeded in regions showing grey matter abnormalities in PD subject group. Patients displayed focally reduced grey matter density in the right amygdala, which was present from the earliest stages of the disease without further advance in mild-moderate disease stages. Right amygdala grey matter density showed negative correlation with autonomic dysfunction and positive with cognitive performance in patients, but no significant interrelations were found with anxiety scores. Patients with PD also demonstrated right amygdala structural disconnection with less structural connectivity of the right amygdala with the cerebellum and thalamus but increased covariance with bilateral temporal cortices compared with controls. Age-related grey matter change was also increased in PD preferentially in the limbic system. In conclusion, detailed brain morphometry in a large group of early PD highlights predominant limbic grey matter deficits with stronger age associations compared with controls and associated altered structural connectivity pattern. This provides in vivo evidence for early limbic grey matter pathology and structural network changes that may reflect extranigral disease spread in PD. Hum Brain Mapp 38:3566-3578, 2017.

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Network structure of brain atrophy in de novo Parkinson's disease

Cited by 207 publications

References 74 publications

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Longitudinal brain atrophy distribution in advanced Parkinson's disease: What makes the difference in “cognitive status” converters?

Limbic grey matter changes in early Parkinson's disease

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