Cognitive and motor correlates of grey and white matter pathology in Parkinson’s disease

Dadar, Mahsa; Gee, Myrlene; Shuaib, Ashfaq; Duchesne, Simon; Camicioli, Richard

doi:10.1016/j.nicl.2020.102353

Cited by 39 publications

(40 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that subcortical structures and WMHs have contrasting intensity profiles in T2w and FLAIR images (i.e., hypointense vs. hyperintense, respectively) and similar intensity profiles in T1w images (hypointense for both) makes it unlikely that they would be incorrectly classified as WMHs, as the WMH segmentations are mainly driven by the intensity profile in T2w and FLAIR sequences. Additionally, the WMH segmentation method used here has been developed and extensively validated for use in multi‐site and multi‐scanner studies, and has been previously used in several multi‐site datasets, including ADNI (Anor, Dadar, Collins, & Tartaglia, 2020; Dadar et al, 2019; Dadar et al, 2020; Dadar, Camicioli, Duchesne, Collins, & Initiative, 2020; Dadar, Gee, Shuaib, Duchesne, & Camicioli, 2020; Misquitta et al, 2018; Sanford et al, 2019). The training library used consists of manually segmented labels from the same dataset (ADNI), to ensure optimal classifier performance (Dadar, Maranzano, Misquitta, et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Dadar¹,

Potvin²,

Camicioli

et al. 2021

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Volumetric estimates of subcortical and cortical structures, extracted from T1‐weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1‐weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2‐weighted or Fluid‐attenuated inversion recovery images. Mixed‐effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age‐matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Dadar¹,

Potvin²,

Camicioli

et al. 2021

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…These processing methods have previously shown patterns of atrophy in cognitively normal, MCI, dementia, and neurodegenerative disease populations, including ADNI (37)(38)(39)(40). These techniques thus have the required sensitivity to reveal group differences between SCD-and SCD+.…”

Section: Discussionmentioning

confidence: 99%

Regional atrophy and cognitive decline depend on definition of subjective cognitive decline

Morrison

Dadar

Shafiee

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundPeople with subjective cognitive decline (SCD) may be at increased risk for Alzheimer’s disease (AD). However, not all studies have observed this increased risk. Inconsistencies may be related to different methods used to define SCD. The current project examined whether four methods of defining SCD (applied to the same sample) results in different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+) and without SCD (SCD-).MethodsMRI scans and questionnaire data for 273 cognitively normal older adults from Alzheimer’s Disease Neuroimaging Initiative were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry only. A previously validated MRI analysis method (SNIPE) was used to measure hippocampal volume and grading. Deformation-based morphometry was performed to examine volume at regions known to be vulnerable to AD. Logistic regressions were completed to determine whether diagnostic method was associated with volume differences between SCD- and SCD+. Linear mixed effects models were performed to examine the relationship between the definitions of SCD and future cognitive decline.ResultsResults varied between the four methods of defining SCD. Left hippocampal grading was lower in SCD+ than SCD-when using the CCI (p=.041) and Worry (p=.021) definitions. The right (p=.008) and left (p=.003) superior temporal regions were smaller in SCD+ than SCD-, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer’s Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment.ConclusionThe current findings suggest that the various methods used to differentiate between SCD- and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.

show abstract

“…In the current sample, SN DBM was associated with future FOG, further demonstrating the value of assessing SN DBM as a T1w-based marker of PD pathology in future studies. WMH burden is associated with gait deficits both in the aging population in general and in PD patients specifically 7,17,[20][21][22]51,52 . Given that WMH load is also associated with CSF amyloid β levels 31,32,57,35 and they both impact gait in PD, we hypothesized that they might impact future FOG through the same pathway.…”

Section: Discussionmentioning

confidence: 99%

“…Of interest to our study, white matter hyperintensities (WMHs) are areas of increased signal on T2weighted (T2w) magnetic resonance imaging (MRI) sequences, commonly observed in the aging population due to an interplay of ischemic, inflammatory, and protein deposition processes 15,16 . In addition to cognitive impairment and executive dysfunction, WMHs have been associated with rigidity and gait impairment in individuals with 7,[17][18][19][20][21][22][23] and without PD [24][25][26][27][28][29][30] . Interestingly, amyloid β deposition has further been linked to an increase in the WMH burden through acceleration of processes such as neuroinflammation, reactive oxygen species production, and oxidative stress [31][32][33][34][35] .…”

Section: Introductionmentioning

confidence: 99%

Amyloid ß Impacts Future Freezing of Gait in Parkinson’s Disease Via White Matter Hyperintensities

Dadar

Duchesne

Camicioli

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Freezing of gait (FOG) is a common symptom in Parkinsons Disease (PD) patients. Previous studies have reported relationships between FOG, substantia nigra (SN) degeneration, dopamine transporter (DAT) concentration, as well as amyloid β deposition. However, there is a paucity of research on the concurrent impact of white matter damage. Objectives: To assess the inter-relationships between these different co-morbidities, their impact on future FOG and whether they act independently of each other. Methods: We used baseline MRI and longitudinal gait data from the Parkinson's Progression Markers Initiative (PPMI). We used deformation based morphometry (DBM) from T1-weighted MRI to measure SN atrophy, and segmentation of white matter hyperintensities (WMH) as a measure of WM pathological load. Putamen and caudate DAT levels from SPECT as well as cerebrospinal fluid (CSF) amyloid β were obtained directly from the PPMI. Following correlation analyses, we investigated whether WMH burden mediates the impact of amyloid β on future FOG. Results: SN DBM, WMH load, putamen and caudate DAT activity and CSF amyloid β levels were significantly different between PD patients with and without future FOG (p < 0.008). Mediation analysis demonstrated an effect of CSF amyloid β levels on future FOG via WMH load, independent of SN atrophy and striatal DAT activity levels. Conclusions: Amyloid β might impact future FOG in PD patients through an increase in WMH burden, in a pathway independent of Lewy body pathology.

show abstract

Cognitive and motor correlates of grey and white matter pathology in Parkinson’s disease

Cited by 39 publications

References 54 publications

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations!

Regional atrophy and cognitive decline depend on definition of subjective cognitive decline

Amyloid ß Impacts Future Freezing of Gait in Parkinson’s Disease Via White Matter Hyperintensities

Contact Info

Product

Resources

About