Hippocampal and insula volume in mild cognitive impairment with Lewy bodies

Firbank, Michael; Durcan, Rory; O’Brien, John; Allan, Louise; Barker, Sally; Ciafone, Joanna; Donaghy, Paul C.; Hamilton, Calum A; Lawley, Sarah; Roberts, Gemma; Taylor, John‐Paul; Thomas, Alan

doi:10.1016/j.parkreldis.2021.03.011

Cited by 13 publications

(17 citation statements)

References 29 publications

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“…The smaller volume of the insula in iRBD was also found to discriminate between iRBD and PD patients. In the present study, greater thinning in the left insula was observed in iRBD patients with MCI, and the presence of MCI in iRBD is a risk factor for DLB (5,6,8), which is in agreement with studies reporting the insula as highly associated with prodromal and clinical DLB (51–54). Its ability to discriminate between iRBD and PD may therefore be due to some iRBD patients being on a dementia-first trajectory.…”

Section: Discussionsupporting

confidence: 93%

Identification of REM Sleep Behavior Disorder by Magnetic Resonance Imaging and Machine Learning

Mei

Rahayel

Desrosiers

et al. 2021

Preprint

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Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a major risk factor for synucleinopathies, and patients often present with clinical signs and morphological brain changes. However, there is an important heterogeneity in the presentation and progression of these alterations, and the brain regions that are more vulnerable to neurodegeneration remain to be determined. Objectives To assess the feasibility of morphology-based machine learning approaches in the identification and subtyping of iRBD. Methods For the three classification tasks [iRBD (n=48) vs controls (n=41); iRBD vs Parkinson's disease (n=29); iRBD with mild cognitive impairment (n=16) vs without mild cognitive impairment (n=32)], machine learning models were trained with morphometric measurements (thickness, surface area, volume, and deformation) extracted from T1- weighted structural magnetic resonance imaging. Model performance and the most discriminative brain regions were analyzed and identified. Results A high accuracy was reported for iRBD vs controls (79.6%, deformation of the caudal middle frontal gyrus and putamen, thinning of the superior frontal gyrus, and reduced volume of the inferior parietal cortex and insula), iRBD vs Parkinson's disease (82%, larger volume and surface area of the insula, thinning of the entorhinal cortex and lingual gyrus, and reduced volume of the fusiform gyrus), and iRBD with vs without mild cognitive impairment (84.8%, thinning of the pars triangularis, superior temporal gyrus, transverse temporal cortex, larger surface area of the superior temporal gyrus, and deformation of isthmus of the cingulate gyrus). Conclusions Morphology-based machine learning approaches may allow for automated detection and subtyping of iRBD, potentially enabling efficient preclinical identification of synucleinopathies.

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Section: Discussionsupporting

confidence: 93%

Identification of REM Sleep Behavior Disorder by Magnetic Resonance Imaging and Machine Learning

Mei

Rahayel

Desrosiers

et al. 2021

Preprint

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“…After participants provided consent, they underwent a research-level assessment involving a semistructured interview; clinical and neurocognitive assessment and neurological examination by a medical doctor (R.D., S.L., and P.C.D. ); and imaging with FP-CIT, MIBG (second cohort only), and structural neuroimaging [18][19][20] at baseline and then had longitudinal review at approximately annual follow-ups.…”

Section: Clinical Assessments and Imagingmentioning

confidence: 99%

A Longitudinal Study of Plasma pTau181 in Mild Cognitive Impairment with Lewy Bodies and Alzheimer's Disease

et al. 2022

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A BS TRACT:Background: Alzheimer's disease (AD) co-pathology is common in dementia with Lewy bodies and is associated with increased decline. Plasma pTau181 is a blood-based biomarker that can detect AD co-pathology. Objectives: We investigated whether pTau181 was associated with cognitive decline in mild cognitive impairment with Lewy bodies (MCI-LB) and MCI with AD (MCI-AD). Methods: We assessed plasma pTau181 using a singlemolecule array (Simoa) immunoassay at baseline and follow-up in a longitudinal cohort of MCI-LB, MCI-AD, and controls. Results: One hundred forty-six subjects (56 probable MCI-LB, 22 possible MCI-LB, 44 MCI-AD, and 24 controls) were reviewed for up to 5.7 years. Probable MCI-LB had significantly higher pTau181 (22.2% mean increase) compared with controls and significantly lower (24.4% mean decrease) levels compared with MCI-AD. Receiver operating characteristic analyses of pTau181 in discriminating probable MCI-LB from controls showed an area under the curve (AUC) of 0.68 (83% specificity, 57% sensitivity); for discriminating MCI-AD from healthy controls, AUC was 0.8 (83.3% specificity, 72.7% sensitivity). pTau181 concentration was less useful in discriminating between probable MCI-LB and MCI-AD: AUC of 0.64 (71.4% specificity, 52.3% sensitivity). There was an association between pTau181 and cognitive decline in MCI-AD but not in MCI-LB. In a subset with repeat samples there was a nonsignificant 3% increase per follow-up year in plasma pTau181. The rate of change in pTau181 was not significantly different in different diagnostic subgroups. Conclusions: pTau181 was not associated with an increased decline assessed using either baseline or repeat pTau181. pTau181 partially discriminated probable MCI-LB from controls and MCI-AD from controls but was not useful in distinguishing probable MCI-LB from

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“…Following consent participants underwent a research-level assessment involving a semistructured interview, clinical and neurocognitive assessment and neurological examination by a medical doctor (RD ans SL), and imaging with FP-CIT, MIBG, and MRI (Firbank et al, 2021;Roberts et al, 2021a;2021b) at baseline, and then had longitudinal review at approximately annual follow-ups. Mean (SD) of maximum follow-up were 1.4 (0.98) years, with a maximum of 3.7 years from baseline.…”

Section: Participantsmentioning

confidence: 99%

Olfactory impairment in mild cognitive impairment with Lewy bodies and Alzheimer’s disease

Thomas

Hamilton

Barker

et al. 2022

International Psychogeriatrics

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Objectives: Impaired olfaction may be a biomarker for early Lewy body disease, but its value in mild cognitive impairment with Lewy bodies (MCI-LB) is unknown. We compared olfaction in MCI-LB with MCI due to Alzheimer’s disease (MCI-AD) and healthy older adults. We hypothesized that olfactory function would be worse in probable MCI-LB than in both MCI-AD and healthy comparison subjects (HC). Design: Cross-sectional study assessing olfaction using Sniffin’ Sticks 16 (SS-16) in MCI-LB, MCI-AD, and HC with longitudinal follow-up. Differences were adjusted for age, and receiver operating characteristic (ROC) curves were used for discriminating MCI-LB from MCI-AD and HC. Setting: Participants were recruited from Memory Services in the North East of England. Participants: Thirty-eight probable MCI-LB, 33 MCI-AD, 19 possible MCI-LB, and 32HC. Measurements: Olfaction was assessed using SS-16 and a questionnaire. Results: Participants with probable MCI-LB had worse olfaction than both MCI-AD (age-adjusted mean difference (B) = 2.05, 95% CI: 0.62–3.49, p = 0.005) and HC (B = 3.96, 95% CI: 2.51–5.40, p < 0.001). The previously identified cutoff score for the SS-16 of ≤ 10 had 84% sensitivity for probable MCI-LB (95% CI: 69–94%), but 30% specificity versus MCI-AD. ROC analysis found a lower cutoff of ≤ 7 was better (63% sensitivity for MCI-LB, with 73% specificity vs MCI-AD and 97% vs HC). Asking about olfactory impairments was not useful in identifying them. Conclusions: MCI-LB had worse olfaction than MCI-AD and normal aging. A lower cutoff score of ≤ 7 is required when using SS-16 in such patients. Olfactory testing may have value in identifying early LB disease in memory services.

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Hippocampal and insula volume in mild cognitive impairment with Lewy bodies

Cited by 13 publications

References 29 publications

Identification of REM Sleep Behavior Disorder by Magnetic Resonance Imaging and Machine Learning

Identification of REM Sleep Behavior Disorder by Magnetic Resonance Imaging and Machine Learning

A Longitudinal Study of Plasma pTau181 in Mild Cognitive Impairment with Lewy Bodies and Alzheimer's Disease

Olfactory impairment in mild cognitive impairment with Lewy bodies and Alzheimer’s disease

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