Multivariate classification of patients with Alzheimer’s and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study

2012 ◽  
Vol 260 (4) ◽  
pp. 1104-1115 ◽  
Author(s):  
Alexander V. Lebedev ◽  
E. Westman ◽  
M. K. Beyer ◽  
M. G. Kramberger ◽  
C. Aguilar ◽  
...  
2019 ◽  
Vol 54 (6) ◽  
pp. 633-643
Author(s):  
Sean J Colloby ◽  
Rosie Watson ◽  
Andrew M Blamire ◽  
John T O’Brien ◽  
John-Paul Taylor

Background: We investigated the structural changes associated with Alzheimer’s disease, dementia with Lewy bodies and Parkinson disease dementia by means of cortical thickness analysis. Methods: Two hundred and forty-five participants: 76 Alzheimer’s disease, 65 dementia with Lewy bodies, 29 Parkinson disease dementia and 76 cognitively normal controls underwent 3-T T1-weighted magnetic resonance imaging and clinical and cognitive assessments. We implemented FreeSurfer to obtain cortical thickness estimates to contrast patterns of cortical thinning across groups and their clinical correlates. Results: In Alzheimer’s disease and dementia with Lewy bodies, a largely similar pattern of regional cortical thinning was observed relative to controls apart from a more severe loss within the entorhinal and parahippocampal structures in Alzheimer’s disease. In Parkinson disease dementia, regional cortical thickness was indistinguishable from controls and dementia with Lewy bodies, suggesting an ‘intermediate’ pattern of regional cortical change. In terms of global cortical thickness, group profiles were controls > Parkinson disease dementia > dementia with Lewy bodies > Alzheimer’s disease (F3, 241 ⩽ 123.2, p < 0.001), where percentage wise, the average difference compared to controls were −1.8%, −5.5% and −6.4%, respectively. In these samples, cortical thinning was also associated with cognitive decline in dementia with Lewy bodies but not in Parkinson disease dementia and Alzheimer’s disease. Conclusion: In a large and well-characterised cohort of people with dementia, regional cortical thinning in dementia with Lewy bodies was broadly similar to Alzheimer’s disease. There was preservation of the medial temporal lobe structures in dementia with Lewy bodies compared with Alzheimer’s disease, supporting its inclusion as a supportive biomarker in the revised clinical criteria for dementia with Lewy bodies. However, there was less global cortical thinning in Parkinson disease dementia, with no significant regional difference between Parkinson disease dementia and controls. These findings highlight the overlap across the Alzheimer’s disease/Parkinson disease dementia spectrum and the potential for differing mechanisms underlying neurodegeneration and cognition in dementia with Lewy bodies and Parkinson disease dementia.


Neurology ◽  
2012 ◽  
Vol 79 (6) ◽  
pp. 553-560 ◽  
Author(s):  
K. Kantarci ◽  
T. J. Ferman ◽  
B. F. Boeve ◽  
S. D. Weigand ◽  
S. Przybelski ◽  
...  

2007 ◽  
Vol 15 (11) ◽  
pp. 961-967 ◽  
Author(s):  
Yasuhiro Nagahama ◽  
Tomoko Okina ◽  
Norio Suzuki ◽  
Minoru Matsuda ◽  
Kenjiro Fukao ◽  
...  

Neurology ◽  
2011 ◽  
Vol 77 (9) ◽  
pp. 875-882 ◽  
Author(s):  
T. J. Ferman ◽  
B. F. Boeve ◽  
G. E. Smith ◽  
S.- C. Lin ◽  
M. H. Silber ◽  
...  

Neurology ◽  
2017 ◽  
Vol 89 (4) ◽  
pp. 318-326 ◽  
Author(s):  
Vincenzo Donadio ◽  
Alex Incensi ◽  
Giovanni Rizzo ◽  
Sabina Capellari ◽  
Roberta Pantieri ◽  
...  

Objective:To investigate whether (1) phosphorylated α-synuclein (p-syn) deposits in skin nerves could be useful in differentiating dementia with Lewy bodies (DLB) from different forms of dementia and (2) small fiber neuropathy (SFN) is associated with DLB.Methods:We studied 18 well-characterized patients with DLB (11 with autonomic dysfunction), 23 patients with nonsynucleinopathy dementia (NSD; 13 with young-onset Alzheimer disease dementia, 6 frontotemporal dementia, and 4 vascular dementia), and 25 healthy controls. All participants underwent skin biopsies from proximal (i.e., cervical) and distal (i.e., thigh and distal leg) sites to study small nerve fibers and deposits of p-syn, considered the pathologic form of α-synuclein.Results:No p-syn was detected in any skin sample in patients with NSD and controls but was found in all patients with DLB. SFN was found in patients with DLB and the autonomic denervation of skin was more severe in patients with autonomic dysfunctions.Conclusions:(1) In autonomic skin nerves, p-syn is a sensitive biomarker for DLB diagnosis, helping to differentiate DLB from other forms of dementia, although this needs to be confirmed in a larger, more representative sample; and (2) skin autonomic neuropathy is part of the DLB pathology and may contribute to autonomic symptoms.Classification of evidence:This study provides Class III evidence that p-syn in skin nerve fibers on skin biopsy accurately distinguishes DLB from other forms of dementia.


2021 ◽  
Vol 11 (3) ◽  
pp. 7135-7139
Author(s):  
G. Anuradha ◽  
D. N. Jamal

Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods.


PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0127396 ◽  
Author(s):  
Frederic Blanc ◽  
Sean J. Colloby ◽  
Nathalie Philippi ◽  
Xavier de Pétigny ◽  
Barbara Jung ◽  
...  

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