Inclusion of RBD improves the diagnostic classification of dementia with Lewy bodies

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.

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Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.4112 ◽

2021 ◽

Vol 11 (3) ◽

pp. 7135-7139

Author(s):

G. Anuradha ◽

D. N. Jamal

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dementia With Lewy Bodies ◽

Lewy Bodies ◽

Normal Subjects ◽

Dominant Frequency ◽

Abnormal Behavior ◽

Feed Forward ◽

Artificial Neural

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.

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