Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer’s disease patients

Peraza, Luis R.; Cromarty, Ruth; Kobeleva, Xenia; Firbank, Michael; Killen, Alison; Graziadio, Sara; Thomas, Alan; O’Brien, John; Taylor, John‐Paul

doi:10.1038/s41598-018-22984-5

Cited by 57 publications

(127 citation statements)

References 64 publications

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“…This might have partially restored the EEG activity towards healthier values (Agnoli, Martucci, Manna, Conti, & Fioravanti, ; Balkan et al, ). Nevertheless, we found significant alterations across patient groups and our findings resonated with results from previous studies (Peraza et al, ; Stam et al, ).…”

Section: Discussionsupporting

confidence: 92%

“…In particular, the metric that mostly drove the classification was WPLI in the β band. As mentioned above, this may highlight the role of the randomisation of the network in LBDs associated with the pathology (Peraza et al, ).…”

Section: Discussionmentioning

confidence: 86%

“…As mentioned earlier, this approach leads to fully connected weighted graphs (Stam et al, ), resulting in full sized networks. In fact, Peraza et al () performed a study using the MST on the same cohort of participants. However, although the differences in connectivity strength are comparable with our findings, the MST approach does not provide local alterations within the network architecture, which we found significant in patient groups.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, previous studies focused on the shifting of the dominant frequency (DF) towards slower frequencies. These studies showed that the frequency with the most prominent peak in the power spectrum moves towards a lower range of frequencies in patients when compared with healthy controls (Bonanni et al, ; Peraza et al, ; Stylianou et al, ). DLB related changes were also found by EEG network connectivity studies.…”

Section: Introductionmentioning

confidence: 99%

“…According to the authors, this aspect is associated with the fact that the pathology is similar in both LBDs, that is, DLB and PDD. A recent work in EEG based on MST reported the α band to be discriminative between HC and dementia, whereas significant differences in the PLI strength between AD and DLB were found in the β (beta; 15–30 Hz) band (Peraza et al, ). Hence, the authors suggested that the β network might potentially be an EEG biomarker of DLB against AD.…”

Section: Introductionmentioning

confidence: 99%

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Weighted network measures reveal differences between dementia types: An EEG study

Mehraram

Kaiser

Cromarty

et al. 2019

Human Brain Mapping

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The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Weighted network measures reveal differences between dementia types: An EEG study

Mehraram

Kaiser

Cromarty

et al. 2019

Human Brain Mapping

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Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

Babiloni

Arakaki

Azami

et al. 2021

Alzheimer's & Dementia

106

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The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and “interrelatedness” at posterior alpha (8‐12 Hz) and widespread delta (< 4 Hz) and theta (4‐8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact‐free rsEEG periods are needed; (2) power density and “interrelatedness” rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

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EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease

Hasoon,

Hamilton,

Schumacher

et al. 2024

Int J Geriat Psychiatry

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BackgroundPredicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia.MethodsParticipants with a diagnosis of MCI due to Alzheimer's disease (MCI‐AD) or Lewy body disease (MCI‐LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network‐based statistic (NBS) toolbox was used to assess differences in network topology.ResultsThe converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre‐alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, −2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, −2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score.ConclusionPatients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI‐AD and MCI‐LB convert to dementia, regardless of the neurodegenerative aetiology.

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Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer’s disease patients

Cited by 57 publications

References 64 publications

Weighted network measures reveal differences between dementia types: An EEG study

Weighted network measures reveal differences between dementia types: An EEG study

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease

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