Electroencephalogram in dementia with Lewy bodies: a systematic review

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.

Assessment of Four Serum Biochemical Markers in Elderly Patients with Vascular Dementia after Cerebral Infarction and Their Response to Donepezil and Idebenone

Objective  Our study aimed to explore four serum levels of biochemical markers, including brain-derived neurotrophic factor (BDNF), homocysteine (Hcy), nitric oxide (NO), and γ-interferon (IFN-γ), in elderly patients with vascular dementia (VD) after the cerebral infarction and to elucidate possible connections between them. Method  The elderly patients with VD after cerebral infarction admitted in our hospital, and the elderly persons for physical examination from November 2020 to December 2021 were included in this study. The serum levels of BDNF, Hcy, NO, and IFN-γ were compared between the study group and the control group. Results  In the study group, the serum levels of Hcy and IFN-γ were significantly higher than that in the control group, whereas significantly lower serum levels of BDNF and NO were found in the study group compared with the control group. After receiving the intervention of donepezil and/or idebenone, the serum levels of Hcy and IFN-γ in group B were significantly lower than that in group A, while the serum levels of BDNF and NO in group B were significantly higher than that in Group A. Conclusion  The results of our study showed abnormally expressed serum levels of Hcy, IFN-γ, BDNF, and NO in elderly patients with VD after cerebral infarction which might strongly reflect the severity of VD. Moreover, after intervention of donepezil alone or combined with idebenone, the changes of serum levels of Hcy, IFN-γ, BDNF, and NO may reflect the curative effect of the disease.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Background

Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition.

Objectives

We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG.

Methods

We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests.

Results

We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants.

Conclusions

Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

One Minute of EEG Data Provides Sufficient and Reliable Data for Identifying Lewy Body Dementia

OBJECTIVE

To determine the minimum duration of electroencephalography (EEG) data necessary to differentiate EEG features of Lewy body dementia (LBD), that is, dementia with Lewy bodies and Parkinson disease dementia, from non-LBD patients, that is, Alzheimer disease and Parkinson disease.

METHODS

We performed quantitative EEG analysis for 16 LBD and 14 non-LBD patients. After artifact removal, a fast Fourier transform was performed on 90, 60, and thirty 2-second epochs to derive dominant frequency; dominant frequency variability; and dominant frequency prevalence.

RESULTS

In LBD patients, there were no significant differences in EEG features derived from 90, 60, and thirty 2-second epochs (all P >0.05). There were no significant differences in EEG features derived from 3 different groups of thirty 2-second epochs (all P >0.05). When analyzing EEG features derived from ninety 2-second epochs, we found that LBD had significantly reduced dominant frequency, reduced dominant frequency variability, and reduced dominant frequency prevalence alpha compared with the non-LBD group (all P <0.05). These same differences were observed between the LBD and non-LBD groups when analyzing thirty 2-second epochs.

CONCLUSIONS

There were no differences in EEG features derived from 1 minute versus 3 minutes of EEG data, and both durations of EEG data equally differentiated LBD from non-LBD.

Recent advances in Lewy body dementia: A comprehensive review

Lewy Body Dementia is the second most frequent neurodegenerative illness proven to cause dementia, after Alzheimer's disease (AD). It is believed to be vastly underdiagnosed, as there is a significant disparity between the number of cases diagnosed clinically and those diagnosed via neuropathology at the time of postmortem autopsy. Strikingly, many of the pharmacologic treatments used to treat behavioral and cognitive symptoms in other forms of dementia exacerbate the symptoms of DLB. Therefore, it is critical to accurately diagnose DLB as these patients require a specific treatment approach. This article focuses on its pathophysiology, risk factors, differentials, and its diverse treatment modalities. In this study, an English language literature search was conducted on Medline, Cochrane, Embase, and Google Scholar till April 2022. The following search strings and Medical Subject Headings (MeSH) terms were used: "Lewy Body Dementia," "Dementia with Lewy bodies," and "Parkinson's Disease Dementia." We explored the literature on Lewy Body Dementia for its epidemiology, pathophysiology, the role of various genes and how they bring about the disease, biomarkers, its differential diagnoses and treatment options.

Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review

Stroke is one of the leading causes of death and disability. Despite the high prevalence of stroke, characterizing the acute neural recovery patterns that follow stroke and predicting long-term recovery remains challenging. Objective methods to quantify and characterize neural injury are still lacking. Since neuroimaging methods have a poor temporal resolution, EEG has been used as a method for characterizing post-stroke recovery mechanisms for various deficits including motor, language, and cognition as well as predicting treatment response to experimental therapies. In addition, transcranial magnetic stimulation (TMS), a form of non-invasive brain stimulation, has been used in conjunction with EEG (TMS-EEG) to evaluate neurophysiology for a variety of indications. TMS-EEG has significant potential for exploring brain connectivity using focal TMS-evoked potentials and oscillations, which may allow for the system-specific delineation of recovery patterns after stroke. In this review, we summarize the use of EEG alone or in combination with TMS in post-stroke motor, language, cognition, and functional/global recovery. Overall, stroke leads to a reduction in higher frequency activity (≥8 Hz) and intra-hemispheric connectivity in the lesioned hemisphere, which creates an activity imbalance between non-lesioned and lesioned hemispheres. Compensatory activity in the non-lesioned hemisphere leads mostly to unfavorable outcomes and further aggravated interhemispheric imbalance. Balanced interhemispheric activity with increased intrahemispheric coherence in the lesioned networks correlates with improved post-stroke recovery. TMS-EEG studies reveal the clinical importance of cortical reactivity and functional connectivity within the sensorimotor cortex for motor recovery after stroke. Although post-stroke motor studies support the prognostic value of TMS-EEG, more studies are needed to determine its utility as a biomarker for recovery across domains including language, cognition, and hemispatial neglect. As a complement to MRI-based technologies, EEG-based technologies are accessible and valuable non-invasive clinical tools in stroke neurology.

Lewy Body Dementias: Controversies and Drug Development

Lewy body dementia (LBD) is one of the most common neurodegenerative dementias. Clinical trials for symptomatic and disease-modifying therapies in LBD remain a national research priority, but there are many challenges in both past and active drug developments in LBD. This review highlights the controversies in picking the appropriate populations, interventions, target selections, and outcome measures, which are all critical components of clinical trial implementation in LBD. The heterogeneity of LBD neuropathology and clinical presentations, limited understanding of core features such as cognitive fluctuations, and lack of validated LBD-specific outcome measures and biomarkers represent some of the major challenges in LBD trials.

Lewy Body Dementias: A Coin with Two Sides?

Lewy body dementias (LBDs) consist of dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), which are clinically similar syndromes that share neuropathological findings with widespread cortical Lewy body deposition, often with a variable degree of concomitant Alzheimer pathology. The objective of this article is to provide an overview of the neuropathological and clinical features, current diagnostic criteria, biomarkers, and management of LBD. Literature research was performed using the PubMed database, and the most pertinent articles were read and are discussed in this paper. The diagnostic criteria for DLB have recently been updated, with the addition of indicative and supportive biomarker information. The time interval of dementia onset relative to parkinsonism remains the major distinction between DLB and PDD, underpinning controversy about whether they are the same illness in a different spectrum of the disease or two separate neurodegenerative disorders. The treatment for LBD is only symptomatic, but the expected progression and prognosis differ between the two entities. Diagnosis in prodromal stages should be of the utmost importance, because implementing early treatment might change the course of the illness if disease-modifying therapies are developed in the future. Thus, the identification of novel biomarkers constitutes an area of active research, with a special focus on α-synuclein markers.

Differential changes in visual and auditory event-related oscillations in dementia with Lewy bodies

OBJECTIVE

Aside from the cognitive impairment, patients with dementia with Lewy bodies (DLB) have a high frequency of visual hallucinations and a number of other vision-related symptoms, whereas auditory hallucinations are less frequent. To better understand the differential dysfunction of the visual network in DLB, we compared auditory and visual event-related potentials and oscillations in patients with DLB.

METHODS

Event-related potentials elicited by visual and auditory oddball tasks were recorded in 23 patients with DLB and 22 healthy controls and analyzed in time and time-frequency domain.

RESULTS

DLB patients had decreased theta band activity related to both early sensory and later cognitive processing in the visual, but not in the auditory task. Patients had lower delta and higher alpha and beta bands power related to later cognitive processing in both auditory and visual tasks.

CONCLUSIONS

In DLB visual event-related oscillations are characterized by a decrease in theta and lack of inhibition in alpha bands.

SIGNIFICANCE

Decreased theta and a lack of inhibition in alpha band power might be an oscillatory underpinning of some classical DLB symptoms such as fluctuations in attention and high-level visual disturbances and a potential marker of dysfunction of the visual system in DLB.