Functional and structural brain network correlates of visual hallucinations in Lewy body dementia.

Visual hallucinations are a common feature of Lewy body dementia. Previous studies have shown that visual hallucinations are highly specific in differentiating Lewy body dementia from Alzheimer's disease dementia and Alzheimer-Lewy body mixed pathology cases. Computational models propose that impairment of visual and attentional networks is aetiologically key to the manifestation of visual hallucinations symptomatology. However, there is still a lack of experimental evidence on functional and structural brain network abnormalities associated with visual hallucinations in Lewy body dementia. We used EEG source localization and network based statistics to assess differential topographical patterns in Lewy body dementia between 25 participants with visual hallucinations and 17 participants without hallucinations. Diffusion tensor imaging was used to assess structural connectivity between thalamus, basal forebrain and cortical regions belonging to the functionally affected network component in the hallucinating group, as assessed with network based statistics. The number of white matter streamlines within the cortex and between subcortical and cortical regions was compared between hallucinating and not hallucinating groups and correlated with average EEG source connectivity of the affected subnetwork. Moreover, modular organization of the EEG source network was obtained, compared between groups and tested for correlation with structural connectivity. Network analysis showed that compared to non-hallucinating patients, those with hallucinations feature consistent weakened connectivity within the visual ventral network, and between this network and default mode and ventral attentional networks, but not between or within attentional networks. The occipital lobe was the most functionally disconnected region. Structural analysis yielded significantly affected white matter streamlines connecting the cortical regions to the nucleus basalis of Meynert and the thalamus in hallucinating compared to not hallucinating patients. The number of streamlines in the tract between the basal forebrain and the cortex correlated with cortical functional connectivity in non-hallucinating patients, while a correlation emerged for the white matter streamlines connecting the functionally affected cortical regions in the hallucinating group. This study proposes, for the first time, differential functional networks between hallucinating and not hallucinating Lewy body dementia patients, and provides empirical evidence for existing models of visual hallucinations. Specifically, the outcome of the present study shows that the hallucinating condition is associated with functional network segregation in Lewy body dementia and supports the involvement of the cholinergic system as proposed in the current literature.

Slowing on quantitative EEG is associated with transition to dementia in mild cognitive impairment.

Electroencephalographic (EEG) abnormalities are greater in mild cognitive impairment (MCI) with Lewy bodies (MCI-LB) than in MCI due to Alzheimer's disease (MCI-AD) and may anticipate the onset of dementia. We aimed to assess whether quantitative EEG (qEEG) slowing would predict a higher annual hazard of dementia in MCI across these etiologies. MCI patients (n = 92) and healthy comparators (n = 31) provided qEEG recording and underwent longitudinal clinical and cognitive follow-up. Associations between qEEG slowing, measured by increased theta/alpha ratio, and clinical progression from MCI to dementia were estimated with a multistate transition model to account for death as a competing risk, while controlling for age, cognitive function, and etiology classified by an expert consensus panel.Over a mean follow-up of 1.5 years (SD = 0.5), 14 cases of incident dementia and 5 deaths were observed. Increased theta/alpha ratio on qEEG was associated with increased annual hazard of dementia (hazard ratio = 1.84, 95% CI: 1.01-3.35). This extends previous findings that MCI-LB features early functional changes, showing that qEEG slowing may anticipate the onset of dementia in prospectively identified MCI.

In vivo nucleus basalis of Meynert degeneration in mild cognitive impairment with Lewy bodies.

OBJECTIVES: To investigate in vivo degeneration of the cholinergic system in mild cognitive impairment with Lewy bodies (MCI-LB), we studied nucleus basalis of Meynert (NBM) volumes from structural MR images and its relation to EEG slowing and cognitive impairment. METHODS: We studied the NBM using structural MR images in 37 patients with MCI-LB, 34 patients with MCI with Alzheimer's disease (MCI-AD), and 31 healthy control participants. We also tested correlations between NBM volumes and measures of overall cognition and measures of EEG slowing in the MCI groups. RESULTS: Overall NBM volume was reduced in MCI-LB compared to controls with no significant difference between MCI-AD and controls or between the two MCI groups. The voxel-wise analysis revealed bilateral clusters of reduced NBM volume in MCI-LB compared to controls and smaller clusters in MCI-AD compared to controls. There was a significant association between overall NBM volume and measures of overall cognition in MCI-LB, but not in MCI-AD. In both MCI groups, reduced NBM volume was correlated with more severe EEG slowing. CONCLUSIONS: This study provides in vivo evidence that early cholinergic degeneration in DLB occurs at the MCI stage and is related to the severity of cognitive impairment. Furthermore, the results suggest that early EEG slowing in MCI-LB might be in part cholinergically driven. Importantly, these findings suggest an early cholinergic deficit in MCI-LB that may motivate further testing of the effectiveness of cholinesterase inhibitors in this group.

Quantitative EEG as a biomarker in mild cognitive impairment with Lewy bodies.

OBJECTIVES: To investigate using quantitative EEG the (1) differences between patients with mild cognitive impairment with Lewy bodies (MCI-LB) and MCI with Alzheimer's disease (MCI-AD) and (2) its utility as a potential biomarker for early differential diagnosis. METHODS: We analyzed eyes-closed, resting-state, high-density EEG data from highly phenotyped participants (39 MCI-LB, 36 MCI-AD, and 31 healthy controls). EEG measures included spectral power in different frequency bands (delta, theta, pre-alpha, alpha, and beta), theta/alpha ratio, dominant frequency, and dominant frequency variability. Receiver operating characteristic (ROC) analyses were performed to assess diagnostic accuracy. RESULTS: There was a shift in power from beta and alpha frequency bands towards slower frequencies in the pre-alpha and theta range in MCI-LB compared to healthy controls. Additionally, the dominant frequency was slower in MCI-LB compared to controls. We found significantly increased pre-alpha power, decreased beta power, and slower dominant frequency in MCI-LB compared to MCI-AD. EEG abnormalities were more apparent in MCI-LB cases with more diagnostic features. There were no significant differences between MCI-AD and controls. In the ROC analysis to distinguish MCI-LB from MCI-AD, beta power and dominant frequency showed the highest area under the curve values of 0.71 and 0.70, respectively. While specificity was high for some measures (up to 0.97 for alpha power and 0.94 for theta/alpha ratio), sensitivity was generally much lower. CONCLUSIONS: Early EEG slowing is a specific feature of MCI-LB compared to MCI-AD. However, there is an overlap between the two MCI groups which makes it difficult to distinguish between them based on EEG alone.

EEG alpha reactivity and cholinergic system integrity in Lewy body dementia and Alzheimer's disease.

BACKGROUND: Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is characterised by marked deficits within the cholinergic system which are more severe than in Alzheimer's disease (AD) and are mainly caused by degeneration of the nucleus basalis of Meynert (NBM) whose widespread cholinergic projections provide the main source of cortical cholinergic innervation. EEG alpha reactivity, which refers to the reduction in alpha power over occipital electrodes upon opening the eyes, has been suggested as a potential marker of cholinergic system integrity. METHODS: Eyes-open and eyes-closed resting state EEG data were recorded from 41 LBD patients (including 24 patients with DLB and 17 with PDD), 21 patients with AD, and 40 age-matched healthy controls. Alpha reactivity was calculated as the relative reduction in alpha power over occipital electrodes when opening the eyes. Structural MRI data were used to assess volumetric changes within the NBM using a probabilistic anatomical map. RESULTS: Alpha reactivity was reduced in AD and LBD patients compared to controls with a significantly greater reduction in LBD compared to AD. Reduced alpha reactivity was associated with smaller volumes of the NBM across all groups (ρ = 0.42, pFDR = 0.0001) and in the PDD group specifically (ρ = 0.66, pFDR = 0.01). CONCLUSIONS: We demonstrate that LBD patients show an impairment in alpha reactivity upon opening the eyes which distinguishes this form of dementia from AD. Furthermore, our results suggest that reduced alpha reactivity might be related to a loss of cholinergic drive from the NBM, specifically in PDD.

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

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 ß-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, ß- 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.

Structural correlates of attention dysfunction in Lewy body dementia and Alzheimer's disease: an ex-Gaussian analysis.

BACKGROUND: Lewy body dementia (LBD) and Alzheimer's disease (AD) are common forms of degenerative dementia. While they are characterized by different clinical profiles, attentional deficits are a common feature. The objective of this study was to investigate how attentional problems in LBD and AD differentially affect different aspects of reaction time performance and to identify possible structural neural correlates. METHODS: We studied reaction time data from an attention task comparing 39 LBD patients, 28 AD patients, and 22 age-matched healthy controls. Data were fitted to an ex-Gaussian model to characterize different facets of the reaction time distribution (mean reaction time, reaction time variability, and the subset of extremely slow responses). Correlations between ex-Gaussian parameters and grey and white matter volume were assessed by voxel-based morphometry. RESULTS: Both dementia groups showed an increase in extremely slow responses. While there was no difference between AD and controls with respect to mean reaction time and variability, both were significantly increased in LBD patients compared to controls and AD. There were widespread correlations between mean reaction time and variability and grey matter loss in AD, but not in LBD. CONCLUSIONS: This study shows that different aspects of reaction time performance are differentially affected by AD and LBD, with a difference in structural neural correlates underlying the observed behavioural deficits. While impaired attentional performance is linked to brain atrophy in AD, in LBD it might be related to functional or microstructural rather than macrostructural changes.

Structural Brain Correlates of Attention Dysfunction in Lewy Body Dementias and Alzheimer's Disease.

Lewy body dementia (LBD) and Alzheimer's disease (AD) are common forms of dementia that have different clinical profiles but are both commonly associated with attentional deficits. The aim of this study was to investigate efficiency of different attentional systems in LBD and AD and its association with brain structural abnormalities. We studied reaction time (RT) data from 45 LBD, 31 AD patients and 22 healthy controls (HCs) using the Attention Network Test (ANT) to assess the efficiency of three different attentional systems: alerting, orienting and executive conflict. Voxel-based morphometry (VBM) was used to investigate relations between different attention components and cortical volume. Both dementia groups showed slower overall RTs than controls, with additional slowing in LBD relative to AD. There was a significant alerting effect in controls which was absent in the dementia groups, the executive conflict effect was greater in both dementia groups compared to controls, but the orienting effect did not differ between groups. Mean RT in AD was negatively correlated with occipital gray matter (GM) volume and in LBD orienting efficiency was negatively related to occipital white matter (WM) volume. Given that previous studies in less impaired patients suggest a maintenance of the alerting effect, the absent alerting effect in our study suggests a loss of alerting efficiency with dementia progression. While orienting was largely preserved, it might be related to occipital structural abnormalities in LBD. Executive function was markedly impaired in both dementia groups, however, the absence of relations to brain volume suggests that it might be more related to functional rather than macrostructural pathophysiological changes.

Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis.

OBJECTIVE: We investigated for quantitative EEG (QEEG) differences between Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) patients and healthy controls, and for QEEG signatures of cognitive fluctuations (CFs) in DLB. METHODS: We analysed eyes-closed, resting state EEGs from 18 AD, 17 DLB and 17 PDD patients with mild dementia, and 21 age-matched controls. Measures included spectral power, dominant frequency (DF), frequency prevalence (FP), and temporal DF variability (DFV), within defined EEG frequency bands and cortical regions. RESULTS: DLB and PDD patients showed a leftward shift in the power spectrum and DF. AD patients showed greater DFV compared to the other groups. In DLB patients only, greater DFV and EEG slowing were correlated with CFs, measured by the clinician assessment of fluctuations (CAF) scale. The diagnostic accuracy of the QEEG measures was 94% (90.4-97.9%), with 92.26% (80.4-100%) sensitivity and 83.3% (73.6-93%) specificity. CONCLUSION: Although greater DFV was only shown in the AD group, within the DLB group a positive DFV - CF correlation was found. QEEG measures could classify DLB and AD patients with high sensitivity and specificity. SIGNIFICANCE: The findings add to an expanding literature suggesting that EEG is a viable diagnostic and symptom biomarker in dementia, particularly DLB.

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

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG). In this regard, an established electrophysiological difference in DLB is a decrease of dominant frequency (DF)-the frequency with the highest signal power between 4 and 15 Hz. Here, we investigated network connectivity in EEG signals acquired from DLB patients, and whether these networks were able to differentiate DLB from healthy controls (HCs) and associated dementias. We analysed EEG recordings from old adults: HCs, AD, DLB and Parkinson's disease dementia (PDD) patients. Brain networks were assessed with the minimum spanning tree (MST) within six EEG bands: delta, theta, high-theta, alpha, beta and DF. Patients showed lower alpha band connectivity and lower DF than HCs. DLB and PDD showed a randomised MST compared with HCs and AD in high-theta and alpha but not in DF. The MST randomisation in DLB and PDD reflects decreased brain efficiency as well as impaired neural synchronisation. However, the lack of network topology differences at the DF between all dementia groups and HCs may indicate a compensatory response of the brain to the neuropathology.

EEG Markers of Dementia with Lewy Bodies: A Multicenter Cohort Study.

Quantitative EEG (QEEG) has demonstrated good discriminative capacity for dementia with Lewy bodies (DLB) diagnosis as compared to Alzheimer's disease (AD) with a predictive value of 100% in a single cohort study. EEG in DLB was characterized by a dominant frequency (DF) in pre-alpha (5.5-7.5 Hz), theta, or delta bands and DF variability (DFV) >1.2 Hz, frequency prevalence (FP) pre-alpha in >40% and FP alpha in <32% of the epochs. To validate the aforementioned QEEG findings in independent cohorts of clinically diagnosed DLB versus AD patients, we analyzed EEG traces of 79 DLB and 133 AD patients (MMSE >20) collected from four European Centers. EEG traces from 19 scalp derivations were acquired as at least 10 min continuous signals and epoched in off-setting as series of 2-second-long epochs, subsequently processed by Fast Fourier Transform (frequency resolution 0.5 Hz). DLB patients showed EEG specific abnormalities in posterior derivations characterized by DF <8 Hz FP pre-alpha >50%, FP alpha <25%. DFV was >0.5 Hz. AD patients displayed stable alpha DF, DFV <0.5 Hz, FP pre-alpha <30%, and FP alpha >55%. DLB and AD differed for DF (p < 10-6), DFV (p < 0.05), FP pre-alpha (p < 10-12) and FP alpha (p < 10-12). Discriminant analysis detected specific cut-offs for every EEG mathematical descriptor; DF = 8, DFV = 2.2 Hz, FP pre-alpha=33%, FP alpha = 41% for posterior derivations. If at least one of the cut-off values was met, the percentage of DLB and AD patients correctly classified was 90% and 64%, respectively. The findings in this multicenter study support the validity of QEEG analysis as a tool for diagnosis in DLB patients.

Multimodal EEG-MRI in the differential diagnosis of Alzheimer's disease and dementia with Lewy bodies.

Differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) remains challenging; currently the best discriminator is striatal dopaminergic imaging. However this modality fails to identify 15-20% of DLB cases and thus other biomarkers may be useful. It is recognised electroencephalography (EEG) slowing and relative medial temporal lobe preservation are supportive features of DLB, although individually they lack diagnostic accuracy. Therefore, we investigated whether combined EEG and MRI indices could assist in the differential diagnosis of AD and DLB. Seventy two participants (21 Controls, 30 AD, 21 DLB) underwent resting EEG and 3 T MR imaging. Six EEG classifiers previously generated using support vector machine algorithms were applied to the present dataset. MRI index was derived from medial temporal atrophy (MTA) ratings. Logistic regression analysis identified EEG predictors of AD and DLB. A combined EEG-MRI model was then generated to examine whether there was an improvement in classification compared to individual modalities. For EEG, two classifiers predicted AD and DLB (model: χ(2) = 22.1, df = 2, p < 0.001, Nagelkerke R(2) = 0.47, classification = 77% (AD 87%, DLB 62%)). For MRI, MTA also predicted AD and DLB (model: χ(2) = 6.5, df = 1, p = 0.01, Nagelkerke R(2) = 0.16, classification = 67% (77% AD, 52% DLB). However, a combined EEG-MRI model showed greater prediction in AD and DLB (model: χ(2) = 31.1, df = 3, p < 0.001, Nagelkerke R(2) = 0.62, classification = 90% (93% AD, 86% DLB)). While suggestive and requiring validation, diagnostic performance could be improved by combining EEG and MRI, and may represent an alternative to dopaminergic imaging.

Neurophysiological biomarkers for Lewy body dementias.

OBJECTIVE: Lewy body dementias (LBD) include both dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD), and the differentiation of LBD from other neurodegenerative dementias can be difficult. Currently, there are few biomarkers which might assist early diagnosis, map onto LBD symptom severity, and provide metrics of treatment response. Traditionally, biomarkers in LBD have focussed on neuroimaging modalities; however, as biomarkers need to be simple, inexpensive and non-invasive, neurophysiological approaches might also be useful as LBD biomarkers. METHODS: In this review, we searched PubMED and PsycINFO databases in a semi-systematic manner in order to identify potential neurophysiological biomarkers in the LBDs. RESULTS: We identified 1491 studies; of these, 37 studies specifically examined neurophysiological biomarkers in LBD patients. We found that there was substantial heterogeneity with respect to methodologies and patient cohorts. CONCLUSION: Generally, many of the findings have yet to be replicated, although preliminary findings reinforce the potential utility of approaches such as quantitative electroencephalography and motor cortical stimulation paradigms. SIGNIFICANCE: Various neurophysiological techniques have the potential to be useful biomarkers in the LBDs. We recommend that future studies focus on maximising the diagnostic specificity and sensitivity of the most promising neurophysiological biomarkers.