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

Cromarty, Ruth; Schumacher, Julia; Graziadio, Sara; Gallagher, Peter; Killen, Alison; Firbank, Michael; Blamire, Andrew M.; Kaiser, Marcus; Thomas, Alan; O’Brien, John; Peraza, Luis R.; Taylor, John‐Paul

doi:10.3389/fnagi.2018.00347

Cited by 14 publications

(19 citation statements)

References 43 publications

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“…This matches as well with the outcome from the distance analysis, which revealed that the most prominent differences are observable in both α and β frequency ranges for the longest edges (Figure b). We hypothesise that the weakening of the posterior–anterior connections is associated with impairment of the attentional networks, which are known to be affected in AD and DLB (Corbetta & Shulman, ; Cromarty et al, ). We believe that the disruption of the occipital brain network may play a role in the alteration of the information flow towards the frontal area in DLB (Bonanni et al, ; Briel et al, ; Peraza et al, ).…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

Mehraram

Kaiser

Cromarty

et al. 2019

Human Brain Mapping

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“…It was reported that clumps of pyramidal cells in the neocortex are more prone to neurofibrillary tangle formation and the degenerative process than those of the inhibitory interneurons. The association fibers interconnecting regions on a hemisphere are at a higher risk of this degeneration, as well as the cholinergic projection from the nucleus basalis of Meynert, although the thalamocortical projection fibers would less susceptible [6,7,14].…”

Section: Discussionmentioning

confidence: 99%

“…In 2050, the proportion of adults over 60 years old will be approximately 22% worldwide. The neurodegenerative disorders are expected to arise, in particular, with Alzheimer disease (AD) being one of the most common causes of dementia [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Cellular Composition in the Aging Cerebral Cortex of Humans

2019

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The numbers/densities of neurons and gliocytes and the neuron-to-glia ratios were calculated in different layers of the cerebral cortex of three subjects in a post mortem study. The subjects were 60 to 70 years old (which was qualified as a relatively early period of aging) and had no history of neurodegeneration diseases. The brains embalmed in 10% formalin-fixative solution were processed, and coronal 4-µmthick slices of tissues of the frontal, parietal, temporal, and occipital lobes were prepared and stained with hematoxylin-eosin. Clear cortical lamination was observed in all lobes. In the frontal lobe, the mean neuron density (number of neurons within visual fields at a 400× magnification) was significantly higher, while that of gliocytes was significantly lower compared to the respective values in other lobes (P < 0.05). A significant association between the number of neurons and glia was found in the frontal lobe (r 2 = 0.40, P = 0.0029) and in all superficial cortical layers (r 2 = 0.72, P = 0.0025), but not in the deeper layers (r 2 = 0.18, P = 0.14). The mean neuron-to-glia ratio in different lobes and layers varied from 1.0 : 0.81 to 1.0 : 3.54. It is concluded that, in early-aging subjects with no cerebral pathology, the quantitative characteristics of cortical neurons and glial cells do not differ significantly from those in the brains of younger adult healthy subjects.

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“…We used a modified version of the attention network test (ANT) [23, 24] based on the version described by Fan et al [25]. The main rationale for adapting the ANT was to make it suitable for older adults and dementia patients.…”

Section: Methodsmentioning

confidence: 99%

“…A detailed description of the task can be found in Ref. [24]. Briefly, the ANT combines elements of the Erikson flanker task [27] and the Posner cueing paradigm [28] to form a single visual reaction time task.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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. Electronic supplementary material The online version of this article (10.1007/s00415-019-09323-y) contains supplementary material, which is available to authorized users.

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

Cited by 14 publications

References 43 publications

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

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

Cellular Composition in the Aging Cerebral Cortex of Humans

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

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