Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Musaeus, Christian Sandøe; Engedal, Knut; Høgh, Peter; Jelić, Vesna; Mørup, Morten; Naik, Mala; Oeksengaard, Anne Rita; Snædal, Jón; Wahlund, Lars Olof; Waldemar, Gunhild; Andersen, Birgitte

doi:10.1016/j.clinph.2019.07.016

Cited by 33 publications

(32 citation statements)

References 58 publications

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“…The study with the largest sample (Koenig et al [87], n(NY) = 148 AD, 51 MCI, 25 OA; n(Stockholm) = 63 AD, 41 MCI, 21 OA) did not find significant differences between people with MCI and cognitively healthy older adults, but there was significantly lower global connectivity in people with AD than in people with MCI. Lower connectivity in people with AD than in people with MCI was also reported by Smailovic et al [73] (n = 197 AD, 230 MCI), Jelic et al [75] (n = 18 AD, 19 aMCI, 16 OA), Musaeus et al [84] (n = 117 AD, 117 MCI, 135 OA), and Núñez et al [88] (n = 45 AD, 51 AD-MCI, 36 OA). In the studies that did not measure connectivity at the global level, differences between people with MCI and people with AD were mostly found in temporo-parietal areas [75,84,88].…”

Section: Alpha Power and Functional Connectivity During Wakeful Restsupporting

confidence: 73%

“…In addition to finding the expected pattern of lower connectivity in more cognitively impaired groups, Musaeus et al [84] also found higher connectivity in people with MCI than in cognitively healthy older adults, and in people with AD than in people with MCI. They used three different measures of connectivity and found that whereas coherence decreased with increasing impairment, the imaginary part of coherence and weighted phase lag index showed both lower and higher connectivity in more cognitively impaired groups.…”

Section: Alpha Power and Functional Connectivity During Wakeful Restmentioning

confidence: 91%

“…Five of the twelve studies that compared functional connectivity in the full alpha band between people with MCI and cognitively healthy older adults found lower connectivity in people with MCI (Michels et al [43], n = 17 aMCI, 17 OA; Gonzalez-Escamilla et al [83], n = 29 aMCI, 29 OA; Musaeus et al [84], n = 117 AD, 117 MCI, 135 OA; Teipel et al [85], n = 16 aMCI, 20 OA; Wen et al [86], n = 12 aMCI, 12 OA). Connectivity was lower mainly within temporo-parietal areas [43,83,85] or between temporo-parietal and other areas [43].…”

Section: Alpha Power and Functional Connectivity During Wakeful Restmentioning

confidence: 99%

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Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Lejko

Larabi

Herrmann

et al. 2020

JAD

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Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.

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Section: Alpha Power and Functional Connectivity During Wakeful Restsupporting

confidence: 73%

Section: Alpha Power and Functional Connectivity During Wakeful Restmentioning

confidence: 91%

Section: Alpha Power and Functional Connectivity During Wakeful Restmentioning

confidence: 99%

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Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Lejko

Larabi

Herrmann

et al. 2020

JAD

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“…Revealing the spatial characteristics and the dynamic properties and type of disruption involving brain networks would be a step in the right direction, aiming to develop a diagnostic tool and a sensitive biomarker that can track disease progression. Oscillatory synchronisation between EEG recordings has been commonly estimated in previous studies with coherence [30], a linear method of measuring synchronisation although other methods of functional coupling, that allow detection of nonlinear interactions, like the synchronisation likelihood have been also implemented to study network disruption in dementia [31].…”

Section: Introductionmentioning

confidence: 99%

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Zhao

Durongbhan

et al. 2020

IEEE Trans. Med. Imaging

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Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This paper proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification

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“…As neuronal connectivity is affected by the accumulation of amyloid-β in the extracellular space (67), larger inhibitory circuits operating under high-frequency regimes turn into subpopulations that may produce these oscillations without synchrony with each other. In (68) the deficit in coherence between oscillations measured by EEG electrodes across the scalp in different frequency bands has been proposed as a diagnostic marker of dementia caused by Alzheimer’s disease.…”

Section: Discussionmentioning

confidence: 99%

Olfactory Response as a Marker for Alzheimer's Disease: Evidence from Perceptual and Frontal Oscillation Coherence Deficit

Sedghizadeh

Hojjati

Ezzatdoost

et al. 2019

Preprint

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High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer's disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain contains neural circuitry involved in olfactory perception. Several studies have suggested that olfactory deficit can be used as a marker for early diagnosis of AD. A quantitative assessment of the performance of the olfactory system can hence serve as a potential biomarker for Alzheimer's disease, offering a relatively convenient and inexpensive diagnosis method. This study examines the decline in the perception of olfactory stimuli and the deficit in the performance of high-frequency frontal oscillations in response to olfactory stimulation as markers for AD. Two measurement modalities are employed for assessing the olfactory performance: 1) An interactive smell identification test is used to sample the response to a sizable variety of odorants, and 2) Electrophysiological data are collected in an olfactory perception task with a pair of selected odorants in order to assess the connectivity of frontal cortex regions. Statistical analysis methods are used to assess the significance of selected features extracted from the recorded modalities as Alzheimer's biomarkers. Olfactory decline regressed to age in both healthy and Mild AD groups are evaluated, and single- and multi-modal classifiers are also developed. The novel aspects of this study include: 1) Combining EEG response to olfactory stimulation with behavioral assessment of olfactory perception as a marker of AD, 2) Identification of odorants most significantly affected in Mild AD patients, 3) Identification of odorants which are still adequately perceived by Mild AD patients, 4) Analysis of the decline in the spatial coherence of different oscillatory bands in response to olfactory stimulation, and 5) Being the first study to quantitatively assess the performance of olfactory decline due to aging and AD in the Iranian population.

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Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Cited by 33 publications

References 58 publications

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Olfactory Response as a Marker for Alzheimer's Disease: Evidence from Perceptual and Frontal Oscillation Coherence Deficit

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