Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E

Vecchio, Fabrizio; Miraglia, Francesca; Iberite, Francesco; Lacidogna, Giordano; Guglielmi, Valeria; Marra, Camillo; Pasqualetti, Patrizio; Tiziano, Francesco Danilo; Rossini, Paolo Maria

doi:10.1002/ana.25289

Cited by 72 publications

(68 citation statements)

References 64 publications

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“…The measure of network small-worldness (Sw) is defined as the ratio between C and L individually normalized with respect to the frequency bands (Miraglia et al 2020;Vecchio et al 2019a;Vecchio et al 2018a;Vecchio et al 2018b;Vecchio et al 2019b) The Sw coefficient is used to describe the balance between the local connectedness and the global integration of a network. Small-world organization is intermediate between that of random networks, the short overall path length of which is associated with a low level of local clustering, and that of regular networks or lattices, the high-level of clustering of which is accompanied by a long path length.…”

Section: Graph Analysismentioning

confidence: 99%

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Vecchio

Miraglia

Judica³

et al. 2020

GeroScience

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Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact lowresolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain-as well as for the left and the right hemispheres separately-and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects. Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

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Section: Graph Analysismentioning

confidence: 99%

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Vecchio

Miraglia

Judica³

et al. 2020

GeroScience

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“…Developing effective treatments for AD may have been hampered by a failure to detect disease before pathological decline becomes inexorable [1]. Predicting progression at the stage of mild cognitive impairment (MCI) has had some success [2,3]. However, in order to identify patients early, we need a marker that can be easily applied at scale with low cost and low patient burden at a stage before patients have symptoms that impair quality of life.…”

Section: Introductionmentioning

confidence: 99%

Accelerated long-term forgetting in healthy older adults predicts cognitive decline over 1 year

et al. 2020

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Background Here, we address a pivotal factor in Alzheimer’s prevention—identifying those at risk early, when dementia can still be avoided. Recent research highlights an accelerated forgetting phenotype as a risk factor for Alzheimer’s disease. We hypothesized that delayed recall over 4 weeks would predict cognitive decline over 1 year better than 30-min delayed recall, the current gold standard for detecting episodic memory problems which could be an early clinical manifestation of incipient Alzheimer’s disease. We also expected hippocampal subfield volumes to improve predictive accuracy. Methods Forty-six cognitively healthy older people (mean age 70.7 ± 7.97, 21/46 female), recruited from databases such as Join Dementia Research, or a local database of volunteers, performed 3 memory tasks on which delayed recall was tested after 30 min and 4 weeks, as well as Addenbrooke’s Cognitive Examination III (ACE-III) and CANTAB Paired Associates Learning. Medial temporal lobe subregion volumes were automatically measured using high-resolution 3T MRI. The ACE-III was repeated after 12 months to assess the change in cognitive ability. We used univariate linear regressions and ROC curves to assess the ability of tests of delayed recall to predict cognitive decline on ACE-III over the 12 months. Results Fifteen of the 46 participants declined over the year (≥ 3 points lost on ACE-III). Four-week verbal memory predicted cognitive decline in healthy older people better than clinical gold standard memory tests and hippocampal MRI. The best single-test predictor of cognitive decline was the 4-week delayed recall on the world list (R2 = .123, p = .018, β = .418). Combined with hippocampal subfield volumetry, 4-week verbal recall identifies those at risk of cognitive decline with 93% sensitivity and 86% specificity (AUC = .918, p < .0001). Conclusions We show that a test of accelerated long-term forgetting over 4 weeks can predict cognitive decline in healthy older people where traditional tests of delayed recall cannot. Accelerated long-term forgetting is a sensitive, easy-to-test predictor of cognitive decline in healthy older people. Used alone or with hippocampal MRI, accelerated forgetting probes functionally relevant Alzheimer’s-related change. Accelerated forgetting will identify early-stage impairment, helping to target more invasive and expensive molecular biomarker testing.

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“…Second, the present approach produces EEG findings that can be compared and discussed with those of several studies by independent research groups. They have successfully described abnormalities in rsEEG cortical sources estimated in patients with several cognitive and psychiatric disorders in whom rsEEG activity was recorded using the present 10-20 montage system and source estimation techniques [e.g., (68)(69)(70)(71)(72)(73)(74)]. In this line of reasoning, a Workgroup of International Federation of Clinical Neurophysiology expressed the consensus position that EEG source estimation of rsEEG data collected by the traditional 10-20 montage system can be used for exploratory, retrospective studies performed on available rsEEG databases relative to neurological and psychiatric patients (75).…”

Section: Methodological Remarksmentioning

confidence: 99%

Abnormalities of Cortical Sources of Resting State Delta Electroencephalographic Rhythms Are Related to Epileptiform Activity in Patients With Amnesic Mild Cognitive Impairment Not Due to Alzheimer's Disease

et al. 2020

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Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E

Cited by 72 publications

References 64 publications

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Accelerated long-term forgetting in healthy older adults predicts cognitive decline over 1 year

Abnormalities of Cortical Sources of Resting State Delta Electroencephalographic Rhythms Are Related to Epileptiform Activity in Patients With Amnesic Mild Cognitive Impairment Not Due to Alzheimer's Disease

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