Telling functional networks apart using ranked network features stability

Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)-218S314French Ministry of Foreign Affairs PHC-Bosphore ProgramEuropean Research Council (ERC)Spanish State Research Agency, through the Severo Ochoa and Maria de Maeztu Program for Centers and Units of Excellence in R

Patients with Alzheimer’s disease dementia show partially preserved parietal ‘hubs’ modeled from resting-state alpha electroencephalographic rhythms

IntroductionGraph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. ‘Degree’ hubs reflect node centrality (the connection rate), while ‘connector’ hubs are those linked to several clusters of nodes (mainly long-range connections).MethodsHere, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer’s disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a ‘network disease’ and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8–12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2–40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of ‘connector’ hub were used.ResultsConvergent results showed that in both the Nold and ADD groups there were significant parietal ‘degree’ and ‘connector’ hubs derived from alpha rhythms. These hubs had a prominent outward ‘directionality’ in the two groups, but that ‘directionality’ was lower in ADD participants than in Nold participants.DiscussionIn conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward ‘directionality’ of partially preserved parietal ‘degree’ and ‘connector’ hubs derived from rsEEG alpha rhythms

Patients with Alzheimer’s disease dementia show partially preserved parietal ‘hubs’ modeled from resting-state alpha electroencephalographic rhythms

Introduction: Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. ‘Degree’ hubs reflect node centrality (the connection rate), while ‘connector’ hubs are those linked to several clusters of nodes (mainly long-range connections). Methods: Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer’s disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a ‘network disease’ and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8–12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2–40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of ‘connector’ hub were used. Results: Convergent results showed that in both the Nold and ADD groups there were significant parietal ‘degree’ and ‘connector’ hubs derived from alpha rhythms. These hubs had a prominent outward ‘directionality’ in the two groups, but that ‘directionality’ was lower in ADD participants than in Nold participants. Discussion: In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward ‘directionality’ of partially preserved parietal ‘degree’ and ‘connector’ hubs derived from rsEEG alpha rhythms

Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

Brain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.Fil: Moguilner, Sebastian. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. Harvard Medical School; Estados UnidosFil: Baez, Sandra. University of California; Estados Unidos. Trinity College Dublin; Irlanda. Universidad de los Andes; ColombiaFil: Hernandez, Hernan. Universidad Adolfo Ibañez; ChileFil: Migeot, Joaquín. Universidad Adolfo Ibañez; ChileFil: Legaz, Agustina. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; ChileFil: Farina, Francesca R.. University of California; Estados Unidos. Trinity College Dublin; IrlandaFil: Prado, Pavel. Universidad San Sebastián; ChileFil: Cuadros, Jhosmary. Universidad Adolfo Ibañez; Chile. Universidad Nacional Experimental del Táchira; Venezuela. Universidad Técnica Federico Santa María; ChileFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile. Universidad de Buenos Aires; ArgentinaFil: Altschuler, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaFil: Maito, Marcelo Adrián. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileFil: Godoy, María E.. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileFil: Cruzat, Josephine. Universidad Adolfo Ibañez; ChileFil: Valdes Sosa, Pedro A.. University of Electronic Sciences and Technology of China; China. Technology of China; China. Cuban Neuroscience Center; CubaFil: Lopera, Francisco. Universidad de Antioquia; ColombiaFil: Ochoa Gómez, John Fredy. Universidad de Antioquia; ColombiaFil: Gonzalez Hernandez, Alfredis. Universidad Surcolombiana Neiva; ColombiaFil: Bonilla Santos, Jasmin. Universidad Cooperativa de Colombia; ColombiaFil: Gonzalez Montealegre, Rodrigo A.. Universidad Surcolombiana Neiva; ColombiaFil: Anghinah, Renato. Universidade de Sao Paulo; BrasilFil: d’Almeida Manfrinati, Luís E.. Universidade de Sao Paulo; BrasilFil: Fittipaldi, Sol. University of California; Estados Unidos. Trinity College Dublin; Irlanda. Universidad Adolfo Ibañez; ChileFil: Medel, Vicente. Universidad Adolfo Ibañez; ChileFil: Olivares, Daniela. Universidad Adolfo Ibañez; Chile. Universidad de Chile; Chile. Centro de Neuropsicología Clínica; ChileFil: Yener, Görsev G.. Izmir University of Economics; Turquía. Dokuz Eylul University; Turquía. Izmir Biomedicine and Genome Center; TurquíaFil: Escudero, Javier. University of Edinburgh; Reino UnidoFil: Babiloni, Claudio. Università degli Studi di Roma "La Sapienza"; Italia. Hospital San Raffaele Cassino; ItaliaFil: Whelan, Robert. University of California; Estados Unidos. Trinity College Dublin; IrlandaFil: Güntekin, Bahar. Istanbul Medipol University; TurquíaFil: Barttfeld, Pablo. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; Argentin

Different abnormalities of electroencephalographic (EEG) markers in quiet wakefulness are related to motor visual hallucinations in patients with Parkinson's and Lewy body diseases

AbstractBackgroundParkinson's disease (PD) is the second‐most common neurodegenerative disorder that affects 2–3% of the population ≥ 65 years of age and may belong to cognitive deficits and dementia in 50% of cases. Disease with Lewy Bodies (DLB) is emerging as another important cause of dementia in pathological aging. PD and DLB are both due to intra‐neuronal Lewy bodies and are characterized not only by motor dysfunctions but also by cognitive and/or psychiatric symptoms. An open issue is the extent to which these diseases are distinct entities. In this respect, here we compared cortical sources of resting state eyes‐closed electroencephalographic (rsEEG) rhythms in PD and DLB patients having visual hallucinations.MethodClinical and rsEEG rhythms in demographic matched PD (N = 93), DLB (N = 46), Alzheimer's disease dementia (AD, N= 70) and healthy elderly (Nold, N = 60) subjects were available from an international archive. Pathological groups were matched for cognitive status. Individual alpha frequency peak was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were considered. The eLORETA freeware estimated rsEEG cortical sources.ResultAs a confirmation of previous studies, compared to the Nold subjects, the AD, LBD, and PD patients showed higher widespread delta source activities and lower posterior alpha source activities. Specifically, posterior alpha source activities were more abnormal in the AD than the LBD and PD groups, while widespread delta source activities were more abnormal in the PD and DLB than the AD group. As novel results, in relation to the LBD and PD patients without visual hallucinations and the control groups (Nold, AD), those with visual hallucinations were characterized by higher parietal delta source activities (LBD, Figure 1) and parieto‐occipital alpha sources activities (PD, Figure 2).ConclusionThese novel results suggest that in LBD and PD patients resting in the quiet wakefulness, abnormalities in cortical neural synchronization at delta and alpha frequencies in parietal cortex are differently related to visual hallucinations despite the essence of alpha‐synucleinopathy

Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases

Objective: This study tested the hypothesis that markers of functional cortical source connectivity of resting state eyes-closed electroencephalographic (rsEEG) rhythms may be abnormal in subjects with mild cognitive impairment due to Alzheimer's (ADMCI) and Parkinson's (PDMCI) diseases compared to healthy elderly subjects (Nold). Methods: rsEEG data had been collected in ADMCI, PDMCI, and Nold subjects (N = 75 for any group). eLORETA freeware estimated functional lagged linear connectivity (LLC) from rsEEG cortical sources. Area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification of Nold and MCI individuals. Results: Posterior interhemispheric and widespread intrahemispheric alpha LLC solutions were abnormally lower in both MCI groups compared to the Nold group. At the individual level, AUROC curves of LLC solutions in posterior alpha sources exhibited moderate accuracies (0.70-0.72) in the discrimination of Nold vs. ADMCI-PDMCI individuals. No differences in the LLC solutions were found between the two MCI groups. Conclusions: These findings unveil similar abnormalities in functional cortical connectivity estimated in widespread alpha sources in ADMCI and PDMCI. This was true at both group and individual levels. Significance: The similar abnormality of alpha source connectivity in ADMCI and PDMCI subjects might reflect common cholinergic impairment. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved

Biomarker counseling, disclosure of diagnosis and follow-up in patients with mild cognitive impairment:A European Alzheimer's Disease Consortium survey

Objectives: Mild cognitive impairment (MCI) is associated with an increased risk of further cognitive decline, partly depending on demographics and biomarker status. The aim of the present study was to survey the clinical practices of physicians in terms of biomarker counseling, management, and follow-up in European expert centers diagnosing patients with MCI. Methods: An online email survey was distributed to physicians affiliated with European Alzheimer's disease Consortium centers (Northern Europe: 10 centers; Eastern and Central Europe: 9 centers; and Southern Europe: 15 centers) with questions on attitudes toward biomarkers and biomarker counseling in MCI and dementia. This included postbiomarker counseling and the process of diagnostic disclosure of MCI, as well as treatment and follow-up in MCI. Results: The response rate for the survey was 80.9% (34 of 42 centers) across 20 countries. A large majority of physicians had access to biomarkers and found them useful. Pre- and postbiomarker counseling varied across centers, as did practices for referral to support groups and advice on preventive strategies. Less than half reported discussing driving and advance care planning with patients with MCI. Conclusions: The variability in clinical practices across centers calls for better biomarker counseling and better training to improve communication skills. Future initiatives should address the importance of communicating preventive strategies and advance planning

P3‐209: Impact of Biomarkers On Diagnostic Confidence in Clinical Assessment of Patients with Suspected Alzheimer's Disease and High Diagnostic Uncertainty: An EADC Study

Background: NIA-AA and IWG diagnostic criteria for Alzheimer's Disease (AD) include core structural, functional, and CSF biomarkers. The impact of core biomarkers in clinical settings is still unclear. This study aimed at measuring the impact of core biomarkers on the diagnostic confidence of uncertain AD cases in a routine memory clinic setting. // Methods: 356 patients with mild dementia (MMSE = 20) or Mild Cognitive Impairment possibly due to AD were recruited in 17 European Alzheimer's Disease Consortium (EADC) memory clinics. The following variables were collected: age; sex; MMSE; neuropsychological evaluation including long term memory, executive functions, language and visuospatial abilities. Core biomarkers were collected following local practices: Scheltens’s visual assessment of medial temporal atrophy (MTA) on MR scan; visual assessment of hypometabolism/hypoperfusion on FDG-PET/SPECT brain scan; CSF Aß1-42, tau and phospho-tau levels. At diagnostic workup completion, an estimate of confidence that cognitive complaints were due to AD was elicited from clinicians on a structured scale ranging from 0 to 100. Only cases with uncertain diagnoses (confidence between 15% and 85%) were retained for analysis. Generalized linear models were used to describe the relationship between the collected measures and the diagnostic confidence of AD. // Results: Neuropsychological assessment was carried out in almost all cases (98% of the cases). Medial temporal atrophy ratings were done in 40% of cases, assessment of cortical hypometabolism/hypoperfusion in 34%, and CSF Aß and tau levels in 26%. The markers that better explained the variability of diagnostic confidence were CSF Aß1-42 level (R2=0.46) and hypometabolism/hypoperfusion (R2=0.45), followed by CSF tau level (R2=0.35), MTA assessment (R2=0.32) and. All figures were highly significant, at p<<0.001. The diagnostic confidence variability due to neuropsychological tests for different domains was lower: MMSE (R2=0.29); long term memory (R2=0.23); executive functions (R2=0.05); language (R2=0.02); visuospatial abilities (R2=0.04) even if significant (p<0.01). // Conclusions: The use of core biomarkers in the clinical assessment of subjects with suspected AD and high diagnostic uncertainty is still limited. However, when assessed, these biomarkers show a higher impact on diagnostic confidence of AD than the most widespread clinical measures

Brain health in diverse settings : How age, demographics and cognition shape brain function

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