Brain oscillations; Circadian; Cognitive aging; Diurnal rhythms; Integration; Multilayer; Recruitment; RsFC; Aging; Veterinary (miscellaneous); Complementary and Alternative Medicine; Geriatrics and Gerontology; Cardiology and Cardiovascular Medicine
Abstract :
[en] Resting-state functional connectivity (rsFC) is a highly dynamic process that varies across different times of the day within each individual. Although this variability was long considered to be noise, recent evidence suggests it may allow for an optimal adaptation to changes in the environment. However, the way rsFC is shaped on a circadian scale and its association with cognition are still unclear. We analyzed data from 90 late middle-aged participants from the Cognitive Fitness in Aging study (61 women; 50-69 years). Participants completed five electroencephalographic (EEG) recordings of spontaneous resting-state activity spread over 20 h of prolonged wakefulness. Using a temporal multilayer network approach, we characterized the diurnal variations of the dynamic recruitment and integration of resting-state brain networks. We focused on the theta and gamma frequency bands within the default mode network (DMN), central executive network (CEN), and salience network (SN). Additionally, we investigated the relationship between the recruitment and integration of these networks with baseline cognitive performance and at a 7-year longitudinal follow-up, as well as with positron emission tomography (PET) early neuropathological markers of Alzheimer's disease such as β-amyloid and tau/neuroinflammation. Diurnal changes in theta and gamma dynamics were associated with distinct cognitive aspects. Specifically, higher baseline memory performance was associated with higher theta dynamic integration of the SN and the CEN, as well as higher theta dynamic recruitment of the DMN. Moreover, lower longitudinal memory decline at 7 years was associated with higher theta dynamic integration of the SN, CEN, and DMN. In contrast, higher gamma diurnal dynamic integration of the SN and the CEN was associated with lower executive and attentional performance, as well as higher early β-amyloid accumulation, at baseline. These findings suggest that maintaining a balance between network flexibility and stability throughout the diurnal phase of the circadian cycle may play a crucial role in cognitive aging, with stable theta-band connectivity supporting memory, whereas excessive gamma-band stability in the SN and CEN may contribute to executive decline and early amyloid accumulation. These insights highlight the importance of considering time-of-day in brain rsFC studies, calling for a temporal multilayer approach to capture these dynamic patterns more effectively.
Disciplines :
Neurosciences & behavior
Author, co-author :
Bennis, Kenza; Inserm, U1077, EPHE, UNICAEN, Normandie Université, PSL Université Paris, CHU de Caen, GIP Cyceron, Neuropsychologie Et Imagerie de La Mémoire Humaine (NIMH), Caen, 14000, France
Canal-Garcia, Anna; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
Pereira, Joana B; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
Volpe, Giovanni; Department of Physics, University of Gothenburg, Gothenburg, Sweden
Eustache, Francis; Inserm, U1077, EPHE, UNICAEN, Normandie Université, PSL Université Paris, CHU de Caen, GIP Cyceron, Neuropsychologie Et Imagerie de La Mémoire Humaine (NIMH), Caen, 14000, France
Phillips, Christophe ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore)
F.R.S.-FNRS - Fonds de la Recherche Scientifique FWB - Fédération Wallonie-Bruxelles ULiège - University of Liège Fondation Pierre et Simone Clerdent ERDF - European Regional Development Fund Swedish Research Council KI - Karolinska Institute Stiftelsen för Gamla Tjänarinnor EU - European Union
Funding text :
Funding for this project was provided by Fonds National de la Recherche Scientifique (FRS-FNRS, FRSM 3.4516.11, F.4513.17, and T.0242.19, EOS Project MEMODYN No. 30446199; Belgium), the Wallonia-Brussels Federation (Grant for Concerted Research Actions\u2014SLEEPDEM 17/27\u201309), Stop Alzheimer Foundation (Belgium, grants 15018, 2019/0025), University of Li\u00E8ge, Fondation Simone et Pierre Clerdent, European Regional Development Fund (ERDF, Radiomed Project). [18F]Flutemetamol doses were provided and cost covered by GE Healthcare Ltd (Little Chalfont, UK) as part of an investigator-sponsored study (ISS290) agreement. This agreement had no influence on the protocol and results of the study reported here. JBP and ACG were supported by the Swedish Research Council (#2022-01108), the Swedish Alzheimer Foundation (#AF-968323), a Consolidator Karolinska Institute grant, the Swedish Brain Foundation (FO2022-0147), Gamla Tj\u00E4narinnor (#2020-01016; #2021-01207; #2022-01341), KI foundations, Stohnes or the project \u201CA Multimodal Brain Connectivity Marker for the Early Detection of Alzheimer\u2019s Disease\u201D funded by the European Union \u2013 NextGenerationEU and the Romanian Government, under the National Recovery and Resilience Plan for Romania, contract no. 760250/28.12.2023, cod PNRR-C9-I8-CF109/31.07.2023, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, Investment I8.
Anderson ND Craik FIM 50 years of cognitive aging theory J Gerontol B Psychol Sci Soc Sci 2017 72 1 1 6 10.1093/geronb/gbw108 27974471
Andrews-Hanna JR Reidler JS Sepulcre J Poulin R Buckner RL Functional-anatomic fractionation of the brain’s default network Neuron 2010 65 4 550 62 1:CAS:528:DC%2BC3cXltlWku78%3D 10.1016/j.neuron.2010.02.005 20188659 2848443
Babiloni C Blinowska K Bonanni L Cichocki A De Haan W Del Percio C et al. What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons Neurobiol Aging 2020 85 58 73 10.1016/j.neurobiolaging.2019.09.008 31739167
Bakhtiari A Petersen J Urdanibia-Centelles O Ghazi MM Fagerlund B Mortensen EL et al. Power and distribution of evoked gamma oscillations in brain aging and cognitive performance Geroscience 2023 45 3 1523 38 10.1007/s11357-023-00749-x 36763241 10400513
Başar E Düzgün A How is the brain working? Int J Psychophysiol 2016 103 3 11 10.1016/j.ijpsycho.2015.02.007 25660309
Bassett DS Wymbs NF Porter MA Mucha PJ Carlson JM Grafton ST Dynamic reconfiguration of human brain networks during learning Proc Natl Acad Sci U S A 2011 108 18 7641 6 1:CAS:528:DC%2BC3MXmtV2lsLk%3D 10.1073/pnas.1018985108 21502525 3088578
Bassett DS Yang M Wymbs NF Grafton ST Learning-induced autonomy of sensorimotor systems Nat Neurosci 2015 18 5 744 51 1:CAS:528:DC%2BC2MXmtVarsbs%3D 10.1038/nn.3993 25849989 6368853
Benjamini Y Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Series B Stat Methodol 1995 57 1 289 300 10.1111/j.2517-6161.1995.tb02031.x
Bennis K, Eustache F, Collette F, Vandewalle G, & Hinault T. Daily dynamics of resting-state EEG theta and gamma fluctuations are associated with cognitive performance in healthy aging. J Gerontol B Psychol Sci Soc Sci, 2024;gbae152. https://doi.org/10.1093/geronb/gbae152
Blautzik J Vetter C Peres I Gutyrchik E Keeser D Berman A Kirsch V Mueller S Pöppel E Reiser M Roenneberg T Meindl T Classifying fMRI-derived resting-state connectivity patterns according to their daily rhythmicity Neuroimage 2013 71 298 306 10.1016/j.neuroimage.2012.08.010 22906784
Cabeza R Albert M Belleville S Craik FIM Duarte A Grady CL et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing Nat Rev Neurosci 2018 19 11 701 10 1:CAS:528:DC%2BC1cXhvFygurjL 10.1038/s41583-018-0068-2 30305711 6472256
Calhoun VD Miller R Pearlson G Adalı T The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery Neuron 2014 84 2 262 74 1:CAS:528:DC%2BC2cXhvVSmtrnM 10.1016/j.neuron.2014.10.015 25374354 4372723
Canal-Garcia A Veréb D Mijalkov M Westman E Volpe G Pereira JB Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer’s disease Cereb Cortex 2024 10.1093/cercor/bhad542 38212285 10839846
Chan MY Alhazmi FH Park DC Savalia NK Wig GS Resting-state network topology differentiates task signals across the adult life span J Neurosci 2017 37 10 2734 45 1:CAS:528:DC%2BC2sXhvFSisLvL 10.1523/JNEUROSCI.2406-16.2017 28174333 5354325
Chand GB Wu J Hajjar I Qiu D Interactions of the salience network and its subsystems with the default-mode and the central-executive networks in normal aging and mild cognitive impairment Brain Connect 2017 7 7 401 412 10.1089/brain.2017.0509 28707959 5647507
Chang Y-W, Zufiria-Gerbolés B, Gómez-Ruiz E, Canal-Garcia A, Zhao H, Mijalkov M, et al. BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences. bioRxiv. https://doi.org/10.1101/2025.04.11.648455
Chen AC Oathes DJ Chang C Bradley T Zhou ZW Williams LM et al. Causal interactions between fronto-parietal central executive and default-mode networks in humans Proc Nat Acad Sci. 2013 110 49 19944 19949 1:CAS:528:DC%2BC3sXhvFKms7vP 10.1073/pnas.1311772110 24248372 3856839
Chylinski DO Van Egroo M Narbutas J Grignard M Koshmanova E Berthomier C et al. Heterogeneity in the links between sleep arousals, amyloid-β, and cognition JCI Insight 2021 6 24 10.1172/jci.insight.152858 34784296 8783672 e152858
Chylinski D Van Egroo M Narbutas J Muto V Bahri MA Berthomier C et al. Timely coupling of sleep spindles and slow waves linked to early amyloid-β burden and predicts memory decline Elife 2022 11 1:CAS:528:DC%2BB38XitlCmsb7J 10.7554/eLife.78191 35638265 9177143 e78191
Courtney SM Hinault T When the time is right: temporal dynamics of brain activity in healthy aging and dementia Prog Neurobiol 2021 203 1:STN:280:DC%2BB2c%2FntVaguw%3D%3D 10.1016/j.pneurobio.2021.102076 34015374 102076
Da Silva Castanheira J Orozco Perez HD Misic B Baillet S Brief segments of neurophysiological activity enable individual differentiation Nat Commun 2021 12 1 1:CAS:528:DC%2BB3MXitFCgtLvE 10.1038/s41467-021-25895-8 34588439 8481307 5713
Damoiseaux JS Effects of aging on functional and structural brain connectivity Neuroimage 2017 160 32 40 10.1016/j.neuroimage.2017.01.077 28159687
Desikan RS Ségonne F Fischl B Quinn BT Dickerson BC Blacker D Buckner RL Dale AM Maguire RP Hyman BT Albert MS Killiany RJ An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest Neuroimage 2006 31 3 968 980 10.1016/j.neuroimage.2006.01.021 16530430
Drinkenburg W (Pim), Tok S, Ahnaou A. Functional neurophysiological biomarkers of early‐stage alzheimer’s disease: an experimental perspective of network hyperexcitability in disease progression and pharmacological interventions. Alzheimer’s & Dementia. 2022;18(S4):e060269. https://doi.org/10.1002/alz.060269.
Droby A Varangis E Habeck C Hausdorff JM Stern Y Mirelman A et al. Effects of aging on cognitive and brain inter-network integration patterns underlying usual and dual-task gait performance Front Aging Neurosci 2022 14 10.3389/fnagi.2022.956744 36247996 9557358 956744
Facer-Childs ER Campos BM Middleton B Skene DJ Bagshaw AP Circadian phenotype impacts the brain’s resting-state functional connectivity, attentional performance, and sleepiness Sleep 2019 42 5 10.1093/sleep/zsz033 30763951 6519915 zsz033
Farahani FV Karwowski W D’Esposito M Betzel RF Douglas PK Sobczak AM Bohaterewicz B Marek T Fafrowicz M Diurnal variations of resting-state fMRI data: a graph-based analysis Neuroimage 2022 256 10.1016/j.neuroimage.2022.119246 35477020 119246
Fell J Axmacher N The role of phase synchronization in memory processes Nat Rev Neurosci 2011 12 2 105 18 1:CAS:528:DC%2BC3MXnvVGluw%3D%3D 10.1038/nrn2979 21248789
Gaggioni G, Ly JQM, Muto V, Chellappa SL, Jaspar M, Meyer C, Delfosse T, Vanvinckenroye A, Dumont R, Coppieters ’T Wallant D, Berthomier C, Narbutas J, Van Egroo M, Luxen A, Salmon E, Collette F, Phillips C, Schmidt C, & Vandewalle G. Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition. Neurobiol Aging. 2019;78:52–63. https://doi.org/10.1016/j.neurobiolaging.2019.02.004
Garrett DD Kovacevic N McIntosh AR Grady CL The importance of being variable J Neurosci 2011 31 12 4496 503 1:CAS:528:DC%2BC3MXktVKrt7g%3D 10.1523/JNEUROSCI.5641-10.2011 21430150 3104038
Golchert J Smallwood J Jefferies E Seli P Huntenburg JM Liem F et al. Individual variation in intentionality in the mind-wandering state is reflected in the integration of the default-mode, fronto-parietal, and limbic networks Neuroimage 2017 146 226 35 10.1016/j.neuroimage.2016.11.025 27864082
Gramfort A Papadopoulo T Olivi E Clerc M OpenMEEG: opensource software for quasistatic bioelectromagnetics BioMed Eng OnLine 2010 9 1 10.1186/1475-925X-9-45 20819204 2949879 45
Guzmán-Vélez E Diez I Schoemaker D Pardilla-Delgado E Vila-Castelar C Fox-Fuller JT et al. Amyloid-β and tau pathologies relate to distinctive brain dysconnectomics in preclinical autosomal-dominant Alzheimer’s disease Proc Natl Acad Sci U S A 2022 119 15 1:CAS:528:DC%2BB38XhtFCjtr7N 10.1073/pnas.2113641119 35380901 9169643 e2113641119
He L Wang X Zhuang K Qiu J Decreased dynamic segregation but increased dynamic integration of the resting-state functional networks during normal aging Neuroscience 2020 437 54 63 1:CAS:528:DC%2BB3cXovVChur4%3D 10.1016/j.neuroscience.2020.04.030 32353459
Hinault T Mijalkov M Pereira JB Volpe G Bakke A Courtney SM Age-related differences in network structure and dynamic synchrony of cognitive control Neuroimage 2021 236 1:STN:280:DC%2BB3sbkt1Oisw%3D%3D 10.1016/j.neuroimage.2021.118070 33887473 118070
Hinault T Baillet S Courtney SM Age-related changes of deep-brain neurophysiological activity Cereb Cortex 2023 33 7 3960 8 1:STN:280:DC%2BB28%2FktFSquw%3D%3D 10.1093/cercor/bhac319 35989316
Jauny G Eustache F Hinault T Connectivity dynamics and cognitive variability during aging Neurobiol Aging 2022 118 99 105 1:STN:280:DC%2BB2MbmsVGktQ%3D%3D 10.1016/j.neurobiolaging.2022.07.001 35914474
Jensen O Gips B Bergmann TO Bonnefond M Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing Trends Neurosci 2014 37 7 357 69 1:CAS:528:DC%2BC2cXotVaqsLc%3D 10.1016/j.tins.2014.04.001 24836381
Jockwitz C Caspers S Resting-state networks in the course of aging—differential insights from studies across the lifespan vs. amongst the old Pflugers Arch 2021 473 5 793 803 1:CAS:528:DC%2BB3MXktVSjt70%3D 10.1007/s00424-021-02520-7 33576851 8076139
Khazaie H Veronese M Noori K Emamian F Zarei M Ashkan K et al. Functional reorganization in obstructive sleep apnoea and insomnia: a systematic review of the resting-state fMRI Neurosci Biobehav Rev 2017 77 219 31 10.1016/j.neubiorev.2017.03.013 28344075 6167921
Kumral D Şansal F Cesnaite E Mahjoory K Al E Gaebler M et al. BOLD and EEG signal variability at rest differently relate to aging in the human brain Neuroimage 2020 207 1:STN:280:DC%2BB3MfitlCrtA%3D%3D 10.1016/j.neuroimage.2019.116373 31759114 116373
Kryger MH, Roth T, & Dement WC. Principles and practice of sleep medicine E-book: expert consult-online and print. Elsevier Health Sciences; 2010.
La Corte V Piolino P Episodic foresight in normal cognitive and pathological aging Gériatrie et Psychologie Neuropsychiatrie du Viellissement 2016 14 1 58 66 10.1684/pnv.2016.0594
La Corte V Sperduti M Malherbe C Vialatte F Lion S Gallarda T et al. Cognitive decline and reorganization of functional connectivity in healthy aging: the pivotal role of the salience network in the prediction of age and cognitive performances Front Aging Neurosci. 2016 8 204 10.3389/fnagi.2016.00204 27616991 5003020
Lopez ME, Aurtenetxe S, Pereda E, Cuesta P, Castellanos NP, Bruna R, et al. Cognitive reserve is associated with the functional organization of the brain in healthy aging: A MEG study. Front Aging Neurosci. 2014;6. https://doi.org/10.3389/fnagi.2014.00125.
Lundqvist M Herman P Warden MR Brincat SL Miller EK Gamma and beta bursts during working memory readout suggest roles in its volitional control Nat Commun 2018 9 1 394 1:CAS:528:DC%2BC1cXhtFSntr3I 10.1038/s41467-017-02791-8 29374153 5785952
Ly JQM Gaggioni G Chellappa SL Papachilleos S Brzozowski A Borsu C et al. Circadian regulation of human cortical excitability Nat Commun 2016 7 1 1:CAS:528:DC%2BC28XhtVKiurfE 10.1038/ncomms11828 27339884 4931032 11828
Ma J Kim M Kim J Hong G Namgung E Park S et al. Decreased functional connectivity within the salience network after two-week morning bright light exposure in individuals with sleep disturbances: a preliminary randomized controlled trial Sleep Med 2020 74 66 72 10.1016/j.sleep.2020.05.009 32841846
Malagurski B Liem F Oschwald J Mérillat S Jäncke L Longitudinal functional brain network reconfiguration in healthy aging Hum Brain Mapp 2020 41 17 4829 45 10.1002/hbm.25161 32857461 7643380
Mattar MG Cole MW Thompson-Schill SL Bassett DS A functional cartography of cognitive systems PLoS Comput Biol 2015 11 12 1:CAS:528:DC%2BC28Xmtlajtrc%3D 10.1371/journal.pcbi.1004533 26629847 4668064 e1004533
Menon V Large-scale brain networks and psychopathology: a unifying triple network model Trends Cogn Sci 2011 15 10 483 506 10.1016/j.tics.2011.08.003 21908230
Menon V Palaniyappan L Supekar K Integrative brain network and salience models of psychopathology and cognitive dysfunction in schizophrenia Biol Psychiatry 2023 94 2 108 120 10.1016/j.biopsych.2022.09.029 36702660
Mijalkov M Kakaei E Pereira JB Westman E Volpe G BRAPH: a graph theory software for the analysis of brain connectivity PLoS ONE 2017 12 8 1:CAS:528:DC%2BC1cXnsVShs7g%3D 10.1371/journal.pone.0178798 28763447 5538719 e0178798
Miraglia F Vecchio F Rossini PM Searching for signs of aging and dementia in EEG through network analysis Behav Brain Res 2017 317 292 300 10.1016/j.bbr.2016.09.057 27693849
Missonnier P Herrmann FR Michon A Fazio-Costa L Gold G Giannakopoulos P Early disturbances of gamma band dynamics in mild cognitive impairment J Neural Transm 2010 117 4 489 498 10.1007/s00702-010-0384-9 20217436
Mucha PJ Richardson T Macon K Porter MA Onnela J-P Community structure in time-dependent, multiscale, and multiplex networks Science 2010 328 5980 876 8 1:CAS:528:DC%2BC3cXlvVeltr8%3D 10.1126/science.1184819 20466926
Murdock MH Yang CY Sun N et al. Multisensory gamma stimulation promotes glymphatic clearance of amyloid Nature 2024 627 149 156 1:CAS:528:DC%2BB2cXkslClurk%3D 10.1038/s41586-024-07132-6 38418876 10917684
Narbutas J Van Egroo M Chylinski D Bahri MA Koshmanova E Talwar P et al. Associations between cognitive complaints, memory performance, mood, and amyloid-β accumulation in healthy amyloid negative late-midlife individuals J Alzheimers Dis 2021 83 1 127 41 1:CAS:528:DC%2BB3MXhvFWrsL7J 10.3233/JAD-210332 34275899
Ng KK Lo JC Lim JKW Chee MWL Zhou J Reduced functional segregation between the default mode network and the executive control network in healthy older adults: a longitudinal study Neuroimage 2016 133 321 30 10.1016/j.neuroimage.2016.03.029 27001500
Oschmann M Gawryluk JR Alzheimer’s Disease Neuroimaging Initiative A longitudinal study of changes in resting-state functional magnetic resonance imaging functional connectivity networks during healthy aging Brain Connect 2020 10 7 377 84 10.1089/brain.2019.0724 32623915 7495915
Pascual-Marqui RD Michel CM Lehmann D Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain Int J Psychophysiol 1994 18 1 49 65 1:STN:280:DyaK2M7otFagsQ%3D%3D 10.1016/0167-8760(84)90014-X 7876038
Pedersen M Zalesky A Omidvarnia A Jackson GD Multilayer network switching rate predicts brain performance Proc Natl Acad Sci U S A 2018 115 52 13376 81 1:CAS:528:DC%2BC1cXisF2rsrbK 10.1073/pnas.1814785115 30545918 6310789
Pishdadian S Hoang NV Baker S Moscovitch M Rosenbaum RS Not only memory: investigating the sensitivity and specificity of the mnemonic similarity task in older adults Neuropsychologia 2020 149 1:STN:280:DC%2BB3s3hsVyntQ%3D%3D 10.1016/j.neuropsychologia.2020.107670 33157087 107670
Puxeddu MG Faskowitz J Betzel RF Petti M Astolfi L Sporns O The modular organization of brain cortical connectivity across the human lifespan Neuroimage 2020 218 10.1016/j.neuroimage.2020.116974 32450249 116974
Raichle ME The brain’s default mode network Annu Rev Neurosci 2015 38 433 47 1:CAS:528:DC%2BC2MXhsVCjurfJ 10.1146/annurev-neuro-071013-014030 25938726
Reuter-Lorenz PA, Park DC. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol Rev. 2014;24(3):355–70. https://doi.org/10.1007/s11065-014-9270-9.
Rizzolo L Narbutas J Van Egroo M Chylinski D Besson G Baillet M et al. Relationship between brain AD biomarkers and episodic memory performance in healthy aging Brain Cogn 2021 148 10.1016/j.bandc.2020.105680 33418512 105680
Rodrigue KM Kennedy KM Park DC Beta-amyloid deposition and the aging brain Neuropsychol Rev 2009 19 4 436 50 10.1007/s11065-009-9118-x 19908146 2844114
Sala-Llonch R, Bartrés-Faz D, Junqué C. Reorganization of brain networks in aging: A review of functional connectivity studies. Front Psychol. 2015;6. https://doi.org/10.3389/fpsyg.2015.00663.
Setton R Mwilambwe-Tshilobo L Girn M Lockrow AW Baracchini G Hughes C Lowe AJ Cassidy BN Li J Luh W-M Bzdok D Leahy RM Ge T Margulies DS Misic B Bernhardt BC Stevens WD De Brigard F Kundu P Spreng RN Age differences in the functional architecture of the human brain Cereb Cortex 2022 33 1 114 134 10.1093/cercor/bhac056 35231927 9758585
Sporns O Graph theory methods: applications in brain networks Dialogues Clin Neurosci 2018 20 2 111 121 10.31887/DCNS.2018.20.2/osporns 30250388 6136126
Stam CJ Nolte G Daffertshofer A Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources Hum Brain Mapp 2007 28 11 1178 93 10.1002/hbm.20346 17266107 6871367
Stanley ML Simpson SL Dagenbach D Lyday RG Burdette JH Laurienti PJ Changes in brain network efficiency and working memory performance in aging PLoS ONE 2015 10 4 10.1371/journal.pone.0123950 25875001 4395305 e0123950
Stark SM Yassa MA Lacy JW Stark CE A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment Neuropsychologia 2013 51 12 2442 2449 10.1016/j.neuropsychologia.2012.12.014 23313292 3675184
Tadel F Baillet S Mosher JC Pantazis D Leahy RM Brainstorm: a user-friendly application for MEG/EEG analysis Comput Intell Neurosci 2011 2011 1 13 10.1155/2011/879716
Thomas Yeo BT Krienen FM Sepulcre J Sabuncu MR Lashkari D Hollinshead M et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity J Neurophysiol 2011 106 3 1125 65 10.1152/jn.00338.2011 3174820
Toppi J Astolfi L Risetti M Anzolin A Kober SE Wood G Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis Front Hum Neurosci 2018 11 10.3389/fnhum.2017.00637 29379425 5770976 637
Tzourio-Mazoyer N Landeau B Papathanassiou D Crivello F Etard O Delcroix N et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain Neuroimage 2002 15 1 273 289 1:STN:280:DC%2BD38%2FltFCntw%3D%3D 10.1006/nimg.2001.0978 11771995
Uddin LQ Yeo BTT Spreng RN Towards a universal taxonomy of macro-scale functional human brain networks Brain Topogr 2019 32 6 926 42 10.1007/s10548-019-00744-6 31707621 7325607
Van Egroo M Chylinski D Narbutas J Besson G Muto V Schmidt C et al. Early brainstem [18F]THK5351 uptake is linked to cortical hyperexcitability in healthy aging JCI Insight 2021 6 2 10.1172/jci.insight.142514 33290274 7934880 e142514
Van Egroo M Narbutas J Chylinski D Villar González P Ghaemmaghami P Muto V et al. Preserved wake-dependent cortical excitability dynamics predict cognitive fitness beyond age-related brain alterations Commun Biol 2019 2 1 10.1038/s42003-019-0693-y 31815203 6890637 449
Varangis E Habeck CG Razlighi QR Stern Y The effect of aging on resting state connectivity of predefined networks in the brain Front Aging Neurosci 2019 11 10.3389/fnagi.2019.00234 31555124 6737010 234
Wig GS Segregated systems of human brain networks Trends Cogn Sci 2017 21 12 981 96 10.1016/j.tics.2017.09.006 29100737
