Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost‐effective technique for studying dementia globally, but lacks mechanistic models and produces non‐replicable results.

[1]  Morten L. Kringelbach,et al.  A synergetic turn in cognitive neuroscience of brain diseases , 2024, Trends in Cognitive Sciences.

[2]  Sol Fittipaldi,et al.  Socioeconomic disparities harm social cognition , 2024, Trends in Cognitive Sciences.

[3]  S. Baez,et al.  Global South research is critical for understanding brain health, ageing and dementia , 2023, Clinical and translational medicine.

[4]  G. Northoff,et al.  Brain-wide changes in excitation-inhibition balance of major depressive disorder: a systematic review of topographic patterns of GABA- and glutamatergic alterations , 2023, Molecular Psychiatry.

[5]  A. Ibáñez,et al.  Addressing the gaps between socioeconomic disparities and biological models of dementia , 2023, Brain.

[6]  A. Ibáñez,et al.  Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases , 2023, Research square.

[7]  M. Breakspear,et al.  State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer’s disease , 2023, Network Neuroscience.

[8]  M. A. Lopez-Gordo,et al.  Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease , 2023, bioRxiv.

[9]  A. Ibáñez,et al.  The EuroLaD‐EEG consortium: towards a global EEG platform for dementia, for seeking to reduce the regional impact of dementia , 2022, Alzheimer's & Dementia.

[10]  A. Ibáñez,et al.  Genuine high-order interactions in brain networks and neurodegeneration , 2022, Neurobiology of Disease.

[11]  C. Gillan,et al.  Developments in scalable strategies for detecting early markers of cognitive decline , 2022, Translational Psychiatry.

[12]  C. Caltagirone,et al.  Precuneus magnetic stimulation for Alzheimer’s disease: a randomized, sham-controlled trial , 2022, Brain : a journal of neurology.

[13]  C. Gießing,et al.  Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation , 2022, NeuroImage.

[14]  A. Destexhe,et al.  Dynamical models to evaluate structure–function relationships in network neuroscience , 2022, Nature Reviews Neuroscience.

[15]  M. Kringelbach,et al.  Model-based whole-brain perturbational landscape of neurodegenerative diseases , 2022, bioRxiv.

[16]  S. Risacher,et al.  Biomarkers for dementia in Latin American countries: Gaps and opportunities , 2022, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[17]  A. Ibanez,et al.  A predictive coding framework of allostatic–interoceptive overload in frontotemporal dementia , 2022, Trends in Neurosciences.

[18]  Adrienne S. Kline,et al.  PsmPy: A Package for Retrospective Cohort Matching in Python , 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[19]  A. Ibáñez,et al.  The Latin American Brain Health Institute, a regional initiative to reduce the scale and impact of dementia , 2022, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[20]  Justin W. M. Domhof,et al.  Reliability and subject specificity of personalized whole-brain dynamical models , 2022, NeuroImage.

[21]  Michael M. Craig,et al.  Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness , 2022, Communications Biology.

[22]  Timothy O. Laumann,et al.  Reproducible brain-wide association studies require thousands of individuals , 2022, Nature.

[23]  K. Vossel,et al.  Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease , 2022, bioRxiv.

[24]  B. Boeve,et al.  Advances and controversies in frontotemporal dementia: diagnosis, biomarkers, and therapeutic considerations , 2022, The Lancet Neurology.

[25]  G. Northoff,et al.  Intrinsic neural timescales: temporal integration and segregation , 2022, Trends in Cognitive Sciences.

[26]  A. Ibáñez,et al.  Multimodal Neurocognitive Markers of Naturalistic Discourse Typify Diverse Neurodegenerative Diseases. , 2021, Cerebral cortex.

[27]  A. Ibáñez,et al.  Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration , 2021, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[28]  Fernando E. Rosas,et al.  Greater than the parts: a review of the information decomposition approach to causal emergence , 2021, Philosophical Transactions of the Royal Society A.

[29]  D. E. Olson,et al.  Psychedelics and Other Psychoplastogens for Treating Mental Illness , 2021, Frontiers in Psychiatry.

[30]  Fernando E. Rosas,et al.  High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model , 2021, bioRxiv.

[31]  S. Baez,et al.  Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes , 2021, Journal of Alzheimer's disease : JAD.

[32]  Patricio Orio,et al.  Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation , 2021, Frontiers in Computational Neuroscience.

[33]  Teeba Athar,et al.  Recent advances on drug development and emerging therapeutic agents for Alzheimer’s disease , 2021, Molecular Biology Reports.

[34]  J. DeFelipe,et al.  Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology , 2021, Ageing Research Reviews.

[35]  Precision medicine needs an equity agenda , 2021, Nature Medicine.

[36]  J. Schott,et al.  Beyond the average patient: how neuroimaging models can address heterogeneity in dementia , 2021, Brain : a journal of neurology.

[37]  Miguel Martorell Caro,et al.  Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration , 2021, The Journal of Neuroscience.

[38]  R. Myers,et al.  The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat): Driving Multicentric Research and Implementation Science , 2021, Frontiers in Neurology.

[39]  Changsong Zhou,et al.  Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities , 2021, Proceedings of the National Academy of Sciences.

[40]  G. Stranjalis,et al.  Blood Biomarkers in Frontotemporal Dementia: Review and Meta-Analysis , 2021, Brain sciences.

[41]  A. Ibáñez,et al.  Metacognition of emotion recognition across neurodegenerative diseases , 2021, Cortex.

[42]  Diego Lombardo,et al.  Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox , 2020, MethodsX.

[43]  G. Logroscino,et al.  Biomarker Testing: Piercing the Fog of Alzheimer’s and Related Dementia , 2020, Biomedicine Hub.

[44]  Agustín Ibáñez,et al.  Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study , 2020, NeuroImage.

[45]  Pedro A. M. Mediano,et al.  A synergistic core for human brain evolution and cognition , 2020, bioRxiv.

[46]  Erik D. Fagerholm,et al.  A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders , 2020, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[47]  S. Vann Jones,et al.  Psychedelics as a Treatment for Alzheimer’s Disease Dementia , 2020, Frontiers in Synaptic Neuroscience.

[48]  Fernando E. Rosas,et al.  Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up , 2020, Brain sciences.

[49]  Naaheed Mukadam,et al.  Dementia prevention, intervention, and care: 2020 report of the Lancet Commission , 2020, The Lancet.

[50]  Andreas Daffertshofer,et al.  Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan , 2020, NeuroImage.

[51]  Ignacio Perez Ipiña,et al.  Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness , 2020, bioRxiv.

[52]  Morten L. Kringelbach,et al.  Modeling regional changes in dynamic stability during sleep and wakefulness , 2020, NeuroImage.

[53]  Jesus M. Cortes,et al.  High-order interdependencies in the aging brain , 2020, bioRxiv.

[54]  Diego Lombardo,et al.  Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation , 2020, NeuroImage.

[55]  Stephan Lewandowsky,et al.  Low replicability can support robust and efficient science , 2020, Nature Communications.

[56]  Gustavo Deco,et al.  Data augmentation based on dynamical systems for the classification of brain states , 2020, bioRxiv.

[57]  A. Ibáñez,et al.  Alzheimer's Disease or Behavioral Variant Frontotemporal Dementia? Review of Key Points Toward an Accurate Clinical and Neuropsychological Diagnosis. , 2019, Journal of Alzheimer's disease : JAD.

[58]  M. Abdel-Naseer.  Epidemiology of dementia in developing countries , 2019, Journal of the Neurological Sciences.

[59]  Morten L. Kringelbach,et al.  Awakening: Predicting external stimulation to force transitions between different brain states , 2019, Proceedings of the National Academy of Sciences.

[60]  Hanni Kiiski,et al.  A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment , 2019, NeuroImage.

[61]  Hovagim Bakardjian,et al.  EEG EVIDENCE OF COMPENSATORY MECHANISMS IN PRECLINICAL ALZHEIMER’S DISEASE , 2019, Alzheimer's & Dementia.

[62]  H. Higashida,et al.  Excitation/inhibition imbalance and impaired neurogenesis in neurodevelopmental and neurodegenerative disorders , 2019, Reviews in the neurosciences.

[63]  Andreas Spiegler,et al.  Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease , 2019, bioRxiv.

[64]  Horacio G. Rotstein,et al.  Task-evoked activity quenches neural correlations and variability across cortical areas , 2019, bioRxiv.

[65]  H. Bergman,et al.  Deep brain stimulation: current challenges and future directions , 2019, Nature Reviews Neurology.

[66]  O. Franco,et al.  Life Expectancy With and Without Dementia: A Population-Based Study of Dementia Burden and Preventive Potential , 2018, American journal of epidemiology.

[67]  Morten L. Kringelbach,et al.  Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD , 2018, Current Biology.

[68]  Christopher W. Lynn,et al.  The physics of brain network structure, function and control , 2018, Nature Reviews Physics.

[69]  Mark W. Woolrich,et al.  A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks , 2018, PLoS Comput. Biol..

[70]  S. Baez,et al.  Dementia in Latin America , 2018, Neurology.

[71]  Morten L. Kringelbach,et al.  Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data , 2017, NeuroImage.

[72]  M. Daulatzai,et al.  Cerebral hypoperfusion and glucose hypometabolism: Key pathophysiological modulators promote neurodegeneration, cognitive impairment, and Alzheimer's disease , 2017, Journal of neuroscience research.

[73]  Morten L. Kringelbach,et al.  The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling , 2017, NeuroImage.

[74]  M. Sigman,et al.  Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining. , 2016, Brain : a journal of neurology.

[75]  Viktor K. Jirsa,et al.  Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study , 2016, NeuroImage.

[76]  Gert Cauwenberghs,et al.  EEG channel interpolation using ellipsoid geodesic length , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[77]  G. F. González,et al.  Graph analysis of EEG resting state functional networks in dyslexic readers , 2016, Clinical Neurophysiology.

[78]  P. Bosco,et al.  Brain atrophy in Alzheimer’s Disease and aging , 2016, Ageing Research Reviews.

[79]  Neda Jahanshad,et al.  Disrupted rich club network in behavioral variant frontotemporal dementia and early‐onset Alzheimer's disease , 2015, Human brain mapping.

[80]  Marko A. Hofmann.  Searching for effects in big data: Why p-values are not advised and what to use instead , 2015, 2015 Winter Simulation Conference (WSC).

[81]  Yong He,et al.  Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease. , 2015, Cerebral cortex.

[82]  M. Agrawal,et al.  Molecular diagnostics of neurodegenerative disorders , 2015, Front. Mol. Biosci..

[83]  Timo Grimmer,et al.  Prefrontal Hypometabolism in Alzheimer Disease Is Related to Longitudinal Amyloid Accumulation in Remote Brain Regions , 2015, The Journal of Nuclear Medicine.

[84]  M. Kringelbach,et al.  Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders , 2014, Neuron.

[85]  David Huepe,et al.  Comparing moral judgments of patients with frontotemporal dementia and frontal stroke. , 2014, JAMA neurology.

[86]  M. Corbetta,et al.  How Local Excitation–Inhibition Ratio Impacts the Whole Brain Dynamics , 2014, The Journal of Neuroscience.

[87]  N. Bohnen,et al.  Brain PET in suspected dementia: patterns of altered FDG metabolism. , 2014, Radiographics : a review publication of the Radiological Society of North America, Inc.

[88]  Raul Vicente,et al.  Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment , 2013, Front. Aging Neurosci..

[89]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[90]  Puneet Kumar,et al.  Excitotoxicity: bridge to various triggers in neurodegenerative disorders. , 2013, European journal of pharmacology.

[91]  Manuel Graña,et al.  Model‐based analysis of multishell diffusion MR data for tractography: How to get over fitting problems , 2012, Magnetic resonance in medicine.

[92]  G. Chételat,et al.  Region-Specific Hierarchy between Atrophy, Hypometabolism, and β-Amyloid (Aβ) Load in Alzheimer's Disease Dementia , 2012, The Journal of Neuroscience.

[93]  C. Oluigbo,et al.  Deep Brain Stimulation for Neurological Disorders , 2012, IEEE Reviews in Biomedical Engineering.

[94]  Cornelis J. Stam,et al.  Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease , 2012, PLoS Comput. Biol..

[95]  F. Manes,et al.  Contextual social cognition and the behavioral variant of frontotemporal dementia , 2012, Neurology.

[96]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[97]  Nick C Fox,et al.  Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. , 2011, Brain : a journal of neurology.

[98]  Bin He,et al.  Dynamic imaging of ictal oscillations using non-invasive high-resolution EEG , 2011, NeuroImage.

[99]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[100]  C. Caltagirone,et al.  Anatomical connectivity mapping: A new tool to assess brain disconnection in Alzheimer's disease , 2011, NeuroImage.

[101]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[102]  C. Caltagirone,et al.  Regional brain atrophy and functional disconnection across Alzheimer's disease evolution , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

[103]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[104]  P. Scheltens,et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria , 2007, The Lancet Neurology.

[105]  Claudio Del Percio,et al.  Development and assessment of methods for detecting dementia using the human electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[106]  G. Rees.  Statistical Parametric Mapping , 2004, Practical Neurology.

[107]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[108]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[109]  Karl J. Friston,et al.  A neural mass model for MEG/EEG: coupling and neuronal dynamics , 2003, NeuroImage.

[110]  Haim Sompolinsky,et al.  Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity , 2003, The Journal of Neuroscience.

[111]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[112]  A. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[113]  H Sattel,et al.  Discrimination of Alzheimer's disease and normal aging by EEG data. , 1997, Electroencephalography and clinical neurophysiology.

[114]  G. Dirlich,et al.  Cardiac field effects on the EEG. , 1997, Electroencephalography and clinical neurophysiology.

[115]  P. Montoya,et al.  Event-related brain potentials and the processing of cardiac activity , 1996, Biological Psychology.

[116]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[117]  F Angeleri,et al.  EEG spectral analysis in vascular and Alzheimer dementia. , 1995, Electroencephalography and clinical neurophysiology.

[118]  T. Wiesel,et al.  Targets of horizontal connections in macaque primary visual cortex , 1991, The Journal of comparative neurology.

[119]  OUP accepted manuscript , 2021, Cerebral Cortex.

[120]  R J Harvey,et al.  Donepezil for dementia due to Alzheimer's disease. , 2006, The Cochrane database of systematic reviews.

[121]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[122]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .