The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint

ABSTRACT Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting‐state magnetic resonance imaging (rs‐fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a “chronnectome” approach (recurring, time‐varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472at‐risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD‐related pathogenetic mutations and 223 mutation non‐carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding‐time window correlation to rs‐fMRI data, and meta‐state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta‐states, shifting less often across them, and travelling through a narrowed meta‐state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials. HIGHLIGHTSFrontotemporal Dementia is preceded by a long period of subtle brain changes.Time‐varying dynamic connectivity can unveiled underappreciated brain details.Presymptomatic Frontotemporal Dementia exhibits a reduced dynamic fluidity.Frontotemporal Dementia showed a selective vulnerability of specific brain regions.At the very early stage Frontotemporal Dementia is affecting brain as global system.

[1]  Yong He,et al.  Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns , 2018, Human brain mapping.

[2]  Franco Cauda,et al.  Multimodal fMRI Resting-State Functional Connectivity in Granulin Mutations: The Case of Fronto-Parietal Dementia , 2014, PloS one.

[3]  C. Stam,et al.  Direction of information flow in large-scale resting-state networks is frequency-dependent , 2016, Proceedings of the National Academy of Sciences.

[4]  Juan Zhou,et al.  Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states , 2016, Proceedings of the National Academy of Sciences.

[5]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[6]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[7]  David T. Jones,et al.  Altered functional connectivity in asymptomatic MAPT subjects , 2011, Neurology.

[8]  David Bartrés-Faz,et al.  Distinctive age-related temporal cortical thinning in asymptomatic granulin gene mutation carriers , 2013, Neurobiology of Aging.

[9]  Yufeng Zang,et al.  DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging , 2016, Neuroinformatics.

[10]  Howard J. Rosen,et al.  Network degeneration and dysfunction in presymptomatic C9ORF72 expansion carriers , 2016, NeuroImage: Clinical.

[11]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[12]  K. Koepsell,et al.  Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies , 2010, Proceedings of the National Academy of Sciences.

[13]  James M. Shine,et al.  Shaped by our thoughts – A new task to assess spontaneous cognition and its associated neural correlates in the default network , 2015, Brain and Cognition.

[14]  Viviana Betti,et al.  Cortical cores in network dynamics , 2018, NeuroImage.

[15]  Giovanni B. Frisoni,et al.  Pattern of structural and functional brain abnormalities in asymptomatic granulin mutation carriers , 2014, Alzheimer's & Dementia.

[16]  D Perani,et al.  Brain magnetic resonance imaging structural changes in a pedigree of asymptomatic progranulin mutation carriers. , 2008, Rejuvenation research.

[17]  Toru Yanagawa,et al.  Analysis of ongoing dynamics in neural networks , 2009, Neuroscience Research.

[18]  Gustavo Deco,et al.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? , 2016, NeuroImage.

[19]  Mary Beth Nebel,et al.  The impact of T1 versus EPI spatial normalization templates for fMRI data analyses , 2017, Human brain mapping.

[20]  Philip D. Harvey,et al.  Administration and interpretation of the Trail Making Test , 2006, Nature Protocols.

[21]  Russell A. Poldrack,et al.  Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.

[22]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[23]  Vince D. Calhoun,et al.  Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information , 2015, NeuroImage.

[24]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[25]  Xiaoping Hu,et al.  Behavioral Relevance of the Dynamics of the Functional Brain Connectome , 2014, Brain Connect..

[26]  A. Belger,et al.  Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia , 2014, NeuroImage: Clinical.

[27]  Lucina Q. Uddin,et al.  Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks , 2016, Scientific Reports.

[28]  Vince D. Calhoun,et al.  Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  P. Hluštík,et al.  Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.

[30]  V. Calhoun,et al.  Changing brain connectivity dynamics: From early childhood to adulthood , 2018, Human brain mapping.

[31]  Jessica A. Turner,et al.  Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients , 2016, PloS one.

[32]  Ben D. Fulcher,et al.  An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI , 2017, NeuroImage.

[33]  Vince D. Calhoun,et al.  The chronnectome: Evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[34]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[35]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[36]  Vince D. Calhoun,et al.  A method for functional network connectivity among spatially independent resting-state components in schizophrenia , 2008, NeuroImage.

[37]  B. Borroni,et al.  Looking for Neuroimaging Markers in Frontotemporal Lobar Degeneration Clinical Trials: A Multi-Voxel Pattern Analysis Study in Granulin Disease. , 2016, Journal of Alzheimer's disease : JAD.

[38]  C R Jack,et al.  Voxel-based morphometry patterns of atrophy in FTLD with mutations in MAPT or PGRN , 2009, Neurology.

[39]  Roger A. Barker,et al.  The Cambridge Behavioural Inventory revised , 2008, Dementia & neuropsychologia.

[40]  P. Fries A mechanism for cognitive dynamics: neuronal communication through neuronal coherence , 2005, Trends in Cognitive Sciences.

[41]  Kent A. Kiehl,et al.  A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[42]  M. Jorge Cardoso,et al.  Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study , 2017, Brain : a journal of neurology.

[43]  S. Cappa,et al.  Brain connectivity in neurodegenerative diseases—from phenotype to proteinopathy , 2014, Nature Reviews Neurology.

[44]  Jason D. Warren,et al.  Disintegrating Brain Networks: from Syndromes to Molecular Nexopathies , 2012, Neuron.

[45]  D. Lehmann,et al.  Segmentation of brain electrical activity into microstates: model estimation and validation , 1995, IEEE Transactions on Biomedical Engineering.

[46]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[47]  V. Calhoun,et al.  Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects , 2014, Front. Hum. Neurosci..

[48]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[49]  Chin-Hui Lee,et al.  Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states , 2016, NeuroImage.

[50]  Carlo Caltagirone,et al.  Progranulin genetic variations in frontotemporal lobar degeneration: evidence for low mutation frequency in an Italian clinical series , 2008, Neurogenetics.

[51]  Efstathios D. Gennatas,et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. , 2010, Brain : a journal of neurology.

[52]  J. Morris,et al.  The Uniform Data Set (UDS): Clinical and Cognitive Variables and Descriptive Data From Alzheimer Disease Centers , 2006, Alzheimer disease and associated disorders.

[53]  Vince D Calhoun,et al.  Dynamic functional connectivity of neurocognitive networks in children , 2017, Human brain mapping.

[54]  Nick C Fox,et al.  Molecular nexopathies: a new paradigm of neurodegenerative disease , 2013, Trends in Neurosciences.

[55]  Stanley Durrleman,et al.  Lateral Temporal Lobe: An Early Imaging Marker of the Presymptomatic GRN Disease? , 2015, Journal of Alzheimer's disease : JAD.

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

[57]  Roberto Gasparotti,et al.  Effect of TMEM106B polymorphism on functional network connectivity in asymptomatic GRN mutation carriers. , 2014, JAMA neurology.

[58]  Scott Makeig,et al.  Information-based modeling of event-related brain dynamics. , 2006, Progress in brain research.

[59]  Hao He,et al.  Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches , 2016, Human brain mapping.

[60]  Kaustubh Supekar,et al.  Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network , 2016, PLoS biology.

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

[62]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[63]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[64]  B. Miller,et al.  Classification of primary progressive aphasia and its variants , 2011, Neurology.

[65]  Vince D. Calhoun,et al.  Replicability of time-varying connectivity patterns in large resting state fMRI samples , 2017, NeuroImage.

[66]  Patrizia Rizzu,et al.  Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia , 2013, Neurology.

[67]  Olaf Sporns,et al.  Communication dynamics in complex brain networks , 2017, Nature Reviews Neuroscience.

[68]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[69]  Veronica Redaelli,et al.  Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis , 2015, The Lancet Neurology.

[70]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[71]  Vince D. Calhoun,et al.  Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity , 2016, NeuroImage.