Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

Significance Recently, it was shown that functional connectivity patterns exhibit complex spatiotemporal dynamics at the scale of tens of seconds. Of particular interest is the observation of a limited set of quasi-stable, whole-brain, recurring configurations—commonly referred to as functional connectivity states (FC states)—hypothesized to reflect the continuous flux of cognitive processes. Here, to test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. We demonstrate that there is a strong relationship between FC states and ongoing cognition that permits accurate tracking of mental states in individual subjects. We also demonstrate how informative changes in connectivity are not restricted solely to those regions with sustained elevations in activity during task performance. Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.

[1]  Bernard Ng,et al.  Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm. , 2016, Cerebral cortex.

[2]  M. Cugmas,et al.  On comparing partitions , 2015 .

[3]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

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

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

[6]  Laura C. Buchanan,et al.  The spatial structure of resting state connectivity stability on the scale of minutes , 2014, Frontiers in Neuroscience.

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

[8]  Bettina Sorger,et al.  Windowed Correlation: A Suitable Tool for Providing Dynamic fMRI-Based Functional Connectivity Neurofeedback on Task Difficulty , 2014, PloS one.

[9]  Dieter Jaeger,et al.  Neural correlates of time-varying functional connectivity in the rat , 2013, NeuroImage.

[10]  R. Cameron Craddock,et al.  Clinical applications of the functional connectome , 2013, NeuroImage.

[11]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[12]  Sergey V. Fogelson,et al.  Network structure and dynamics of the mental workspace , 2013, Proceedings of the National Academy of Sciences.

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

[14]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[15]  Shella D. Keilholz,et al.  Dynamic Properties of Functional Connectivity in the Rodent , 2013, Brain Connect..

[16]  Wen-Ming Luh,et al.  Effects of image contrast on functional MRI image registration , 2013, NeuroImage.

[17]  Mason A. Porter,et al.  Robust Detection of Dynamic Community Structure in Networks , 2012, Chaos.

[18]  Enzo Tagliazucchi,et al.  Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..

[19]  Daniel A. Handwerker,et al.  Periodic changes in fMRI connectivity , 2012, NeuroImage.

[20]  Enzo Tagliazucchi,et al.  Automatic sleep staging using fMRI functional connectivity data , 2012, NeuroImage.

[21]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[22]  Jakob Heinzle,et al.  Visuomotor Functional Network Topology Predicts Upcoming Tasks , 2012, The Journal of Neuroscience.

[23]  Martin A. Lindquist,et al.  Dynamic connectivity regression: Determining state-related changes in brain connectivity , 2012, NeuroImage.

[24]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[25]  Han Yuan,et al.  Spatiotemporal dynamics of the brain at rest — Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks , 2012, NeuroImage.

[26]  C. Dickey,et al.  Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study , 2012, American Journal of Neuroradiology.

[27]  Noah D. Brenowitz,et al.  Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis , 2012, Proceedings of the National Academy of Sciences.

[28]  Paul Pavlidis,et al.  Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity , 2012, PLoS Comput. Biol..

[29]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[30]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[31]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[32]  Dimitri Van De Ville,et al.  Decoding brain states from fMRI connectivity graphs , 2011, NeuroImage.

[33]  Juliane Britz,et al.  EEG microstate sequences in healthy humans at rest reveal scale-free dynamics , 2010, Proceedings of the National Academy of Sciences.

[34]  Tracy Warbrick,et al.  Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks , 2010, NeuroImage.

[35]  Dimitri Van De Ville,et al.  BOLD correlates of EEG topography reveal rapid resting-state network dynamics , 2010, NeuroImage.

[36]  G. Tononi.  Information integration: its relevance to brain function and consciousness. , 2010, Archives italiennes de biologie.

[37]  Hang Joon Jo,et al.  Mapping sources of correlation in resting state FMRI, with artifact detection and removal , 2010, NeuroImage.

[38]  Marc Joliot,et al.  The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state , 2010, Brain Research Bulletin.

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

[40]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[41]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[42]  Anil K. Seth,et al.  Consciousness and Complexity , 2022 .

[43]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[44]  D. Steinley.  Properties of the Hubert-Arabie adjusted Rand index. , 2004, Psychological methods.

[45]  B. Baars.  The conscious access hypothesis: origins and recent evidence , 2002, Trends in Cognitive Sciences.

[46]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[47]  T. Koenig,et al.  Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts. , 1998, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[48]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[49]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.