Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18–72) and one magnetoencephalography (n = 31, ages 20–75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.

[1]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[2]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[3]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[4]  E. G. Jones Cerebral Cortex , 1987, Cerebral Cortex.

[5]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[6]  K. Krishnan,et al.  Aging of the human corpus callosum: magnetic resonance imaging in normal volunteers. , 1991, The Journal of neuropsychiatry and clinical neurosciences.

[7]  Yi-Cheng Zhang Complexity and 1/f noise. A phase space approach , 1991 .

[8]  R. Emmerson,et al.  EEG and event-related potentials in normal aging , 1993, Progress in Neurobiology.

[9]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[10]  James Theiler,et al.  Generalized redundancies for time series analysis , 1995 .

[11]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[12]  Frank H. Duffy,et al.  Effects of age upon interhemispheric EEG coherence in normal adults , 1996, Neurobiology of Aging.

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

[14]  N. Birbaumer,et al.  Age increases brain complexity. , 1996, Electroencephalography and clinical neurophysiology.

[15]  J. V. Haxby,et al.  Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares , 1996, NeuroImage.

[16]  R. Kikinis,et al.  White matter changes with normal aging , 1998, Neurology.

[17]  Cheryl L Grady,et al.  Brain imaging and age-related changes in cognition , 1998, Experimental Gerontology.

[18]  P. Nunez,et al.  Spatial filtering and neocortical dynamics: estimates of EEG coherence , 1998, IEEE Transactions on Biomedical Engineering.

[19]  R. Emmerson,et al.  Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency , 1999, Clinical Neurophysiology.

[20]  J. Sloane,et al.  Increased microglial activation and protein nitration in white matter of the aging monkey☆ , 1999, Neurobiology of Aging.

[21]  Olaf Sporns,et al.  Connectivity and complexity: the relationship between neuroanatomy and brain dynamics , 2000, Neural Networks.

[22]  J A Kelso,et al.  Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[23]  M. Kikuchi,et al.  Effect of Normal Aging upon Interhemispheric EEG Coherence: Analysis during Rest and Photic Stimulation , 2000, Clinical EEG.

[24]  P. Greenwood,et al.  The frontal aging hypothesis evaluated , 2000, Journal of the International Neuropsychological Society.

[25]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[26]  J. Townsend,et al.  Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. , 2000, Radiology.

[27]  Claude E. Shannon,et al.  A mathematical theory of communication , 1948, MOCO.

[28]  Haruyasu Yamada,et al.  Normal aging in the central nervous system: quantitative MR diffusion-tensor analysis , 2002, Neurobiology of Aging.

[29]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[30]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[31]  S. Black,et al.  Evidence from Functional Neuroimaging of a Compensatory Prefrontal Network in Alzheimer's Disease , 2003, The Journal of Neuroscience.

[32]  K. Pribram,et al.  Age Differences in Dynamic Measures of EEG , 2004, Brain Topography.

[33]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

[34]  G. Bartzokis,et al.  Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical “disconnection” in aging and Alzheimer’s disease , 2004, Neurobiology of Aging.

[35]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[36]  D. Head,et al.  Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. , 2004, Cerebral cortex.

[37]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Egon Wanke,et al.  Mapping brains without coordinates , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[39]  Paul L. Nunez,et al.  Generation of human EEG by a combination of long and short range neocortical interactions , 2005, Brain Topography.

[40]  A. N. Mamelak,et al.  Long-range temporal correlations in the spontaneous spiking of neurons in the hippocampal-amygdala complex of humans , 2005, Neuroscience.

[41]  Jonas Persson,et al.  Structure-function correlates of cognitive decline in aging. , 2006, Cerebral cortex.

[42]  D. Cheyne,et al.  Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event‐related beamforming approach , 2006, Human brain mapping.

[43]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[44]  N. Raz,et al.  Differential Aging of the Brain: Patterns, Cognitive Correlates and Modifiers , 2022 .

[45]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[46]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[47]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[48]  Anders M. Dale,et al.  Frontal connections and cognitive changes in normal aging rhesus monkeys: A DTI study , 2007, Neurobiology of Aging.

[49]  A. McIntosh,et al.  The Interplay of Stimulus Modality and Response Latency in Neural Network Organization for Simple Working Memory Tasks , 2007, The Journal of Neuroscience.

[50]  Christopher G. Wilson,et al.  The effect of time delay on Approximate & Sample Entropy calculations , 2008 .

[51]  S. Rombouts,et al.  Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.

[52]  Natasa Kovacevic,et al.  Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development , 2008, PLoS Comput. Biol..

[53]  N. Raz,et al.  Aging white matter and cognition: Differential effects of regional variations in diffusion properties on memory, executive functions, and speed , 2009, Neuropsychologia.

[54]  Denise C. Park,et al.  The adaptive brain: aging and neurocognitive scaffolding. , 2009, Annual review of psychology.

[55]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[56]  T. Mizuno,et al.  Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis , 2009, Clinical Neurophysiology.

[57]  Natasa Kovacevic,et al.  Differential Maturation of Brain Signal Complexity in the Human Auditory and Visual System , 2009, Frontiers in human neuroscience.

[58]  N. Kovacevic,et al.  Hippocampal signal complexity in mesial temporal lobe epilepsy: a noisy brain is a healthy brain. , 2010, Archives italiennes de biologie.

[59]  Joseph T. Gwin,et al.  Motor control and aging: Links to age-related brain structural, functional, and biochemical effects , 2010, Neuroscience & Biobehavioral Reviews.

[60]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[61]  John A. E. Anderson,et al.  A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. , 2010, Cerebral cortex.

[62]  Torsten Rohlfing,et al.  Longitudinal Study of Callosal Microstructure in the Normal Adult Aging Brain Using Quantitative DTI Fiber Tracking , 2010, Developmental neuropsychology.

[63]  M. Molnár,et al.  Age-dependent features of EEG-reactivity—Spectral, complexity, and network characteristics , 2010, Neuroscience Letters.

[64]  Margot J. Taylor,et al.  Brain noise is task dependent and region specific. , 2010, Journal of neurophysiology.

[65]  C. Grady,et al.  The Importance of Being Variable , 2011, The Journal of Neuroscience.

[66]  Vasily A. Vakorin,et al.  Variability of Brain Signals Processed Locally Transforms into Higher Connectivity with Brain Development , 2011, The Journal of Neuroscience.

[67]  A. McIntosh,et al.  The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain. , 2011, Journal of neurophysiology.

[68]  Anthony Randal McIntosh,et al.  Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.

[69]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[70]  S. Baron-Cohen,et al.  Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis , 2011, Clinical Neurophysiology.

[71]  Anthony Randal McIntosh,et al.  Visual dominance and multisensory integration changes with age , 2013, NeuroImage.

[72]  C. Grady,et al.  The modulation of BOLD variability between cognitive states varies by age and processing speed. , 2013, Cerebral cortex.