Mean and variability in functional brain activations differentially predict executive function in older adults: an investigation employing functional near-infrared spectroscopy

Abstract. Objective: although the preponderance of research on functional brain activity investigates mean group differences, mounting evidence suggests that variability in neural activity is beneficial for optimal central nervous system (CNS) function. Independent of mean signal estimates, recent findings have shown that neural variability diminishes with age and is positively associated with cognitive performance, underscoring its adaptive nature. The present investigation sought to employ functional near infrared spectroscopy (fNIRS) to derive two operationalizations of cerebral oxygenation, representing mean and variability [using standard deviation (SD)] in neural activity, and to specifically contrast these mean- and SD-oxyhemoglobin (HbO) estimates as predictors of cognitive function. Method: a total of 25 older adults (71 to 81 years of age) completed a test of cognitive interference (Multisource Interference Task) while undergoing fNIRS recording using a multichannel continuous-wave optical imaging system (TechEn CW6) over bilateral prefrontal cortex (PFC). Time-varying covariation models were employed to simultaneously estimate the within- and between-person effects of cerebral oxygenation on behavioral performance fluctuations. Results: mean effects were predominantly observed at the between-person level and suggest that greater concentrations of HbO are associated with slower and less accurate performance. Greater HbO variability at the between-person level was associated with slower performance, but was associated with faster performance at the within-person level. Conclusions: these findings are in keeping with assertions that mean and variability confer complementary (as opposed to redundant) sources of information regarding the effective functioning of a neural system and suggest that fNIRS is a viable methodology for capturing meaningful variance in the hemodynamic response that is characteristic of adaptive CNS function.

[1]  Kai Ueltzhöffer,et al.  Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability , 2016, The Journal of Neuroscience.

[2]  M. Sliwinski,et al.  Time-Based and Process-Based Approaches to Analysis of Longitudinal Data , 2008 .

[3]  R. Stawski,et al.  Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis , 2009 .

[4]  Keiji Iramina,et al.  Brain complexity analysis of functional near infrared spectroscopy for working memory study , 2015, 2015 8th Biomedical Engineering International Conference (BMEiCON).

[5]  G. Bush,et al.  The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network , 2006, Nature Protocols.

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

[7]  S. MacDonald,et al.  Neuroscience and Biobehavioral Reviews Review Moment-to-moment Brain Signal Variability: a next Frontier in Human Brain Mapping? , 2022 .

[8]  W. S. Robinson,et al.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[9]  David A. Boas,et al.  Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial , 2015, Neurophotonics.

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

[11]  P. Molenaar A Manifesto on Psychology as Idiographic Science: Bringing the Person Back Into Scientific Psychology, This Time Forever , 2004 .

[12]  Mark D. McDonnell,et al.  The benefits of noise in neural systems: bridging theory and experiment , 2011, Nature Reviews Neuroscience.

[13]  C. Grady The cognitive neuroscience of ageing , 2012, Nature Reviews Neuroscience.

[14]  C. Grady,et al.  Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise , 2010, The Journal of Neuroscience.

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

[16]  M. Rugg,et al.  Task-dependency of the neural correlates of episodic encoding as measured by fMRI. , 2001, Cerebral cortex.

[17]  A. Maki,et al.  Intersubject variability of near-infrared spectroscopy signals during sensorimotor cortex activation. , 2005, Journal of biomedical optics.

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

[19]  David A. Boas,et al.  Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data , 2014, NeuroImage.

[20]  B R Rosen,et al.  The Multi-Source Interference Task: validation study with fMRI in individual subjects , 2003, Molecular Psychiatry.

[21]  Meryem A Yücel,et al.  Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. , 2016, Biomedical optics express.

[22]  Ippeita Dan,et al.  Spatial registration for functional near-infrared spectroscopy: From channel position on the scalp to cortical location in individual and group analyses , 2014, NeuroImage.

[23]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[24]  M. Johnson,et al.  Correction to ‘Reduced neural sensitivity to social stimuli in infants at risk for autism’ , 2016, Proceedings of the Royal Society B: Biological Sciences.

[25]  Cheryl L. Grady,et al.  Understanding variability in the BOLD signal and why it matters for aging , 2013, Brain Imaging and Behavior.

[26]  L. Pinneo On noise in the nervous system. , 1966, Psychological review.

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

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

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

[30]  Peter N. C. Mohr,et al.  Variability in Brain Activity as an Individual Difference Measure in Neuroscience? , 2010, The Journal of Neuroscience.

[31]  D. Stuss,et al.  Staying on the job: the frontal lobes control individual performance variability. , 2003, Brain : a journal of neurology.

[32]  David A. Boas,et al.  A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy , 2012, Front. Neurosci..

[33]  M. Sliwinski,et al.  Understanding Ageing , 2001, Gerontology.

[34]  Natasa Kovacevic,et al.  Brain signal variability relates to stability of behavior after recovery from diffuse brain injury , 2012, NeuroImage.

[35]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[36]  S. MacDonald,et al.  Neural underpinnings of within-person variability in cognitive functioning. , 2009, Psychology and aging.

[37]  G. Dumont,et al.  Wavelet based motion artifact removal for Functional Near Infrared Spectroscopy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[38]  Archana K. Singh,et al.  Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI , 2005, NeuroImage.

[39]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.