The neurovascular basis of processing speed differences in humans: A model-systems approach using multiple sclerosis

Behavioral studies investigating fundamental cognitive abilities provide evidence that processing speed accounts for large proportions of performance variability between individuals. Processing speed decline is a hallmark feature of the cognitive disruption observed in healthy aging and in demyelinating diseases such as multiple sclerosis (MS), neuromyelitis optica, and Wilson's disease. Despite the wealth of evidence suggesting a central role for processing speed in cognitive decline, the neural mechanisms of this fundamental ability remain unknown. Intact neurovascular coupling, acute localized blood flow increases following neural activity, is essential for optimal neural function. We hypothesized that efficient coupling forms the neural basis of processing speed. Because MS features neural-glial-vascular system disruption, we used it as a model to test this hypothesis. To assess the integrity of the coupling system, we measured blood-oxygen-level-dependent (BOLD) signal in healthy controls (HCs) and MS patients using a 3T MRI scanner while they viewed radial checkerboards that flickered periodically at 8 Hz. To assess processing speed and cognitive function, we administered a battery of neuropsychological tests. While MS patients exhibited reduced ΔBOLD with reductions in processing speed, no such relationships were observed in HCs. To further investigate the mechanisms that underlie ΔBOLD-processing speed relationships, we assessed the physiologic components that constitute ΔBOLD signal (i.e., cerebral blood flow, ΔCBF; cerebral metabolic rate of oxygen, ΔCMRO2; neurovascular coupling ratio) in speed-preserved and -impaired MS patients. While ΔCBF and ΔCMRO2 showed no group-differences, the neurovascular coupling ratio was significantly reduced in speed-impaired MS patients compared to speed-preserved MS patients. Together, these results suggest that neurovascular uncoupling might underlie cognitive slowing in MS and might be the central pathogenic mechanism governing processing speed decline.

[1]  Stephen D. Mayhew,et al.  Investigating intrinsic connectivity networks using simultaneous BOLD and CBF measurements , 2014, NeuroImage.

[2]  M. D’Esposito,et al.  Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging , 2003, Nature Reviews Neuroscience.

[3]  Bart Rypma,et al.  Neural Mechanisms of Age-Related Slowing: The ΔCBF/ΔCMRO2 Ratio Mediates Age-Differences in BOLD Signal and Human Performance , 2012, Cerebral cortex.

[4]  Dinesh K. Sivakolundu,et al.  Reduced arterial compliance along the cerebrovascular tree predicts cognitive slowing in multiple sclerosis: Evidence for a neurovascular uncoupling hypothesis , 2019, Multiple sclerosis.

[5]  Wm. R. Wright.  General Intelligence, Objectively Determined and Measured. , 1905 .

[6]  E. Tucker-Drob,et al.  Differentiation of Cognitive Abilities across the Lifespan , 2010 .

[7]  Thomas T. Liu,et al.  A signal processing model for arterial spin labeling functional MRI , 2005, NeuroImage.

[8]  Matthew B. Bouchard,et al.  A Critical Role for the Vascular Endothelium in Functional Neurovascular Coupling in the Brain , 2014, Journal of the American Heart Association.

[9]  S. Crewther,et al.  Cognitive Processing Speed across the Lifespan: Beyond the Influence of Motor Speed , 2017, Front. Aging Neurosci..

[10]  J. DeLuca,et al.  The role of speed versus working memory in predicting learning new information in multiple sclerosis , 2013, Journal of clinical and experimental neuropsychology.

[11]  J. Parisi,et al.  Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination , 2000, Annals of neurology.

[12]  Hanzhang Lu,et al.  Detrimental effects of BOLD signal in arterial spin labeling fMRI at high field strength , 2006, Magnetic resonance in medicine.

[13]  J. DeLuca,et al.  Is Speed of Processing or Working Memory the Primary Information Processing Deficit in Multiple Sclerosis? , 2004, Journal of clinical and experimental neuropsychology.

[14]  Frédéric Blanc,et al.  Cognitive functions in neuromyelitis optica. , 2008, Archives of neurology.

[15]  Dinesh K. Sivakolundu,et al.  Preserved canonicality of the BOLD hemodynamic response reflects healthy cognition: Insights into the healthy brain through the window of Multiple Sclerosis , 2019, NeuroImage.

[16]  P. Baltes,et al.  Emergence of a powerful connection between sensory and cognitive functions across the adult life span: a new window to the study of cognitive aging? , 1997, Psychology and aging.

[17]  Fahmeed Hyder,et al.  Relationship between CMRO2 and Neuronal Activity , 2005 .

[18]  R. Ratcliff.  Methods for dealing with reaction time outliers. , 1993, Psychological bulletin.

[19]  H. Genova,et al.  Information processing speed in multiple sclerosis: Past, present, and future , 2017, Multiple sclerosis.

[20]  Daniel Y. Kimberg,et al.  Neural correlates of cognitive efficiency , 2006, NeuroImage.

[21]  Richard B. Buxton,et al.  Test–retest stability of calibrated BOLD-fMRI in HIV− and HIV+ subjects , 2011, NeuroImage.

[22]  M. Just,et al.  From the SelectedWorks of Marcel Adam Just 1992 A capacity theory of comprehension : Individual differences in working memory , 2017 .

[23]  R. Rudick,et al.  Axonal transection in the lesions of multiple sclerosis. , 1998, The New England journal of medicine.

[24]  R. Benedict,et al.  Lower total cerebral arterial flow contributes to cognitive performance in multiple sclerosis patients , 2019, Multiple sclerosis.

[25]  Eric A Newman,et al.  Glial Cells Dilate and Constrict Blood Vessels: A Mechanism of Neurovascular Coupling , 2006, The Journal of Neuroscience.

[26]  J. J. Chen,et al.  BOLD‐specific cerebral blood volume and blood flow changes during neuronal activation in humans , 2009, NMR in biomedicine.

[27]  T. Salthouse.  The processing-speed theory of adult age differences in cognition. , 1996, Psychological review.

[28]  Dinesh K. Sivakolundu,et al.  A neural-vascular complex of age-related changes in the human brain: Anatomy, physiology, and implications for neurocognitive aging , 2019, Neuroscience & Biobehavioral Reviews.

[29]  P. Baltes,et al.  Sensory functioning and intelligence in old age: a strong connection. , 1994, Psychology and aging.

[30]  Dinesh K. Sivakolundu,et al.  Altered task-induced cerebral blood flow and oxygen metabolism underlies motor impairment in multiple sclerosis , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[31]  S. Lynch,et al.  Difficulties in Planning Among Patients with Multiple Sclerosis: A Relative Consequence of Deficits in Information Processing Speed , 2013, Journal of the International Neuropsychological Society.

[32]  P. Jukkola,et al.  Astrocytes differentially respond to inflammatory autoimmune insults and imbalances of neural activity , 2013, Acta neuropathologica communications.

[33]  Monroe P. Turner,et al.  The efficiency of fMRI region of interest analysis methods for detecting group differences , 2014, Journal of Neuroscience Methods.

[34]  P. Vernon.  Speed of Information Processing and General Intelligence. , 1983 .

[35]  Dinesh K. Sivakolundu,et al.  BOLD hemodynamic response function changes significantly with healthy aging , 2019, NeuroImage.

[36]  J. Towse,et al.  Individual differences in working memory , 2006, Neuroscience.

[37]  R. Kail,et al.  The impact of extended practice on rate of mental rotation. , 1986, Journal of experimental child psychology.

[38]  Richard B. Buxton,et al.  Reproducibility of BOLD, perfusion, and CMRO2 measurements with calibrated-BOLD fMRI , 2007, NeuroImage.

[39]  Peiying Liu,et al.  MRI Mapping of Cerebrovascular Reactivity via Gas Inhalation Challenges , 2014, Journal of visualized experiments : JoVE.

[40]  Frank G. Hillary,et al.  Neural correlates of cognitive fatigue in multiple sclerosis using functional MRI , 2008, Journal of the Neurological Sciences.

[41]  Stephen D. Mayhew,et al.  Evidence that the negative BOLD response is neuronal in origin: A simultaneous EEG–BOLD–CBF study in humans , 2014, NeuroImage.

[42]  P. Goldman-Rakic.  Cellular basis of working memory , 1995, Neuron.

[43]  M D'Esposito,et al.  The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[44]  M. Raichle,et al.  The Effects of Changes in PaCO2 Cerebral Blood Volume, Blood Flow, and Vascular Mean Transit Time , 1974, Stroke.

[45]  G. Bruce Pike,et al.  Hemodynamic and metabolic responses to neuronal inhibition , 2004, NeuroImage.

[46]  Ji-Kyung Choi,et al.  Brain hemodynamic changes mediated by dopamine receptors: Role of the cerebral microvasculature in dopamine-mediated neurovascular coupling , 2006, NeuroImage.

[47]  Peter K Stys,et al.  Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis , 2009, The Lancet Neurology.

[48]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[49]  Peiying Liu,et al.  Impaired cerebrovascular reactivity in multiple sclerosis. , 2014, JAMA neurology.

[50]  G. Crelier,et al.  Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: The deoxyhemoglobin dilution model , 1999, Magnetic resonance in medicine.

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

[52]  M. Cambron,et al.  Vascular aspects of multiple sclerosis , 2011, The Lancet Neurology.

[53]  J Cerella,et al.  Generalized slowing in Brinley plots. , 1994, Journal of gerontology.

[54]  Richard G. Wise,et al.  Neurovascular Coupling During Visual Stimulation in Multiple Sclerosis: A MEG-fMRI Study , 2019, Neuroscience.

[55]  E Rostrup,et al.  Functional MRI of the visual cortex and visual testing in patients with previous optic neuritis , 2002, European journal of neurology.

[56]  Erin L. Mazerolle,et al.  Metabolic and vascular origins of the BOLD effect: Implications for imaging pathology and resting‐state brain function , 2015, Journal of magnetic resonance imaging : JMRI.

[57]  C. Iadecola,et al.  Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. , 2006, Journal of applied physiology.

[58]  Monroe P. Turner,et al.  Multiple sclerosis-related white matter microstructural change alters the BOLD hemodynamic response , 2016, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[59]  G. Kollias,et al.  Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling , 2015, Cell.

[60]  Stephen M. Rao,et al.  Neuropsychology of multiple sclerosis. , 1995, Current opinion in neurology.

[61]  Stephen M. Rao,et al.  Functional magnetic resonance imaging response to increased verbal working memory demands among patients with multiple sclerosis , 2006, Human brain mapping.

[62]  R. Kail.  Development of mental rotation: A speed-accuracy study , 1985 .

[63]  J. DeLuca,et al.  Treatment of cognitive impairment in multiple sclerosis: position paper , 2013, Journal of Neurology.

[64]  Feng Xu,et al.  Estimation of labeling efficiency in pseudocontinuous arterial spin labeling , 2010, Magnetic resonance in medicine.

[65]  Bart Rypma,et al.  Neural and vascular variability and the fMRI-BOLD response in normal aging. , 2010, Magnetic resonance imaging.

[66]  Bart Rypma,et al.  A BOLD Perspective on Age-Related Neurometabolic-Flow Coupling and Neural Efficiency Changes in Human Visual Cortex , 2013, Front. Psychol..

[67]  C. Iadecola.  The Neurovascular Unit Coming of Age: A Journey through Neurovascular Coupling in Health and Disease , 2017, Neuron.

[68]  T. Salthouse,et al.  Interrelations of age, health, and speed. , 1995, The journals of gerontology. Series B, Psychological sciences and social sciences.

[69]  Bart Rypma,et al.  Age-dependent relationships between prefrontal cortex activation and processing efficiency , 2011, Cognitive neuroscience.

[70]  Caterina Mainero,et al.  fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis , 2004, NeuroImage.

[71]  D. Attwell,et al.  Glial and neuronal control of brain blood flow , 2022 .

[72]  T. L. Davis,et al.  Calibrated functional MRI: mapping the dynamics of oxidative metabolism. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[73]  Ian Law,et al.  Negative BOLD signal changes in ipsilateral primary somatosensory cortex are associated with perfusion decreases and behavioral evidence for functional inhibition , 2012, NeuroImage.

[74]  T. Salthouse,et al.  Influence of processing speed on adult age differences in working memory. , 1992, Acta psychologica.

[75]  D. Hadley.  Brain energetics and neuronal activity: applications to fMRI and medicine , 2005 .

[76]  F. Mohamed,et al.  Quantitative functional MR imaging of the visual cortex at 1.5 T as a function of luminance contrast in healthy volunteers and patients with multiple sclerosis. , 2002, AJNR. American journal of neuroradiology.

[77]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[78]  U. Lindenberger,et al.  Coupled Cognitive Changes in Adulthood: A Meta-Analysis , 2019, Psychological bulletin.

[79]  P. Ackerman,et al.  Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. , 2002, Journal of experimental psychology. General.

[80]  J. Birren,et al.  Aging and speed of behavior: possible consequences for psychological functioning. , 1995, Annual review of psychology.

[81]  P. Baltes,et al.  Sensory functioning and intelligence in old age: A strong connection , 1994 .

[82]  S. Lord,et al.  Strength in the lower limbs, visual contrast sensitivity, and simple reaction time predict cognition in older women. , 1997, Psychology and aging.

[83]  Dinesh K. Sivakolundu,et al.  Three‐Dimensional Lesion Phenotyping and Physiologic Characterization Inform Remyelination Ability in Multiple Sclerosis , 2019, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[84]  Phillipp Meister,et al.  Statistical Signal Processing Detection Estimation And Time Series Analysis , 2016 .

[85]  S. Golaszewski,et al.  Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task. , 2002, Brain : a journal of neurology.

[86]  J. Myerson,et al.  Converging evidence for domain-specific slowing from multiple nonlexical tasks and multiple analytic methods. , 1995, The journals of gerontology. Series B, Psychological sciences and social sciences.

[87]  C. Howarth,et al.  The contribution of astrocytes to the regulation of cerebral blood flow , 2014, Front. Neurosci..

[88]  A. Boccellari,et al.  Treatment of cognitive impairment. , 1996, Focus.

[89]  B. A. Conway,et al.  The effects of laforin, malin, Stbd1, and Ptg deficiencies on heart glycogen levels in Pompe disease mouse models , 2015 .

[90]  Elizabeth Fisher,et al.  Multiple sclerosis normal‐appearing white matter: Pathology–imaging correlations , 2011, Annals of neurology.

[91]  Bharat B. Biswal,et al.  Task-Dependent Individual Differences in Prefrontal Connectivity , 2010, Cerebral cortex.

[92]  Bart Rypma,et al.  When less is more and when more is more: The mediating roles of capacity and speed in brain-behavior efficiency. , 2009, Intelligence.

[93]  Daniel G Bobrow,et al.  On data-limited and resource-limited processes , 1975, Cognitive Psychology.

[94]  Hirofumi Ochi,et al.  [Cognitive impairment in multiple sclerosis]. , 2014, Brain and nerve = Shinkei kenkyu no shinpo.

[95]  T. Salthouse.  Aging and measures of processing speed , 2000, Biological Psychology.

[96]  H. Štěpánková,et al.  Toward the processing speed theory of activities of daily living in healthy aging: normative data of the Functional Activities Questionnaire , 2016, Aging Clinical and Experimental Research.

[97]  J. DeLuca,et al.  Processing speed interacts with working memory efficiency in multiple sclerosis. , 2006, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[98]  Bart Rypma,et al.  Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging , 2009, Journal of the International Neuropsychological Society.