Preserved canonicality of the BOLD hemodynamic response reflects healthy cognition: Insights into the healthy brain through the window of Multiple Sclerosis

ABSTRACT The hemodynamic response function (HRF), a model of brain blood‐flow changes in response to neural activity, reflects communication between neurons and the vasculature that supplies these neurons in part by means of glial cell intermediaries (e.g., astrocytes). Intact neural‐vascular communication might play a central role in optimal cognitive performance. This hypothesis can be tested by comparing healthy individuals to those with known white‐matter damage and impaired performance, as seen in Multiple Sclerosis (MS). Glial cell intermediaries facilitate the ability of neurons to adequately convey metabolic needs to cerebral vasculature for sufficient oxygen and nutrient perfusion. In this study, we isolated measurements of the HRF that could quantify the extent to which white‐matter affects neural‐vascular coupling and cognitive performance. HRFs were modeled from multiple brain regions during multiple cognitive tasks using piecewise cubic spline functions, an approach that minimized assumptions regarding HRF shape that may not be valid for diseased populations, and were characterized using two shape metrics (peak amplitude and time‐to‐peak). Peak amplitude was reduced, and time‐to‐peak was longer, in MS patients relative to healthy controls. Faster time‐to‐peak was predicted by faster reaction time, suggesting an important role for vasodilatory speed in the physiology underlying processing speed. These results support the hypothesis that intact neural‐glial‐vascular communication underlies optimal neural and cognitive functioning. HighlightsIntact neural‐vascular communication may play a central role in cognitive performance.Patients with Multiple Sclerosis (MS) are known to have white‐matter damage and impaired cognitive performance.Hemodynamic response function (HRF) shapes of healthy individuals were compared to those of MS patients.Spline interpolation (minimizing shape assumptions) revealed group differences in both HRF amplitude and time‐to‐peak (TTP).Faster performance predicted faster HRF TTP, implicating vasodilatory speed in the physiology underlying cognitive speed.

[1]  R. Buxton,et al.  Modeling the hemodynamic response to brain activation , 2004, NeuroImage.

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

[3]  Guy C. Brown,et al.  Nitric oxide and neuronal death. , 2010, Nitric oxide : biology and chemistry.

[4]  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.

[5]  O. Kantarci,et al.  Gray Matter Atrophy Is Related to Long-Term Disability in Multiple Sclerosis , 2009 .

[6]  B. Cauli,et al.  Revisiting the Role of Neurons in Neurovascular Coupling , 2010, Front. Neuroenerg..

[7]  S. Kondo,et al.  Clinical significance of reduced cerebral metabolism in multiple sclerosis: A combined PET and MRI study , 1998, Annals of nuclear medicine.

[8]  P M Matthews,et al.  The motor cortex shows adaptive functional changes to brain injury from multiple sclerosis , 2000, Annals of neurology.

[9]  A. Dale,et al.  Coupling of the cortical hemodynamic response to cortical and thalamic neuronal activity. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Todd B. Parrish,et al.  Hemodynamic response function in patients with stroke-induced aphasia: Implications for fMRI data analysis , 2007, NeuroImage.

[11]  Guy C. Brown,et al.  Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria , 2003, Molecular Neurobiology.

[12]  G. Carmignoto,et al.  Astrocyte control of synaptic transmission and neurovascular coupling. , 2006, Physiological reviews.

[13]  P M Matthews,et al.  Relating functional changes during hand movement to clinical parameters in patients with multiple sclerosis in a multi‐centre fMRI study , 2008, European journal of neurology.

[14]  B. Trapp,et al.  Multiple sclerosis: an immune or neurodegenerative disorder? , 2008, Annual review of neuroscience.

[15]  J. H. Howard,et al.  Age‐related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging , 2009, Human brain mapping.

[16]  Lars Kai Hansen,et al.  Modeling the hemodynamic response in fMRI using smooth FIR filters , 2000, IEEE Transactions on Medical Imaging.

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

[18]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[19]  D. Pelletier,et al.  Whole-brain atrophy: ready for implementation into clinical decision-making in multiple sclerosis? , 2016, Current opinion in neurology.

[20]  N. Wilczak,et al.  Astrocytes in multiple sclerosis lack beta-2 adrenergic receptors , 1999, Neurology.

[21]  J. Morris,et al.  Functional Brain Imaging of Young, Nondemented, and Demented Older Adults , 2000, Journal of Cognitive Neuroscience.

[22]  V. Calhoun,et al.  Aberrant localization of synchronous hemodynamic activity in auditory cortex reliably characterizes schizophrenia , 2004, Biological Psychiatry.

[23]  Glyn Johnson,et al.  Dynamic susceptibility contrast perfusion MR imaging of multiple sclerosis lesions: characterizing hemodynamic impairment and inflammatory activity. , 2005, AJNR. American journal of neuroradiology.

[24]  G. Johnson,et al.  White matter hemodynamic abnormalities precede sub-cortical gray matter changes in multiple sclerosis , 2009, Journal of the Neurological Sciences.

[25]  Monroe P. Turner,et al.  Cognitive Slowing in Gulf War Illness Predicts Executive Network Hyperconnectivity: Study in a Population-Representative Sample , 2016, NeuroImage: Clinical.

[26]  J. Gabrieli,et al.  Myelination and organization of the frontal white matter in children: a diffusion tensor MRI study. , 1999, Neuroreport.

[27]  Richard B. Buxton,et al.  A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: Modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal , 2011, NeuroImage.

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

[29]  M. Lindquist,et al.  Validity and power in hemodynamic response modeling: A comparison study and a new approach , 2007, Human brain mapping.

[30]  T. Marrie,et al.  Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. , 1994, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

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

[32]  Drew Seils,et al.  Optimal design , 2007 .

[33]  Chiara Romualdi,et al.  Cortical atrophy is relevant in multiple sclerosis at clinical onset , 2007, Journal of Neurology.

[34]  J. E. Rinholm,et al.  Neuroscience: The wrap that feeds neurons , 2012, Nature.

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

[36]  D. Salat,et al.  Prefrontal gray and white matter volumes in healthy aging and Alzheimer disease. , 1999, Archives of neurology.

[37]  B L Miller,et al.  Factor analysis of four measures of prefrontal lobe functioning. , 1998, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[38]  Glyn Johnson,et al.  Pattern of hemodynamic impairment in multiple sclerosis: Dynamic susceptibility contrast perfusion MR imaging at 3.0 T , 2006, NeuroImage.

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

[40]  D. Glahn,et al.  Deconstructing processing speed deficits in schizophrenia: Application of a parametric digit symbol coding test , 2010, Schizophrenia Research.

[41]  Peter C M van Zijl,et al.  Experimental measurement of extravascular parenchymal BOLD effects and tissue oxygen extraction fractions using multi‐echo VASO fMRI at 1.5 and 3.0 T , 2005, Magnetic resonance in medicine.

[42]  Stephen M. Rao,et al.  Cognitive impairment in multiple sclerosis: An 18 year follow-up study. , 2014, Multiple sclerosis and related disorders.

[43]  Jos B. T. M. Roerdink,et al.  Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution , 2008, BMC Medical Imaging.

[44]  M H Al-Omari,et al.  Internal jugular vein morphology and hemodynamics in patients with multiple sclerosis. , 2010, International angiology : a journal of the International Union of Angiology.

[45]  O. Abe,et al.  Cognitive status correlates with white matter alteration in Parkinson's disease , 2012, Human brain mapping.

[46]  Martin A. Lindquist,et al.  Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling , 2009, NeuroImage.

[47]  G. Kraft,et al.  Self-administered Expanded Disability Status Scale with functional system scores correlates well with a physician-administered test , 2001, Multiple sclerosis.

[48]  I Rovira,et al.  Nitric oxide , 2021, Reactions Weekly.

[49]  G. Glover Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[50]  Peiying Liu,et al.  Effect of Hypoxia and Hyperoxia on Cerebral Blood Flow, Blood Oxygenation, and Oxidative Metabolism , 2012, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[51]  A. Fleisher,et al.  Effects of aging on cerebral blood flow, oxygen metabolism, and blood oxygenation level dependent responses to visual stimulation , 2009, Human brain mapping.

[52]  Rohit Bakshi,et al.  Gray matter involvement in multiple sclerosis , 2007, Neurology.

[53]  D. Rossi,et al.  Another BOLD role for astrocytes: coupling blood flow to neural activity , 2006, Nature Neuroscience.

[54]  Xianhong Xie,et al.  Optimal spline smoothing of fMRI time series by generalized cross-validation , 2003, NeuroImage.

[55]  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.

[56]  T. Klingberg,et al.  Maturation of White Matter is Associated with the Development of Cognitive Functions during Childhood , 2004, Journal of Cognitive Neuroscience.

[57]  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.

[58]  Brian N. Pasley,et al.  Analysis of oxygen metabolism implies a neural origin for the negative BOLD response in human visual cortex , 2007, NeuroImage.

[59]  V. Borutaite,et al.  Serial Review: Nitric Oxide in Mitochondria Guest Editors: Christoph Richter and Matthias Schweizer NITRIC OXIDE INHIBITION OF MITOCHONDRIAL RESPIRATION AND ITS ROLE IN CELL DEATH , 2002 .

[60]  Vanessa Sluming,et al.  Calibrated fMRI during a cognitive Stroop task reveals reduced metabolic response with increasing age , 2012, NeuroImage.

[61]  M. Filippi,et al.  Adaptive functional changes in the cerebral cortex of patients with nondisabling multiple sclerosis correlate with the extent of brain structural damage , 2002, Annals of neurology.

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

[63]  Emily Snook,et al.  Cognitive impairments in relapsing-remitting multiple sclerosis: a meta-analysis , 2008, Multiple sclerosis.

[64]  Mark D'Esposito,et al.  Age-related changes in brain–behaviour relationships: Evidence from event-related functional MRI studies , 2001 .

[65]  Giorgio Carmignoto,et al.  The contribution of astrocyte signalling to neurovascular coupling , 2010, Brain Research Reviews.

[66]  R. Goebel,et al.  Optimal design for nonlinear estimation of the hemodynamic response function , 2012, Human brain mapping.

[67]  K. Light,et al.  Brain activation in multiple sclerosis: a BOLD fMRI study of the effects of fatiguing hand exercise , 2009, Multiple sclerosis.

[68]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[69]  Arne D. Ekstrom,et al.  Fornix damage limits verbal memory functional compensation in multiple sclerosis , 2012, NeuroImage.

[70]  A. Prat,et al.  Disruption of central nervous system barriers in multiple sclerosis. , 2011, Biochimica et biophysica acta.

[71]  N. Logothetis The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[72]  D. Bourdette,et al.  Axonal degeneration in multiple sclerosis: The mitochondrial hypothesis , 2009, Current neurology and neuroscience reports.

[73]  B. Douglas Ward,et al.  Deconvolution Analysis of FMRI Time Series Data , 2006 .

[74]  T. Takano,et al.  Astrocyte-mediated control of cerebral blood flow , 2006, Nature Neuroscience.

[75]  Massimo Filippi,et al.  Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. , 2009, Archives of neurology.

[76]  Monroe P. Turner,et al.  Asynchrony in executive networks predicts cognitive slowing in multiple sclerosis. , 2016, Neuropsychology.

[77]  R. Rudick,et al.  Gray matter atrophy in multiple sclerosis: A longitudinal study , 2008, Annals of neurology.

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

[79]  M R Symms,et al.  Abnormalities of cerebral perfusion in multiple sclerosis , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

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

[81]  R. Ogg,et al.  Hemodynamic responses to visual stimulation in children with sickle cell anemia , 2011, Brain Imaging and Behavior.

[82]  Michael Noseworthy,et al.  White matter growth as a mechanism of cognitive development in children , 2006, NeuroImage.

[83]  C. Brosnan,et al.  The astrocyte in multiple sclerosis revisited , 2013, Glia.

[84]  V. Menon,et al.  White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. , 2005, Cerebral cortex.

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

[86]  P. Bandettini,et al.  Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response , 2001, NeuroImage.

[87]  L. Jacobsson,et al.  Regional Cerebral Blood Flow in Multiple Sclerosis Measured by Single Photon Emission Tomography with Technetium-99m Hexamethyl-propyleneamine Oxime , 1993 .

[88]  M. Nedergaard,et al.  White matter astrocytes in health and disease , 2014, Neuroscience.

[89]  J Marshall,et al.  Studies on regional cerebral oxygen utilisation and cognitive function in multiple sclerosis. , 1984, Journal of neurology, neurosurgery, and psychiatry.

[90]  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.

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

[92]  Venkatesh N. Murthy,et al.  Role of Astrocytes in Neurovascular Coupling , 2011, Neuron.

[93]  M. D’Esposito,et al.  The Effect of Normal Aging on the Coupling of Neural Activity to the Bold Hemodynamic Response , 1999, NeuroImage.

[94]  Ying Zheng,et al.  Long Duration Stimuli and Nonlinearities in the Neural–Haemodynamic Coupling , 2005, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[95]  G. McCarthy,et al.  The Effects of Aging upon the Hemodynamic Response Measured by Functional MRI , 2001, NeuroImage.

[96]  Faith M. Gunning-Dixon,et al.  Aging of cerebral white matter: a review of MRI findings , 2009, International journal of geriatric psychiatry.

[97]  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.

[98]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[99]  Matteo Pardini,et al.  Structural connectivity influences brain activation during PVSAT in Multiple Sclerosis , 2009, NeuroImage.

[100]  Monroe P. Turner,et al.  Calibrated imaging reveals altered grey matter metabolism related to white matter microstructure and symptom severity in multiple sclerosis , 2017, Human brain mapping.

[101]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[102]  Thomas T. Liu,et al.  Cerebral blood flow and BOLD responses to a memory encoding task: A comparison between healthy young and elderly adults , 2007, NeuroImage.

[103]  G. Tack,et al.  Effect of 30% Oxygen Administration on Verbal Cognitive Performance, Blood Oxygen Saturation and Heart Rate , 2006, Applied psychophysiology and biofeedback.

[104]  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.

[105]  Ravi S. Menon,et al.  Reduced visual evoked responses in multiple sclerosis patients with optic neuritis: Comparison of functional magnetic resonance imaging and visual evoked potentials , 1999, Multiple sclerosis.

[106]  R. Turner,et al.  Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations , 2002, NeuroImage.

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

[108]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[109]  F. Hyder,et al.  Quantitative functional imaging of the brain: towards mapping neuronal activity by BOLD fMRI , 2001, NMR in biomedicine.

[110]  Pierre J. Magistretti,et al.  Oligodendroglia metabolically support axons and contribute to neurodegeneration , 2012, Nature.

[111]  D. Auer,et al.  Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. , 2009, Brain : a journal of neurology.

[112]  Charlotte Graham,et al.  Reduced grey matter perfusion without volume loss in early relapsing-remitting multiple sclerosis , 2013, Journal of Neurology, Neurosurgery & Psychiatry.

[113]  J. DeLuca,et al.  The relative contributions of processing speed and cognitive load to working memory accuracy in multiple sclerosis , 2011, Journal of clinical and experimental neuropsychology.

[114]  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.

[115]  J. Filosa,et al.  Tone-dependent vascular responses to astrocyte-derived signals. , 2008, American journal of physiology. Heart and circulatory physiology.

[116]  Guy B. Williams,et al.  Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease. , 2010, Brain : a journal of neurology.

[117]  Massimiliano Calabrese,et al.  Measurement and clinical effect of grey matter pathology in multiple sclerosis , 2012, The Lancet Neurology.

[118]  C. Carter,et al.  The BOLD Hemodynamic Response in Healthy Aging , 2004, Journal of Cognitive Neuroscience.

[119]  P N Leigh,et al.  Cognitive change in ALS , 2005, Neurology.

[120]  J. De Keyser,et al.  Hypoperfusion of the Cerebral White Matter in Multiple Sclerosis: Possible Mechanisms and Pathophysiological Significance , 2008, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[121]  Aristide Merola,et al.  Demyelination, Inflammation, and Neurodegeneration in Multiple Sclerosis Deep Gray Matter , 2009, Journal of neuropathology and experimental neurology.

[122]  G. Bartzokis,et al.  White matter structural integrity in healthy aging adults and patients with Alzheimer disease: a magnetic resonance imaging study. , 2003, Archives of neurology.

[123]  B. Rypma,et al.  Age-Related Differences in Activation-Performance Relations in Delayed-Response Tasks: a Multiple Component Analysis , 2007, Cortex.

[124]  Brian B. Avants,et al.  High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis , 2007, IEEE Transactions on Medical Imaging.

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

[126]  G. Johnson,et al.  Microvascular Abnormality in Relapsing-remitting Multiple Sclerosis: Perfusion Mr Imaging Findings in Normal-appearing White Matter , 2003 .

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

[128]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

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

[130]  J. Fisk,et al.  Information Processing Efficiency in Patients with Multiple Sclerosis , 2000, Journal of clinical and experimental neuropsychology.

[131]  M. Filippi,et al.  White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging , 2002, Journal of neurology, neurosurgery, and psychiatry.

[132]  E. Hillman Coupling mechanism and significance of the BOLD signal: a status report. , 2014, Annual review of neuroscience.

[133]  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.

[134]  D. Gronwall Paced Auditory Serial-Addition Task: A Measure of Recovery from Concussion , 1977, Perceptual and motor skills.

[135]  R. Swank,et al.  Cerebral blood flow and red cell delivery in normal subjects and in multiple sclerosis. , 1983, Neurological research.

[136]  J. Desmond,et al.  Load-Dependent Roles of Frontal Brain Regions in the Maintenance of Working Memory , 1999, NeuroImage.

[137]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[138]  A. Dale,et al.  Selective averaging of rapidly presented individual trials using fMRI , 1997, Human brain mapping.

[139]  J. Helpern,et al.  Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging , 2006, NMR in biomedicine.

[140]  Hans Lassmann,et al.  Hypoxia-like tissue injury as a component of multiple sclerosis lesions , 2002, Journal of the Neurological Sciences.

[141]  L. Garey Brodmann's localisation in the cerebral cortex , 1999 .

[142]  C. Iadecola,et al.  Neurovascular coupling in health and disease: lessons from transgenic mice , 2002 .

[143]  H. Lassmann Mechanisms of white matter damage in multiple sclerosis , 2014, Glia.

[144]  A. Minagar Cognitive compensation failure in multiple sclerosis , 2011 .

[145]  K. Scheffler,et al.  Dynamic susceptibility contrast MR imaging of plaque development in multiple sclerosis: Application of an extended blood‐brain barrier leakage correction , 2000, Journal of magnetic resonance imaging : JMRI.

[146]  B. MacVicar,et al.  Calcium transients in astrocyte endfeet cause cerebrovascular constrictions , 2004, Nature.

[147]  James B. Rowe,et al.  The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults , 2015, Human brain mapping.

[148]  Stanley L. Sclove,et al.  Estimation and classification of fMRI hemodynamic response patterns , 2004, NeuroImage.

[149]  Guy C. Brown Nitric oxide and mitochondria. , 2007, Frontiers in bioscience : a journal and virtual library.

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

[151]  Gian Domenico Iannetti,et al.  A longitudinal fMRI study on motor activity in patients with multiple sclerosis. , 2005, Brain : a journal of neurology.

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

[153]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[154]  Roger A. Barker,et al.  White matter pathology in Parkinson's disease: The effect of imaging protocol differences and relevance to executive function , 2012, NeuroImage.

[155]  John E. W. Mayhew,et al.  Investigating neural–hemodynamic coupling and the hemodynamic response function in the awake rat , 2006, NeuroImage.

[156]  C. Figley,et al.  Spatial Correlation of Pathology and Perfusion Changes within the Cortex and White Matter in Multiple Sclerosis , 2018, American Journal of Neuroradiology.

[157]  Frederik Barkhof,et al.  Grey matter pathology in multiple sclerosis , 2008, The Lancet Neurology.