Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer’s disease

BackgroundThe incidence of Alzheimer’s disease (AD) strongly relates to advanced age and progressive deposition of cerebral amyloid-beta (Aβ), hyperphosphorylated tau, and iron. The purpose of this study was to investigate the relationship between cerebral dynamic functional connectivity and variability of long-term cognitive performance in healthy, elderly subjects, allowing for local pathology and genetic risk.MethodsThirty seven participants (mean (SD) age 74 (6.0) years, Mini-Mental State Examination 29.0 (1.2)) were dichotomized based on repeated neuropsychological test performance within 2 years. Cerebral Aβ was measured by 11C Pittsburgh Compound-B positron emission tomography, and iron by quantitative susceptibility mapping magnetic resonance imaging (MRI) at an ultra-high field strength of 7 Tesla (7T). Dynamic functional connectivity patterns were investigated by resting-state functional MRI at 7T and tested for interactive effects with genetic AD risk (apolipoprotein E (ApoE)-ε4 carrier status).ResultsA relationship between low episodic memory and a lower expression of anterior-posterior connectivity was seen (F(9,27) = 3.23, p < 0.008), moderated by ApoE-ε4 (F(9,27) = 2.22, p < 0.005). Inherent node-strength was related to local iron (F(5,30) = 13.2; p < 0.022).ConclusionOur data indicate that altered dynamic anterior-posterior brain connectivity is a characteristic of low memory performance in the subclinical range and genetic risk for AD in the elderly. As the observed altered brain network properties are associated with increased local iron, our findings may reflect secondary neuronal changes due to pathologic processes including oxidative stress.

[1]  B. Gandevia,et al.  DECLARATION OF HELSINKI. , 1964, The Medical journal of Australia.

[2]  John R. Williams.  DECLARATION OF HELSINKI , 1964 .

[3]  P. Schönemann,et al.  On two-sided orthogonal procrustes problems , 1968, Psychometrika.

[4]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[5]  References , 1971 .

[6]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[7]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[8]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[9]  R. Brookshire,et al.  The Boston Naming Test: Revised Administration and Scoring Procedures and Normative Information for Non-Brain-Damaged Adults , 1989 .

[10]  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.

[11]  H F Durwen,et al.  [The Verbal Learning and Retention Test. A useful and differentiated tool in evaluating verbal memory performance]. , 1990, Schweizer Archiv fur Neurologie und Psychiatrie.

[12]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[13]  J. Haines,et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. , 1993, Science.

[14]  Jim Mintz,et al.  In vivo evaluation of brain iron in Alzheimer's disease and normal subjects using MRI , 1994, Biological Psychiatry.

[15]  Karl J. Friston,et al.  Tests for Distributed, Nonfocal Brain Activations , 1995, NeuroImage.

[16]  Cognitive predictors of incident Alzheimer's disease: a prospective longitudinal study. , 1997, Neuropsychology.

[17]  D Commenges,et al.  Cognitive process in preclinical phase of dementia. , 1998, Brain : a journal of neurology.

[18]  Roberta F. White,et al.  The preclinical phase of alzheimer disease: A 22-year prospective study of the Framingham Cohort. , 2000, Archives of neurology.

[19]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[20]  John D. Storey.  A direct approach to false discovery rates , 2002 .

[21]  W. Klunk,et al.  Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. , 2003, Journal of medicinal chemistry.

[22]  W. Klunk,et al.  Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound‐B , 2004, Annals of neurology.

[23]  Khadija Iqbal,et al.  Ferritin is a component of the neuritic (senile) plaque in Alzheimer dementia , 2004, Acta Neuropathologica.

[24]  T. Tombaugh.  Trail Making Test A and B: normative data stratified by age and education. , 2004, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[25]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[26]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[27]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[28]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[29]  Gina N. LaRossa,et al.  [11C]PIB in a nondemented population , 2006, Neurology.

[30]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[31]  C. Jack,et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. , 2008, Brain : a journal of neurology.

[32]  Keith A. Johnson,et al.  Amyloid Deposition Is Associated with Impaired Default Network Function in Older Persons without Dementia , 2009, Neuron.

[33]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[34]  M. Weiner,et al.  Relationships between biomarkers in aging and dementia , 2009, Neurology.

[35]  B. Miller,et al.  Neurodegenerative Diseases Target Large-Scale Human Brain Networks , 2009, Neuron.

[36]  M. Muckenthaler,et al.  Iron toxicity in diseases of aging: Alzheimer's disease, Parkinson's disease and atherosclerosis. , 2009, Journal of Alzheimer's disease : JAD.

[37]  K. Warner Schaie,et al.  “When does age-related cognitive decline begin?” Salthouse again reifies the “cross-sectional fallacy” , 2009, Neurobiology of Aging.

[38]  Qing X Yang,et al.  MRI and histological analysis of beta‐amyloid plaques in both human Alzheimer's disease and APP/PS1 transgenic mice , 2009, Journal of magnetic resonance imaging : JMRI.

[39]  J. Simpkins,et al.  Amyloid-β peptide fibrils induce nitro-oxidative stress in neuronal cells. , 2010, Journal of Alzheimer's disease : JAD.

[40]  R. Tanzi,et al.  The Genetics of Alzheimer Disease: Back to the Future , 2010, Neuron.

[41]  M. Donohue,et al.  Enrichment through biomarkers in clinical trials of Alzheimer's drugs in patients with mild cognitive impairment , 2010, Neurobiology of Aging.

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

[43]  Kyle L. Morris,et al.  Iron Promotes the Toxicity of Amyloid β Peptide by Impeding Its Ordered Aggregation* , 2010, The Journal of Biological Chemistry.

[44]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[45]  D. Head,et al.  Amyloid Plaques Disrupt Resting State Default Mode Network Connectivity in Cognitively Normal Elderly , 2010, Biological Psychiatry.

[46]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[47]  M. Albert.  Changes in cognition , 2011, Neurobiology of Aging.

[48]  H. Arrighi,et al.  Prevalence of Apolipoprotein E4 Genotype and Homozygotes (APOE e4/4) among Patients Diagnosed with Alzheimer’s Disease: A Systematic Review and Meta-Analysis , 2011, Neuroepidemiology.

[49]  William J. Jagust,et al.  Lifespan brain activity, β-amyloid, and Alzheimer's disease , 2011, Trends in Cognitive Sciences.

[50]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[51]  C. Rowe,et al.  Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease , 2011, Annals of neurology.

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

[53]  B. Hyman,et al.  Neuropathological alterations in Alzheimer disease. , 2011, Cold Spring Harbor perspectives in medicine.

[54]  Peter R Luijten,et al.  Blood oxygenation level‐dependent (BOLD) total and extravascular signal changes and ΔR2* in human visual cortex at 1.5, 3.0 and 7.0 T , 2011, NMR in biomedicine.

[55]  D. Long.  Networks of the Brain , 2011 .

[56]  Nick C Fox,et al.  The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease , 2011 .

[57]  Dimitri Van De Ville,et al.  Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity , 2012, NeuroImage.

[58]  L. Nyberg,et al.  Memory aging and brain maintenance , 2012, Trends in Cognitive Sciences.

[59]  Xu Li,et al.  Mapping magnetic susceptibility anisotropies of white matter in vivo in the human brain at 7T , 2012, NeuroImage.

[60]  G. Sapiro,et al.  Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI , 2012, PloS one.

[61]  Tingting Yang,et al.  Interactions between Amyloid-β and Hemoglobin: Implications for Amyloid Plaque Formation in Alzheimer's Disease , 2012, PloS one.

[62]  M. Núñez,et al.  Iron toxicity in neurodegeneration , 2012, BioMetals.

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

[64]  Peter R Luijten,et al.  Spontaneous blood oxygenation level‐dependent fMRI signal is modulated by behavioral state and correlates with evoked response in sensorimotor cortex: A 7.0‐T fMRI study , 2012, Human brain mapping.

[65]  Ferdinand Schweser,et al.  Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study , 2012, NeuroImage.

[66]  A. Wimo,et al.  The global prevalence of dementia: A systematic review and metaanalysis , 2013, Alzheimer's & Dementia.

[67]  V. Pankratz,et al.  Practice Effects and Longitudinal Cognitive Change in Normal Aging vs. Incident Mild Cognitive Impairment and Dementia in The Mayo Clinic Study of Aging , 2013, The Clinical neuropsychologist.

[68]  Stefan Maderwald,et al.  Memory‐Related Hippocampal Activity Can Be Measured Robustly Using fMRI at 7 Tesla , 2013, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[69]  Martina Bocchetta,et al.  Imaging markers for Alzheimer disease , 2013, Neurology.

[70]  A. Bush.  The metal theory of Alzheimer's disease. , 2012, Journal of Alzheimer's disease : JAD.

[71]  Robert Turner,et al.  Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2 ⁎-imaging at ultra-high magnetic field strength , 2013, NeuroImage.

[72]  M. Kruer.  The neuropathology of neurodegeneration with brain iron accumulation. , 2013, International review of neurobiology.

[73]  K. Jellinger.  The relevance of metals in the pathophysiology of neurodegeneration, pathological considerations. , 2013, International review of neurobiology.

[74]  Dimitri Van De Ville,et al.  Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest , 2013, NeuroImage.

[75]  Huaxi Xu,et al.  Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy , 2013, Nature Reviews Neurology.

[76]  Y. Ke,et al.  Reducing iron in the brain: a novel pharmacologic mechanism of huperzine A in the treatment of Alzheimer's disease , 2014, Neurobiology of Aging.

[77]  Habib Benali,et al.  Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements , 2014, NeuroImage.

[78]  D. Leys,et al.  Iron deposits in post‐mortem brains of patients with neurodegenerative and cerebrovascular diseases: a semi‐quantitative 7.0 T magnetic resonance imaging study , 2014, European journal of neurology.

[79]  Keith A. Johnson,et al.  Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. , 2014, JAMA neurology.

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

[81]  Alain Buisson,et al.  Iron overload accelerates neuronal amyloid-β production and cognitive impairment in transgenic mice model of Alzheimer's disease , 2014, Neurobiology of Aging.

[82]  S. Leh,et al.  Cortical Amyloid Beta in Cognitively Normal Elderly Adults is Associated with Decreased Network Efficiency within the Cerebro-Cerebellar System , 2014, Front. Aging Neurosci..

[83]  S. Leh,et al.  Regional Fluid-Attenuated Inversion Recovery (FLAIR) at 7 Tesla correlates with amyloid beta in hippocampus and brainstem of cognitively normal elderly subjects , 2014, Front. Aging Neurosci..

[84]  Jun Hua,et al.  Whole‐brain three‐dimensional T2‐weighted BOLD functional magnetic resonance imaging at 7 Tesla , 2014, Magnetic resonance in medicine.

[85]  Karen M Rodrigue,et al.  Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. , 2015, JAMA.

[86]  S. Leh,et al.  Regional cerebral blood flow estimated by early PiB uptake is reduced in mild cognitive impairment and associated with age in an amyloid-dependent manner , 2015, Neurobiology of Aging.

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

[88]  Alzheimer’s Disease Neuroimaging Initiative,et al.  Ferritin levels in the cerebrospinal fluid predict Alzheimer's disease outcomes and are regulated by APOE , 2015 .

[89]  Keith A. Johnson,et al.  Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE , 2015, Nature Communications.

[90]  H. Wood.  Alzheimer disease: Iron—the missing link between ApoE and Alzheimer disease? , 2015, Nature Reviews Neurology.

[91]  Cindee M. Madison,et al.  Effects of Beta-Amyloid on Resting State Functional Connectivity Within and Between Networks Reflect Known Patterns of Regional Vulnerability. , 2014, Cerebral cortex.

[92]  C. Jack,et al.  Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria , 2016, Alzheimer's & Dementia.

[93]  A. F. Gietl,et al.  Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment , 2016, Scientific Reports.

[94]  P. V. van Zijl,et al.  Quantitative Susceptibility Mapping Suggests Altered Brain Iron in Premanifest Huntington Disease , 2016, American Journal of Neuroradiology.