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Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease

Open AccessPublished:April 03, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.03.019

      Abstract

      Background

      Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment.

      Methods

      We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level–dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function.

      Results

      Data were available for 979 participants (ages 45–74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (−) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN− = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN−. Time spent in DMN-related brain states was associated with executive function.

      Conclusions

      Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.

      Keywords

      The clinical manifestations of cerebral small vessel disease (cSVD) include stroke, depression, and cognitive impairment (
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      Characterization of white matter hyperintensities in large-scale MRI-studies.
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      Dynamic FC (dFC) studies have revealed spatiotemporal organizational principles that cannot be assessed using static FC methods (
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      Time-varying functional network information extracted from brief instances of spontaneous brain activity.
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      Interpreting temporal fluctuations in resting-state functional connectivity MRI.
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      Predicting individual brain maturity using dynamic functional connectivity.
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      ). Understanding of cognitive processes has further benefited from dFC approaches in terms of the detection of different behaviorally relevant timescales of brain dynamics (
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      Resting brain dynamics at different timescales capture distinct aspects of human behavior.
      ) and an appreciation of the discrete event structure underlying FC (
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      Individualized event structure drives individual differences in whole-brain functional connectivity.
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      High-amplitude cofluctuations in cortical activity drive functional connectivity.
      ).
      Spatial coactivation patterns in the presence of cSVD pathology and their relationship to cognitive impairment, however, are unknown. Using data from a population-based cohort study, we have investigated the association of cSVD pathology, spatiotemporal organization of brain activity measured by resting-state functional MRI, and cognitive performance. Based on results of reduced dFC speed associated with aging (
      • Battaglia D.
      • Boudou T.
      • Hansen E.C.A.
      • Lombardo D.
      • Chettouf S.
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      ) and cognitive impairment in multiple sclerosis (
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      Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis.
      ), we hypothesized that the extent of WM damage would be associated with a reduction in the complexity of spatiotemporal patterns. Moreover, we hypothesized that such changes would be associated with measures of subcortical cognitive impairment.

      Methods and Materials

      A high-level overview of the imaging and behavioral data, processing, and analysis steps is shown in Figure S1. MRI sequence parameters, imaging preprocessing steps, and details on the avoidance of false positive WMH segmentations are detailed in the Supplemental Methods.

      Study Population

      The Hamburg City Health Study (HCHS) is a prospective, population-based cohort study that aims to include a cross-sectional sample of 45,000 participants between 45 and 74 years (
      • Jagodzinski A.
      • Johansen C.
      • Koch-Gromus U.
      • Aarabi G.
      • Adam G.
      • Anders S.
      • et al.
      Rationale and design of the Hamburg City Health Study.
      ). Bias is minimized by random selection from official registries. We used all available data at the time of planning the analysis, when 4253 participants had been included in the HCHS, 1000 of which were documented as having received brain imaging. The first and last patients were examined in June 2016 and February 2017, respectively. HCHS was approved by the local ethics committee of the Landesärztekammer Hamburg (State of Hamburg Chamber of Medical Practitioners, PV5131), and all participants provided written informed consent.

      Clinical Characterization

      Arterial hypertension was operationalized as a previous diagnosis of hypertension, prescription of antihypertensive medication, or blood pressure exceeding 140 mm Hg (systolic) or 90 mm Hg (diastolic) during the study clinic visit. Diabetes mellitus was defined as the disjunction of previously diagnosed diabetes mellitus, prescription of antidiabetic medication, or a serum glucose level exceeding 126 mg/dL (fasting) or 200 mg/dL (nonfasting).
      Cognitive functions were assessed by neuropsychological tests during participants’ visits at the study center. The Mini-Mental State Examination was used to screen for global impairment of cognitive function (
      • Tombaugh T.N.
      • McIntyre N.J.
      The Mini-Mental State Examination: A comprehensive review.
      ). The Animal Naming and Word List Learning/Recall tests from the Consortium to Establish a Registry for Alzheimer Disease neuropsychology battery (
      • Fillenbaum G.G.
      • van Belle G.
      • Morris J.C.
      • Mohs R.C.
      • Mirra S.S.
      • Davis P.C.
      • et al.
      Consortium to Establish a Registry for Alzheimer’s Disease (CERAD): The first twenty years.
      ) were used to assess verbal fluency and working memory; the Trail Making Test (TMT) was used to quantify cognitive processing speed and executive function (
      • Tombaugh T.N.
      Trail Making Test A and B: Normative data stratified by age and education.
      ). The multiple choice vocabulary test (Mehrfach-Wortschatz test) was used to estimate semantic memory (
      • Lehrl S.
      • Triebig G.
      • Fischer B.
      Multiple choice vocabulary test MWT as a valid and short test to estimate premorbid intelligence.
      ). Here, participants selected an item from lists of five potential words containing four phonetically related neologisms. The score is the number of correctly identified items, with a maximum score of 37.

      WMH Segmentation

      WMHs were segmented in subject space using k-nearest neighbors classification as implemented in FSL’s Brain Intensity Abnormality Classification Algorithm (
      • Griffanti L.
      • Zamboni G.
      • Khan A.
      • Li L.
      • Bonifacio G.
      • Sundaresan V.
      • et al.
      BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
      ). FLAIR and T1w intensities and Montreal Neurological Institute coordinates were used as features. The classifier was trained on manually segmented WMHs from 98 randomly selected subjects (
      • Petersen M.
      • Frey B.M.
      • Schlemm E.
      • Mayer C.
      • Hanning U.
      • Engelke K.
      • et al.
      Network localisation of white matter damage in cerebral small vessel disease.
      ). For cross-modal registration, images were brain-extracted using HD-BET (
      • Isensee F.
      • Schell M.
      • Pflueger I.
      • Brugnara G.
      • Bonekamp D.
      • Neuberger U.
      • et al.
      Automated brain extraction of multisequence MRI using artificial neural networks.
      ) and linearly aligned using FLIRT with 6 degrees of freedom (
      • Jenkinson M.
      • Bannister P.
      • Brady M.
      • Smith S.
      Improved optimization for the robust and accurate linear registration and motion correction of brain images.
      ,
      • Jenkinson M.
      • Smith S.
      A global optimisation method for robust affine registration of brain images.
      ,
      • Greve D.N.
      • Fischl B.
      Accurate and robust brain image alignment using boundary-based registration.
      ).

      Postprocessing of Lesion Probability Maps

      Binary lesion maps were created from the BIANCA (Brain Intensity Abnormality Classification Algorithm) output using the recent LOCATE (Locally Adaptive Threshold Estimation) approach (
      • Sundaresan V.
      • Zamboni G.
      • Le Heron C.
      • Rothwell P.M.
      • Husain M.
      • Battaglini M.
      • et al.
      Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.
      ). LOCATE was trained on the same 98 subjects as BIANCA. After binarization, clusters containing fewer than 30 lesioned voxels were discarded. For supplemental analyses, lesioned voxels were classified as either periventricular (distance to the ventricles <10 mm) or deep (>10 mm) (
      • Griffanti L.
      • Jenkinson M.
      • Suri S.
      • Zsoldos E.
      • Mahmood A.
      • Filippini N.
      • et al.
      Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults.
      ,
      • DeCarli C.
      • Fletcher E.
      • Ramey V.
      • Harvey D.
      • Jagust W.J.
      Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden.
      ).

      Dynamic Connectivity Quantification

      Denoised voxel-level data were parcellated by averaging the blood oxygen level–dependent (BOLD) signal in 400 regions of the Schaefer atlas (
      • Schaefer A.
      • Kong R.
      • Gordon E.M.
      • Laumann T.O.
      • Zuo X.N.
      • Holmes A.J.
      • et al.
      Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI.
      ). This choice was motivated by prior work showing that parcellations of this scale replicate voxelwise clustering results more closely than coarser scales (
      • Chen J.E.
      • Chang C.
      • Greicius M.D.
      • Glover G.H.
      Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics.
      ).
      We chose to quantify dFC using a coactivation pattern approach (
      • Liu X.
      • Zhang N.
      • Chang C.
      • Duyn J.H.
      Co-activation patterns in resting-state fMRI signals.
      ,
      • Liu X.
      • Chang C.
      • Duyn J.H.
      Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns.
      ). This method was chosen because it provides the maximal temporal resolution of 1 repetition time (TR), is robust, and does not require the a priori selection of additional parameters, such as shapes and lengths in sliding window approaches (
      • Savva A.D.
      • Mitsis G.D.
      • Matsopoulos G.K.
      Assessment of dynamic functional connectivity in resting-state fMRI using the sliding window technique.
      ,
      • Mokhtari F.
      • Akhlaghi M.I.
      • Simpson S.L.
      • Wu G.
      • Laurienti P.J.
      Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.
      ,
      • Hindriks R.
      • Adhikari M.H.
      • Murayama Y.
      • Ganzetti M.
      • Mantini D.
      • Logothetis N.K.
      • Deco G.
      Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? [published correction appears in Neuroimage 2016; 132:115].
      ). To map synchronous brain activation to discrete brain space, we used unsupervised clustering as described previously (
      • Cornblath E.J.
      • Ashourvan A.
      • Kim J.Z.
      • Betzel R.F.
      • Ciric R.
      • Adebimpe A.
      • et al.
      Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands.
      ,
      • Gutierrez-Barragan D.
      • Basson M.A.
      • Panzeri S.
      • Gozzi A.
      Infraslow state fluctuations govern spontaneous fMRI network dynamics.
      ): after concatenating the parcellated BOLD data from all participants into a (nsubjects × ntime points) × 400 feature matrix, we performed k-means clustering in 400-dimensional brain activation space with 1 minus the sample Pearson correlation between points as distance measure d. We thus partitioned the set of nsubjects × ntime points BOLD observations into k clusters C1, C2, . . ., Ck, with centroids γ1, γ2, . . . γk, such that the within-cluster variance D was minimized, calculated as
      D=κ=1kpCκd(p,γκ)


      To this end, we started from some initial centroids and used the iterative expectation-maximization algorithm kmeans++ implemented in MATLAB R2021a (The MathWorks, Inc.) to assign, at each step, individual observations to their closest centroid and to update centroid locations to the mean of z-transformed observations thus assigned until convergence occurred. The number of clusters was varied between k = 2 and k = 12, and 20 repetitions were performed for each k with random initial conditions. The optimal number of clusters was determined by assessing the incremental variance explained by the lowest error solution at each value of k. Clusters were identified as brain states and named based on the cosine similarity of the positive and negative activations of their centroid with seven a priori–defined functional networks (
      • Yeo B.T.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ). To quantify dynamic aspects of spatial coactivation patterns, we estimated subject- and state-specific fractional occupancies and dwell times. The former is defined as the proportion of BOLD volumes assigned to each brain state, and the latter is the average number of contiguous volumes thus assigned (
      • Vidaurre D.
      • Abeysuriya R.
      • Becker R.
      • Quinn A.J.
      • Alfaro-Almagro F.
      • Smith S.M.
      • Woolrich M.W.
      Discovering dynamic brain networks from big data in rest and task.
      ,
      • Kabbara A.
      • Paban V.
      • Hassan M.
      The dynamic modular fingerprints of the human brain at rest.
      ).

      Statistical Analysis

      Multiple imputation analysis and generalized regression modeling with a gamma response distribution was used to describe the association between cardiovascular risk factors and cSVD markers (
      • van Buuren S.
      • Groothuis-Oudshoorn K.
      mice: Multivariate imputation by chained equations in R.
      ). Generalized linear and beta regression models were used to quantify the association between logarithmically transformed WMH volume and peak-width of skeletonized mean diffusivity (PSMD), and spatial coactivation patterns. For cognitive scores, we used generalized linear modeling with gamma and binomial response distributions to assess associations with cSVD markers and brain dynamics. The statistical significance of deviations from the null hypothesis of an absent association was quantified using Wald t tests and adjusted for multiplicity across cognitive measures and brain states using the Bonferroni-Holm method (
      • Holm S.
      A simple sequentially rejective multiple test procedure.
      ). No multiple testing correction was applied across different operationalizations of ischemic WM disease burden (WMH volume, PSMD) and brain dynamics (fractional occupancy, dwell time). Statistical analysis were performed in R version 4.0.5 (
      R Core Team.
      R: A language and environment for statistical computing. R Foundation for Statistical Computing.
      ).

      Results

      Sample Characteristics

      Overall, 1000 MRI session data points were collected from HCHS, corresponding to 986 subjects with usable imaging data. Segmentation of WMHs is exemplified in Figure 1A. The spatial distribution of WMHs is shown in Figure 2B. In 4 subjects, no WMHs were identified and these participants were excluded from further analysis. The numerical distribution of the remaining WMH volumes was skewed to the right with a median of 0.96 mL (interquartile range 0.46–2.2 mL) (Figure 2C). PSMD could be computed for 924 subjects (58 patients did not undergo diffusion-weighted imaging). Median PSMD was 0.000217 (interquartile range 0.000194–0.000245).
      Figure thumbnail gr1
      Figure 1Distribution of white matter lesion burden. (A) Steps involved in white matter hyperintensity segmentation in individual subject space: 1) native FLAIR image; 2) lesion probability map obtained from BIANCA (Brain Intensity Abnormality Classification Algorithm); 3) Voronoi parcellation of spatially varying thresholds obtained from LOCATE (Locally Adaptive Threshold Estimation); 4) binarized lesion mask, retaining clusters containing at least 30 contiguous voxels. Blue (red) indicates deep (periventricular) white matter lesions defined by a distance cutoff of 10 mm to the ventricles. (B) Spatial distribution of white matter hyperintensities. For visualization, individual lesion masks were nonlinearly transformed to Montreal Neurological Institute template space. Note the different scales for deep and periventricular white matter lesions. (C) Numerical distribution of nonzero white matter hyperintensity volumes (N = 979 independent subjects) on a logarithmic scale. Insets indicate mean/standard deviation (red) and median/interquartile range (orange).
      Figure thumbnail gr2
      Figure 2Associations between white matter lesion volume and cognitive performance scores (total N = 979 independent subjects). Opacity of hexagons indicates number of individual patient data falling within each cell. Solid lines represent estimated population means from simple unadjusted generalized linear models (top row: Mini-Mental State Examination, Vocabulary, Word List Recall: binomial with logit link; bottom row: Animal Naming, Trail Making Test [TMT] A/B: gamma with log link). Shaded ribbons indicate pointwise 95% confidence bands. Insets report effect sizes and p values both with (adjusted [Adj]) and without (Raw) adjustment for the nuisance variables age and sex. Effect sizes are quantified as odds ratios (ORs) (top) or response scale multipliers [exp(b)] (bottom) and correspond to a 4.7-fold increase in white matter hyperintensity volume. P(H) indicates Bonferroni-Holm–corrected p values across cognitive scores (six tests).
      For 3 subjects, clinical data were not available, leaving n = 979 subjects (441 females, 45%) for joint analysis of imaging, cardiovascular risk factors, and cognitive function. The mean age was 62.5 years (SD = 8.2 years). Of the patients included in the sample, 666 had hypertension (71.5%; 47 missing), 156 were active smokers (17.5%; 87 missing), and 82 had a diagnosis of diabetes (9.1%; 80 missing); the average body mass index was 26.7 kg/m2 (SD = 4.5 kg/m2; 57 missing). A total of 292 participants had a Framingham risk score exceeding 7.
      Full sets of cognitive scores were available in 673 of 979 (68.7%) subjects. Summary statistics and associations with age are presented in Table 1. Higher age was associated with poorer cognitive performance in most domains. However, older subjects performed better on the multiple choice vocabulary test.
      Table 1Association of Measures of Small Vessel Disease With Demographic and Cardiovascular Risk Factors
      PredictorTotal WMH, N = 979, ncomplete = 758PSMD, n = 924, ncomplete = 710
      Age (per 10 Years)2.11 (1.88–2.36)1.11 (1.10–1.12)
      Sex, Male0.98 (0.82–1.17)0.96 (0.94–0.97)
      Hypertension0.96 (0.78–1.19)1.02 (0.99–1.04)
      Diabetes1.41 (1.03–1.92)1.04 (1.00–1.07)
      BMI (per 1 kg/m2)1.05 (1.02–1.07)1.00 (1.00–1.00)
      Active Smoking1.23 (0.97–1.55)1.03 (1.00–1.05)
      Values are presented as effect size (95% CI).
      Individual models are fitted for WMH volume and PSMD (multiple imputation analysis, m = 10). Estimates of effect sizes are obtained from a multivariable generalized linear regression model with gamma response distribution and logarithmic link function and are thus multiplicative on the response scale.
      BMI, body mass index; PSMD, peak-width of skeletonized mean diffusivity; WMH, white matter hyperintensity.

      Associations Between Cardiovascular Risk Factors, WMH Volume, and Cognition

      Greater WMH volumes were associated with higher age, diabetes, obesity, and smoking. There was no significant association between WMH volume and hypertension. PSMD was associated with higher age, female sex, hypertension, diabetes, and smoking. Multiplicative effect sizes are reported in Table 2.
      Table 2Summary of Cognitive Performance Scores
      Cognitive Performance MeasureNo. MissingMean (SD)Median [Interquartile Range]Association With Age
      Effect Size95% CI
      MMSE (Max. 30)5328 [27–29]OR 0.80 per item and 10 years0.75–0.85
      Word Recall (Max. 10)588 [7–9]OR 0.63 per word and 10 years0.59–0.67
      Vocabulary (MWT-B) (Max. 37)25930.8 (3.9)32 [29–34]OR 1.20 per word and 10 years1.15–1.25
      Animal Naming4624.9 (7.1)24 [20–30]0.94-fold decrease per 10 years0.92–0.96
      TMT-A, seconds14839.3 (14.7)36 [29–46]1.19-fold increase per 10 years1.16–1.23
      TMT-B, seconds15686.2 (36.9)78 [63–99]1.20-fold increase per 10 years1.17–1.24
      Descriptive data for N = 979 subjects and association with increasing age, assessed by multivariable generalized linear regression analysis adjusted for sex and years of formal education. MMSE, word recall, and MWT-B scores are modeled with a binomial response distribution and logit link function; animal naming and TMT scores are modeled with a gamma response distribution and logarithmic link function.
      Max., maximum; MMSE Mini-Mental State Examination; MWT, Mehrfach-Wortschatz test; OR, odds ratio; TMT, Trail Making Test.
      Associations between WMH volume and cognitive scores obtained from generalized linear regression modeling are shown in Figure 3. Adjusted for age and sex, extent of WM disease was associated with impaired executive function. For every 4.7-fold increase in WMH volume (corresponding to the interquartile ratio Q1WMH/Q3WMH), the model predicted a 1.06-fold (95% CI = 1.01–1.10) longer completion time in part B of the TMT (p = .0075, pHolm = .0448), where the Bonferroni-Holm correction is performed over six tests corresponding to different cognitive scores. There was a trend toward an association between WMH volume and reduced short-term memory with an estimated interquartile odds ratio of recalling an item in the delayed recall test of 0.93 (95% CI = 0.87–1.01, p = .0778).
      Figure thumbnail gr3
      Figure 3Discrete brain states obtained from k-means clustering of concatenated preprocessed blood oxygen level–dependent signals. (A) Cumulative and incremental variance explained as a function of the number of clusters (k). Red dots indicate the selected number of k = 5 clusters. (B) Absolute number of subjects whose 400-dimensional blood oxygen level–dependent time series fails to reach each k-identified cluster in regional activation space at least once as a function of k. For fewer than six clusters, each subject visits each cluster at least once. (C) Examples of state space trajectories for 10 individual subjects. Time is represented in multiples of repetition time (TR) (3 seconds), colors indicate different brain states (violet, default mode network [DMN]+; green, DMN−; blue, visual network [VIS]+; red, VIS−; orange, frontoparietal control network [FPCN]−). (D) Green and red dots represent cosine-similarity between positive and negative components of each state and binary indicators of each of seven predefined resting-state networks (
      • Gutierrez-Barragan D.
      • Basson M.A.
      • Panzeri S.
      • Gozzi A.
      Infraslow state fluctuations govern spontaneous fMRI network dynamics.
      ). (E) Anatomical representations of each brain state with coordinates of their centroids in 400-dimensional activation space mapped to colors of corresponding parcels of the Schaefer atlas (
      • DeCarli C.
      • Fletcher E.
      • Ramey V.
      • Harvey D.
      • Jagust W.J.
      Anatomical mapping of white matter hyperintensities (WMH): Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden.
      ,
      • Gale D.J.
      • Vos de Wael R.
      • Benkarim O.
      • Bernhardt B.
      Surfplot: Publication-ready brain surface figures.
      ). DAT, dorsal attentional network; LIM, limbic network; SAL, salience network; SMN, sensorimotor network.
      Similar associations between PSMD and cognitive function are shown in Figure S3. Adjusted for age and sex, every 1.26-fold in increase in PSMD (interquartile ratio Q1PSMD/Q3PSMD) was associated with a 1.06-fold (95% CI = 1.02–1.12, p = .0032, pHolm = .0161) longer completion time in TMT-B and 1.10-fold increased odds of correctly identifying a word from the lexicon in the Mehrfach-Wortschatz test (95% CI = 1.04–1.16, p = .0022, pHolm = .0130).

      Coactivation Patterns as Discrete Brain States

      The variance explained by clustering concatenated BOLD signals into k discrete clusters in 400-dimensional Euclidean space, for k ranging from 2 to 12, and the incremental R2 gain associated with each additional cluster are shown in Figure 4A. Less than 1% additional variance was explained by incrementing the number of clusters beyond k = 5. Moreover, the number of subjects who failed to visit each state at least once markedly increased at this value (Figure 4B), and k = 5 was therefore chosen for further analyses. The identified clusters of recurrent spatial coactivation patterns are shown in Figure 4C. According to their similarity with the DMN, visual network (VIS), and frontoparietal control network (FPCN), we named these states DMN+, DMN−, FPCN+, FPCN−, VIS+, and VIS− (Figure 4D).
      Figure thumbnail gr4
      Figure 4Associations between white matter lesion volume and fractional occupancy/mean dwell time (DT) as state-specific metrics of spatial coactivation patterns. Opacity of hexagons indicates number of individual patient data falling within each cell. Solid lines represent estimated population means from fixed-dispersion beta regressions with logit link function in the case of fractional occupancies (top row) and from simple generalized linear regressions with gamma response function and log link function in the case of DTs (bottom row). Shaded ribbons indicate pointwise 95% confidence bands. DT is given in units of repetition time (TR) (2.5 seconds). Insets report effect sizes and p values both with (adjusted [Adj.]) and without (Raw) adjustment for the nuisance variables age and sex. Effect sizes are quantified as odds ratios (OR) (top) or response scale multipliers [exp(b)] (bottom) and correspond to a 4.7-fold increase in white matter hyperintensity volume. P(H) indicates Bonferroni-Holm–corrected p values across brain states (five tests, separately for fractional occupancy and DT). DMN, default mode network; FPCN, frontoparietal control network; TR, repetition time; VIS, visual network.
      Brain states were similar when constructed separately from patients with WMH volume lower than the first or greater than the third quantile of the whole-sample WMH volume distribution. The spatial correlation between cluster centroids varied between 0.956 (FPCN−) and 0.999 (DMN+).

      Whole-Brain Spatial Coactivation Patterns

      Subjects switched brain states roughly once every two frames (median number of transitions = 68, interquartile range = 64–71; average dwell time [1.85 ± 0.16] × TR). Global switching rate and average dwell time were associated with WM damage. For any 4.7-fold increase in WMH volume, an extra 0.64 (95% CI = 0.18–1.10, p = .0062) state transitions and a dwell time reduction by 0.017 × TR (95% CI = [0.004–0.030] × TR, p = .0120), corresponding to 0.043 seconds, were estimated. Similarly, any 1.26-fold increase in PSMD was associated with an extra 0.55 (95% CI = 0.05–1.06, p = .0342) state transitions and a dwell time reduction by 0.015 × TR (95% CI = [0.001 – 0.030] × TR, p = .0376).
      We observed higher fractional occupancies in DMN+ (mean ± SD: 25.1 ± 7.2%) and DMN− (25.5 ± 7.2%) compared with VIS− (15.8 ± 7.0%), VIS+ (17.6 ± 6.7%), and FPCN− (16.0 ± 6.2%). Consistently, the average dwell times in states DMN+ (1.98 ± 0.40 TR) and DMN− (2.01 ± 0.41 TR) were longer than in VIS− (1.60 ± 0.36 TR), VIS+ (1.65 ± 0.36 TR), and FPCN− (1.62 ± 0.34 TR).
      Associations with WMH volume are depicted in Figure 4. In beta regressions adjusted for age and sex, patients with higher WMH volumes spent less time in the high-occupancy states DMN+ (0.95-times reduction of the odds of occupying DMN+ for every 4.7-fold increase in WMH volume, p = .0108, pHolm = .0325, corrected over five tests corresponding to different brain states) and DMN− (odds ratio = 0.95, p = .0036, pHolm = .0144) and more time in the low-occupancy state FPCN− (odds ratio = 1.07, p = .0010, pHolm = .0050). On a linear scale, these figures correspond to reductions in fractional occupancy of 0.75 (DMN+) and 0.84 (DMN−) percentage points and an increase of 1.0 percentage points (FPCN−), respectively. Consistently, higher WMH burden was associated with reduced average dwell times in default mode states (DMN+: relative shortening = 0.98, p = .0776, pHolm = .3100; DMN−: 0.97, p = .0020, pHolm = .0099), corresponding to absolute reductions on a linear scale of 0.03 × TR (DMN+) and 0.06 × TR (DMN−), respectively. Similar effects were seen for PSMD (Figure S5).
      There were no significant associations between age and fractional occupancy or dwell time in any brain state.

      Association Between Measures of Brain Dynamics and Cognitive Performance

      There was no association between switch rate or average dwell time and cognitive function. Regression modeling adjusted for age, sex, years of education, and severity of WM disease (WMH volume) revealed that deficits in executive functioning, as measured by longer completion times in part B of the TMT, were associated with less time spent in state DMN+ (0.94-fold reduction for each additional TR; 95% CI = 0.88–1.00, p = .0477, pHolm > .9999, corrected over 30 tests corresponding to different brain states and cognitive domains) and more time spent in state VIS− (1.08-fold increase per TR; 95% CI = 1.00–1.16, p = .0478, pHolm > .9999). Higher scores on the vocabulary test were associated with more time spent in state VIS+ (1.12-fold increase per TR; 95% CI = 1.02–1.23, p = .0181, pHolm = .5430). No significant associations were observed between fractional occupancy or dwell time and other cognitive scores (Table S1).

      Discussion

      Our analysis yielded two main results. First, after identifying discrete brain states as clusters of recurrent activity and reproducing their characteristic occupancy pattern (
      • Hindriks R.
      • Adhikari M.H.
      • Murayama Y.
      • Ganzetti M.
      • Mantini D.
      • Logothetis N.K.
      • Deco G.
      Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? [published correction appears in Neuroimage 2016; 132:115].
      ), we showed that increased WMH burden is associated with a reduction of time spent in high-occupancy states and an increase of time spent in low-occupancy states. Second, time spent in states aligned with the DMN was an independent predictor for poor performance in the executive function task.
      We analyzed a risk-enhanced subgroup of HCHS. Our sample was mildly affected by cSVD pathology with a median WMH volume of 0.9 mL, explained by a low median age of 62.5 years and our anatomy-informed segmentation approach with an emphasis on avoiding false positives.
      Of the different cognitive domains, only executive function as measured by the TMT-B was associated with WMH volume after adjusting for age. The effect size estimate of 6% increase in completion time for every 4.7-fold increase in WMH load is consistent with previous reports from the UK Biobank, where a 2.7-fold increase in WMH volume was associated with a 4.5% increase in reaction times in a variant of the Snap card game (
      • Veldsman M.
      • Kindalova P.
      • Husain M.
      • Kosmidis I.
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      Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities.
      ). The observed pattern of neuropsychological deficits with greatest impairments in executive functions and relative sparing of cortical functions, such as episodic memory, is typical for vascular cognitive impairment (
      • Prins N.D.
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      • den Heijer T.
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      Cerebral small-vessel disease and decline in information processing speed, executive function and memory.
      ,
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      ,
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      • Wolf P.A.
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      Biobehavioral characteristics of nondemented older adults with subclinical brain atrophy.
      ), confirming ischemic WM disease as a predominantly subcortical pathology (
      • Kertesz A.
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      Neuropsychological deficits in vascular dementia vs Alzheimer’s disease: Frontal lobe deficits prominent in vascular dementia.
      ,
      • Graham N.L.
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      ). Supplemental analyses (Figures S5 and S6) suggest that the association between WMH and executive dysfunction might be driven in large part by periventricular lesions, supporting the recent notion that these might differ in pathophysiology and clinical sequelae from lesions in the deep white WM (
      • Griffanti L.
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      Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults.
      ,
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      Classification of white matter lesions on magnetic resonance imaging in elderly persons.
      ,
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      Increase in periventricular white matter hyperintensities parallels decline in mental processing speed in a non-demented elderly population.
      ,
      • Jiménez-Balado J.
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      Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment.
      ,
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      • Abeles N.
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      • Bozoki A.
      Regional white matter pathology in mild cognitive impairment: Differential influence of lesion type on neuropsychological functioning.
      ).
      Clustering BOLD signals in regional activity space identified five discrete brain states. Consistent with previous reports, dwell times and fractional occupancies were greater in brain states characterized by activation or suppression of the DMN (+/−) compared with states orthogonal to the DMN (
      • Hindriks R.
      • Adhikari M.H.
      • Murayama Y.
      • Ganzetti M.
      • Mantini D.
      • Logothetis N.K.
      • Deco G.
      Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? [published correction appears in Neuroimage 2016; 132:115].
      ).
      Our first main finding contributes to the search for structural determinants of spatiotemporal brain dynamics. In an analysis of the Human Connectome Project, the global geometry and topology of the spatially embedded brain network were implicated as important factors modulating temporal fluctuations of brain activity (
      • Fukushima M.
      • Sporns O.
      Structural determinants of dynamic fluctuations between segregation and integration on the human connectome.
      ). Focusing on discrete brain states, an application of network control theory to the Philadelphia Neurodevelopment Cohort showed that the transition probabilities between states are constrained by linear spread of activity along the structural connectome (
      • Hindriks R.
      • Adhikari M.H.
      • Murayama Y.
      • Ganzetti M.
      • Mantini D.
      • Logothetis N.K.
      • Deco G.
      Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? [published correction appears in Neuroimage 2016; 132:115].
      ). It would therefore be conceivable that WMH, preferentially damaging long-range fibers (
      • Petersen M.
      • Frey B.M.
      • Schlemm E.
      • Mayer C.
      • Hanning U.
      • Engelke K.
      • et al.
      Network localisation of white matter damage in cerebral small vessel disease.
      ), would lead to impaired communications in brain networks such as the DMN, which relies on distributed processing within anterior and posterior brain regions. While the effect of ischemic WMH on static FC has been described extensively elsewhere (
      • Schulz M.
      • Malherbe C.
      • Cheng B.
      • Thomalla G.
      • Schlemm E.
      Functional connectivity changes in cerebral small vessel disease—A systematic review of the resting-state MRI literature.
      ), dFC has only recently been investigated in the context of ischemic vascular disease; in a small study of 19 patients with subcortical ischemic vascular disease, an increase in time spent in a high-occupancy weakly connected brain state was found compared with healthy control subjects (
      • Fu Z.
      • Caprihan A.
      • Chen J.
      • Du Y.
      • Adair J.C.
      • Sui J.
      • et al.
      Altered static and dynamic functional network connectivity in Alzheimer’s disease and subcortical ischemic vascular disease: Shared and specific brain connectivity abnormalities.
      ). More recently, a study of 101 patients with subcortical ischemic vascular disease found no consistent pattern of altered brain state occupancy along a clinical spectrum from asymptomatic to amnestic and nonamnestic mild cognitive impairment (
      • Xu Y.
      • Shang H.
      • Lu H.
      • Zhang J.
      • Yao L.
      • Long Z.
      Altered dynamic functional connectivity in subcortical ischemic vascular disease with cognitive impairment.
      ). Due to differences in inclusion criteria and severity of WM pathology, comparability with the present work is limited.
      Our results are also broadly consistent with the disturbances of spatiotemporal brain dynamics associated with focal lesions. In patients with ischemic stroke, a sliding window analysis revealed altered preferences for cortical, subcortical, and cerebellar subdomains of a brain network related to motor function that were modulated by the severity of clinical deficits (
      • Bonkhoff A.K.
      • Espinoza F.A.
      • Gazula H.
      • Vergara V.M.
      • Hensel L.
      • Michely J.
      • et al.
      Acute ischaemic stroke alters the brain’s preference for distinct dynamic connectivity states.
      ). In a separate cohort, patients severely affected by stroke preferentially occupied a brain state characterized by high dynamic segregation, reflecting important aspects of the structural connectome after stroke (
      • Bonkhoff A.K.
      • Schirmer M.D.
      • Bretzner M.
      • Etherton M.
      • Donahue K.
      • Tuozzo C.
      • et al.
      Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke.
      ,
      • Cheng B.
      • Schlemm E.
      • Schulz R.
      • Boenstrup M.
      • Messé A.
      • Hilgetag C.
      • et al.
      Altered topology of large-scale structural brain networks in chronic stroke.
      ,
      • Schlemm E.
      • Schulz R.
      • Bönstrup M.
      • Krawinkel L.
      • Fiehler J.
      • Gerloff C.
      • et al.
      Structural brain networks and functional motor outcome after stroke—A prospective cohort study.
      ). At a descriptive level, the observed decrease in time spent in high-occupancy states related to the DMN and simultaneous increase in time spent in low-occupancy states orthogonal to the DMN, in association with increasing WMH load, may be described as a dedifferentiation of brain state preference, although this must not be confused with similar notions from cell or developmental biology.
      The association between WMH volume and dedifferentiation of brain state preference was expected to be confounded by age. Increasing age is a risk factor for cSVD (
      • van Dijk E.J.
      • Prins N.D.
      • Vrooman H.A.
      • Hofman A.
      • Koudstaal P.J.
      • Breteler M.M.B.
      Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study.
      ), and structural attributes of WM are recognized as important imaging markers of biological brain age (
      • Cole J.H.
      • Franke K.
      Predicting age using neuroimaging: Innovative brain ageing biomarkers.
      ). Changes in spatiotemporal brain activation patterns across the life span, on the other hand, are less well understood. By interpreting dFC as a random walk, reduced complexity has been identified as a hallmark of age-related changes in spatiotemporal dynamics (
      • Battaglia D.
      • Boudou T.
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      • Lombardo D.
      • Chettouf S.
      • Daffertshofer A.
      • et al.
      Dynamic functional connectivity between order and randomness and its evolution across the human adult lifespan.
      ). Contrasting results have been obtained in younger people, suggesting an inverted U-shaped relationship between age and state fluidity similar to static FC (
      • Betzel R.F.
      • Byrge L.
      • He Y.
      • Goñi J.
      • Zuo X.N.
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      Changes in structural and functional connectivity among resting-state networks across the human lifespan.
      ,
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      Topological organization of the human brain functional connectome across the lifespan.
      ,
      • Douaud G.
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      A common brain network links development, aging, and vulnerability to disease.
      ,
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      ,
      • Luo N.
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      ); in 879 healthy participants between 8 and 22 years, more time spent in high-occupancy DMN-related brain states was associated with increasing age, reflecting an increase in differentiation during development. In 780 adolescents, increasing age was associated with higher state transition flexibility (
      • Medaglia J.D.
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      Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment.
      ). Similar results of more variable connectivity patterns in association with increasing age manifesting as gradients in mean dwell time and fractional state occupancy were observed in a cross-sectional analysis of 51 children and young adults (
      • Hutchison R.M.
      • Morton J.B.
      Tracking the brain’s functional coupling dynamics over development.
      ). In our sample, neither fractional occupancy nor dwell time in any state were associated with age. This would still be compatible with an inverted U-shaped relationship, given that the median age of 62.5 years in our sample is relatively close to the apex of the age-FC relationship described in (
      • Betzel R.F.
      • Byrge L.
      • He Y.
      • Goñi J.
      • Zuo X.N.
      • Sporns O.
      Changes in structural and functional connectivity among resting-state networks across the human lifespan.
      ). The fact that, in contrast, an association between WMH pathology and brain state metric does exist might point toward a complex relationship between chronological age and brain age, in which WMH pathology anticipates functional changes that are otherwise only seen in older subjects.
      Our data suggest that dedifferentiation of brain state preference is associated with executive function deficits. This effect occurred primarily in functional brain states critical to attention-related cognitive tasks, such as the DMN. Previous static FC analyses have shown an age-dependent effect of lower DMN integrity on TMT-B (
      • Damoiseaux J.S.
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      • Barkhof F.
      • Scheltens P.
      • Stam C.J.
      • et al.
      Reduced resting-state brain activity in the “default network” in normal aging.
      ). Our findings add evidence to the hypothesis that normal cognitive function is contingent on well-regulated brain dynamics characterized by flexibility and complexity in the transitions between latent states (
      • Lombardo D.
      • Cassé-Perrot C.
      • Ranjeva J.P.
      • Le Troter A.
      • Guye M.
      • Wirsich J.
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      Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation.
      ,
      • Battaglia D.
      • Boudou T.
      • Hansen E.C.A.
      • Lombardo D.
      • Chettouf S.
      • Daffertshofer A.
      • et al.
      Dynamic functional connectivity between order and randomness and its evolution across the human adult lifespan.
      ,
      • Medaglia J.D.
      • Satterthwaite T.D.
      • Kelkar A.
      • Ciric R.
      • Moore T.M.
      • Ruparel K.
      • et al.
      Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment.
      ,
      • Taghia J.
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      • Nicholas J.
      • Chen T.
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      Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition.
      ,
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      ). The dedifferentiation hypothesis of aging maintains that cortical activity in response to behavioral demands becomes less selective across the life span (
      • Grady C.
      The cognitive neuroscience of ageing.
      ). This loss of selectivity may indicate cognitive disruption and explain age-related variability in executive function and processing speed (
      • Prins N.D.
      • van Dijk E.J.
      • den Heijer T.
      • Vermeer S.E.
      • Jolles J.
      • Koudstaal P.J.
      • et al.
      Cerebral small-vessel disease and decline in information processing speed, executive function and memory.
      ). Changes in the organization of FC in the brain might be responsible for age-related dedifferentiation of cognitive function (
      • Goh J.O.S.
      Functional dedifferentiation and altered connectivity in older adults: Neural accounts of cognitive aging.
      ). Our results imply that dedifferentiation also occurs at the dynamic network level, manifesting as the equalization of time spent in different brain states. In a potential alternative explanation of our findings, dedifferentiation might occur as a compensatory mechanism. The observed association between dedifferentiation and executive impairment, however, suggests that this compensation would be unsuccessful (
      • Cabeza R.
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      • Duarte A.
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      ).
      We quantified severity of cSVD mainly by the total volume of WMHs. PSMD was explored as a marker of microstructural WM disease (
      • Baykara E.
      • Gesierich B.
      • Adam R.
      • Tuladhar A.M.
      • Biesbroek J.M.
      • Koek H.L.
      • et al.
      A novel imaging marker for small vessel disease based on skeletonization of white matter tracts and diffusion histograms.
      ). Robustness of reported associations was demonstrated by being reproducible in the context of both imaging markers capturing complementary aspects of cSVD pathology.
      Our results are constrained by limitations. On average, study participants were only mildly affected by cSVD. This allowed the application of established processing algorithms for structural and functional MRI and reduced the risk of significant alterations in neurovascular coupling; small WMH volumes, however, limit generalizability of conclusions to more severely affected populations. We cannot rule out that spatially heterogeneous effects of aging and WM disease on neurovascular coupling, even in a mildly affected population, could explain part of the observed associations. Second, due to the cross-sectional observational design of the study, no causal relationships can be asserted. In particular, we were unable to test the hypothesis that altered spatiotemporal patterns of brain activation estimated by BOLD signal intensity causally mediate the effect of ischemic WM disease on cognitive impairment. Even in a longitudinal study design, the lack of controlled interventions would make such an analysis very difficult. Further, no Alzheimer’s disease–specific biomarkers, such as positron emission tomography–computed tomography or cerebrospinal fluid analysis, were available in this HCHS sample. We are therefore unable to quantify the effect of nonvascular pathology of cognitive function or spatial coactivation patterns, in particular with regards to a putative brain state related to the limbic system, which was not identified in our decomposition of brain activation space.
      The paper provides the first characterization of whole-brain temporospatial coactivation patterns in the presence of cSVD and their relevance for associated cognitive deficits. We showed that imaging markers of ischemic WM disease are related to an equalization of time spent in different brain states and that, even conditional on structural brain damage, this dedifferentiation is associated with deficits in executive function as the hallmark of subcortical cognitive impairment.

      Acknowledgments and Disclosures

      The Hamburg City Health Study is supported by Amgen , Astra Zeneca , Bayer , BASF , Deutsche Gesetzliche Unfallversicherung , DIFE , the Innovative Medicines Initiative (Grant No. 116074 ), the Fondation Leducq (Grant No. 16CVD03 ), Novartis , Pfizer , Schiller , Siemens , Unilever, and “Förderverein zur der Förderung HCHS e.V.” Work on the presented project was supported by funding from the Deutsche Forschungsgemeinschaft (German Research Foundation) (Grant No. 178316478 [to CG, project C1; to BC and GT, project C2].
      ES conceived of the project, analyzed the data, and wrote the manuscript. BMF contributed to structural image processing. CM and MP performed manual white matter lesion segmentations. JF and UH oversaw image acquisition and quality assurance. SK, RT, JG, and CG administrated the study and acquired funding. GT and BC supervised the project. All authors critically reviewed and edited the manuscript.
      We acknowledge all participants of the Hamburg City Health Study and cooperation partners, patrons, and the Deanery from the University Medical Center Hamburg-Eppendorf for supporting the Hamburg City Health Study.
      Processed summary data and code used for the analyses that support the findings of this study are openly available at https://github.com/csi-hamburg/dFC-COG-HCHS. Raw data including MRI are not publicly available because they contain information that could compromise the privacy of research participants.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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