Brain functional organization and structure in patients with arteriovenous malformations · 2019-07-15 · FUNCTIONAL NEURORADIOLOGY Brain functional organization and structure in

FUNCTIONAL NEURORADIOLOGY

Brain functional organization and structure in patientswith arteriovenous malformations

Paul-Noel Rousseau1,2& Roberta La Piana2 & Xiaoqian J. Chai3 & Jen-Kai Chen1

&

Denise Klein1,4& Donatella Tampieri2,5

Received: 7 April 2019 /Accepted: 6 June 2019# Springer-Verlag GmbH Germany, part of Springer Nature 2019

AbstractPurpose Developmental in nature, brain arteriovenous malformations (AVM) have the potential to affect whole brain organiza-tion. Here we investigated the impact of AVM on functional and structural brain organization using resting-state functional MRI(rsfMRI) and cortical thickness measures.Methods We investigated brain functional organization and structure using rsfMRI in conjunction with cortical thicknessanalyses in 23 patients with cerebral arteriovenous malformations (AVMs) and 20 healthy control subjects.Results Healthy controls showed the expected anti-correlation between activity in the default mode network (DMN) and frontalareas that are part of the attentional control network. By contrast, patients demonstrated a disruption of this anti-correlation.Disruptions to this anti-correlation were even observed in a subgroup of patients with lesions remote from the main nodes of theDMN and were unrelated to differences in perfusion. Functional connectivity differences were accompanied by reduced corticalthickness in frontal attentional areas in patients compared to the controls.Conclusions These results contribute to the discussion that AVMs affect whole brain networks and not simply thearea surrounding the lesion.

Keywords Arteriovenousmalformations . Resting state networks . Cortical thickness . Brain development

Introduction

Brain arteriovenous malformations (AVMs) are lesions of thevascular system involving direct connection between arteriesand veins without an interposing capillary bed [1]. The

etiology and natural history of AVMs is unclear. WhileAVMs have been regarded as congenital lesions resultingfrom errors in embryonic vascular morphogenesis, there isincreasing evidence that they may also develop after birthand continue to be modified by environmental factors andaberrant gene expression throughout the lifespan [1, 2].AVMs often only become symptomatic in adulthood withthe most common presenting symptoms being seizures orintra-cerebral hemorrhage [3, 4]. A small subset of patients,7–15% depending on the study, will present with focal neuro-logical deficits [5]. Many patients with AVMs remain asymp-tomatic and their lesions are only discovered incidentally [6].

The developmental nature of AVMs, and their formationduring critical periods of brain development, as well as previ-ous evidence that points to functional plasticity in patients, hasled to the question of the potential effects of AVMs on wholebrain organization and structure [7, 8]. A promising methodfor investigating this question is resting-state functional MRI(rsfMRI). Coherent low-frequency fluctuations in blood oxy-gen level–dependent (BOLD) signal when an individual is in astate of wakeful rest are thought to reflect the brain’s intrinsicfunctional organization [9, 10]. Correlations in the

DK and DT are joint senior authors and contributed equally to this work.

* Paul-Noel [email protected]

1 Cognitive Neuroscience Unit, Montreal Neurological Institute,McGill University, 3801 University Street, Montreal, QC H3A 2B4,Canada

2 Department of Neuroradiology, Montreal Neurological Institute,McGill University, Montreal, QC H3G 2A8, Canada

3 Cognitive Neurology/Neuropsychology Division, Department ofNeurology, Johns Hopkins University School of Medicine,Baltimore, MD, USA

4 Centre for Research on Brain, Language and Music, McGillUniversity, Montreal, QC H3G 2A8, Canada

5 Queen’s University, Kingston, Ontario, Canada

Neuroradiologyhttps://doi.org/10.1007/s00234-019-02245-6

spontaneous BOLD signal between different brain regionsallow for the delineation of functional networks [9, 11].

In the present study, we aimed to investigate resting stateconnectivity in patients with AVMs compared to a group ofhealthy control subjects with a focus on the default modenetwork (DMN) and its anti-correlation with the attentionalnetwork. DMN consists of a set of brain regions that are en-gaged when an individual is not involved in a task requiringexternal attention. This network includes the posterior cingu-late cortex, the medial prefrontal cortex, and the lateral parietalcortex [9]. The activity in this network is generally found, inhealthy subjects, to be anti-correlated with a task-positive net-work consisting of regions in the intraparietal sulcus and in-ferior parietal lobe, pre-central sulcus, dorsolateral prefrontalcortex, insula/frontal operculum, and the supplementary mo-tor area that are engaged in tasks requiring external attention[11]. Abnormalities in the DMN and its anti-correlation withthe task-positive network have been observed in several psy-chiatric and neurological disorders including schizophrenia,attention deficit hyperactivity disorder, and even in normalaging [12–16]. Rudimentary versions of these networks existfrom early infancy and continue to develop until early-adulthood potentially coinciding with the developmental tra-jectory of AVMs [17, 18]. Because of the potential of AVMsto cause abnormalities in the BOLD signal [7], we performedan additional perfusion analysis using pseudo-continuous ar-terial spin labeling (PCASL) to confirm that resting state find-ings were not artifacts of abnormal perfusion. We also soughtto supplement the resting-state connectivity analysis with ananatomical analysis of cortical thickness to assess whetherfunctional connectivity differences were accompanied bythinning or thickening of the cortex.

Methods

Participants

The sample was comprised of 23 consecutive patients withbrain AVMs (11 females, average age of 33 years) and 20healthy control subjects (7 females, average age 30 years) thatwere matched for age (t = 1.076, df = 41, p = 0.288) and sex.(Demographic and clinical information for the patients isprovided in Table 1).

The diagnosis of AVM was made at the Department ofNeuroradiology of the Montreal Neurological Hospital by neu-roradiologists with specific expertise in cerebrovascular disordersand endovascular intervention. Diagnosis was based on brainMRI obtained in the clinical setting and confirmed with conven-tional angiogram. Based on clinical exam, all patients included inthe study were also determined to be without a major cognitivedeficit. Patients with previous surgical, radiosurgical, or

endovascular treatment and those with a history of intra-cerebral hemorrhage were not included in the study.

We first performed the analyses on the whole patient groupwhose lesions are depicted in Fig. 1a. Due to the prominenceof the frontal and parietal regions in the DMN [9] and thepotential for hemodynamic confounds in interpreting restingstate results in patients with AVM lesions in the fronto-parietalnetwork, we then performed a second analysis on a subset of12 patients with non-fronto/parietal lesions which are depictedin Fig. 1b [19].

MRI data acquisition

The imaging datawas acquired on a 3TSiemensTrioTim scannerusing a 12-channel head coil at the McConnell Brain ImagingCentre of the Montreal Neurological Institute. T1-weighted im-ages were acquired using a 3D magnetization–prepared rapidgradient echo (MP-RAGE) sequence (slice thickness = 1 mm,TR= 2300 ms, TE= 2.98 ms, matrix size = 256 × 256, FoV=256mm, flip angle = 9 deg., interleaved excitation). Resting stateimages were acquired with a 2D EPI (echo planar imaging se-quence) with 42 3.5-mm-thick transverse slices covering thewhole brain (TR= 2210 ms, TE = 30 ms, matrix size = 64 × 64,FoV = 224 mm, flip angle = 90 deg); 136 volumes were ac-quired. Perfusion data was acquired in a subset of the patientsample (n = 11)with PCASL (TR= 4000ms, TE= 9.9ms, voxelsize = 3.5 × 3.5 × 6.0 mm, label duration = 1.48 s, post-label de-lay = 1.2 s). A separate equilibriummagnetizationmap (M0 scan)without labeling was also obtained with the same parameters(TR = 15,000 ms) in order to estimate cerebral blood flow(CBF). Imaging data was acquired over an extended time frame,and the PCASL was only included towards the end of datacollection.

Resting-state fMRI analysis

Resting-state fMRI data were first preprocessed using SPM8using standard preprocessing steps including slice timing correc-tion, realignment, coregistration to structural image, normaliza-tion, and smoothing with a 6-mm full width at half maximum(FWHM) Gaussian kernel [20]. Motion outliers were identifiedusing ART (Artifact Detection Tools), and removed from theanalysis. Outliers were defined as volumes in which head move-ment exceeded 1 mm from the previous volume or volumes inwhich the average intensity was more than 3 standard deviationsfrom the mean intensity of the session.

Seed-based resting-state connectivity analyses were per-formed using the Matlab/SPM based CONN toolbox [20].We performed our analysis by estimating the temporal corre-lation between the BOLD signal in our seed and the rest of thebrain. We selected a seed in the ventro-medial prefrontal cor-tex corresponding to one of the primary nodes of the DMN.This was defined as a 10-mm sphere at MNI coordinates (− 1,

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49, − 5) [11]. To produce first-level correlation maps, theBOLD time course from this seed region was extracted, andPearson’s correlation coefficients were calculated betweenthis and that of all other brain voxels. These correlation coef-ficients were converted to normally distributed Z scores usinga Fisher transformation in order to allow for second-levelanalyses. All reported clusters survived an FWE-correctedthreshold of p < 0.01 and an uncorrected voxel-level thresholdof p < 0.001, two sided.

Following the suggestion that the SD of the time series isreflective of cerebrovascular reactivity [21] and in order to probethe reliability of using BOLD-derived functional connectivitymetrics in a patient population with vascular malformations suchas in our sample, we examined the overall standard deviation(SD) of the denoised BOLD time series in masks of non-lesionareas of the patients and in the whole brain in controls. We usedan F test to compare the standard deviation of the BOLD timeseries between patients and controls.

Cortical thickness analysis

Cortical reconstruction and volumetric segmentation wereperformed using Freesurfer imaging suite (http://surfer.nmr.mgh.harvard.edu/). These procedures have beendescribed in detail in prior publications [22–24].Processing includes motion correction and averaging ofvolumetric T1 images [24], removal of non-brain tissue,Talairach transformation, intensity normalization [25], tes-sellation of the gray/white matter boundary, automatedtopology correction [26], and surface deformation

following intensity gradients to optimize placement ofgray/white matter and gray/cerebrospinal fluid (CSF)boundaries at the location where the greatest intensityshift defines the transition to the other tissue class [22,23]. The cortical surface was parcellated into 34 regionsusing the Desikan atlas implemented in Freesurfer [27].Cortical thickness was calculated as the closest distancefrom the gray/white matter boundary to the gray matter/CSF boundary at each vertex of the tessellated surface[23]. Data maps were smoothed with a 20-mm FWHMGaussian kernel. Because of the potential for corticalAVMs to interfere with the cortical reconstruction, wechose not to perform a whole brain analysis, instead thefocus was on specific brain regions which were remotefrom the lesions of our AVM group.

Average cortical thickness within regions of interest inthe left and right dorsolateral prefrontal cortex was ex-tracted and an independent samples t test was conductedto compare cortical thickness in these regions between thepatients with non-frontal/parietal lesions and the controlgroup. Using Pearson’s correlations, we also evaluated therelationship between the resting-state connectivity find-ings and cortical thickness within these ROIs [28].

Perfusion analysis

A perfusion analysis was conducted to control for poten-tial effects of perfusion on resting state connectivity. Datawere processed using ASLtbx [29] with SPM8. Labeledand unlabeled arterial spin labelling (ASL) images were

Table 1 Demographic andclinical background of AVMpatients scanned consecutively

Patient Age Sex AVMhemisphere

AVM location Presenting symptom Spetzler-Martin grade

1 43 F L Parieto-occipital Headache IV2 29 F R Basal ganglia Motor deficit IV3 34 F R Parieto-occipital Migraine III4 32 M L Temporal Headache I5 37 F R Temporo-parietal Seizures II6 18 F L Fronto-temporal-basal ganglia Seizures III7 31 F R Fronto-parietal Migraine III8 27 M R Fronto-parietal Seizures/sensory deficit IV9 32 F L Sylvian Headache II10 41 M R Fronto-temporal Headache II11 20 M L Occipital Seizure/migraine II12 52 F L Temporo-occipital Migraine II13 34 M L Frontal Seizures III14 32 M L Temporal Seizures II15 34 M R Fronto-parietal Incidental finding IV16 51 F L Temporal Seizures/headache III17 26 M L Temporal Headache II18 25 M R Temporal Motor deficit V19 38 M L Temporal Migraine I20 28 M L Temporal Seizures III21 28 M L Basal ganglia Motor deficit IV22 36 F R Parietal Seizures II23 27 M L Parietal Headache IV

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independently motion corrected and then a combinedmean image was created which was then coregistered tothe T1-weighted image. The ASL images were thenresliced to match the mean image and spatially smoothedwith a 6-mm FWHM Gaussian kernel. Cerebral bloodflow (CBF) was estimated by subtraction resulting in amean CBF image. CBF calibration (to yield perfusionmaps in ml/100 g/min) was conducted using the followingequation:

f ¼ ΔMλR1a exp ωR1að Þ2M 0α

1− exp −τR1að Þ½ �−1

where f is CBF, ΔM is the signal difference between con-trol and label images, R1a is the longitudinal relaxationrate of blood, τ is the labeling time, ω is the post-label

delay time, α is the labeling efficiency, λ is blood/tissuewater partition coefficient, and M0 is approximated by thecontrol image intensity [29]. The parameters used in thisstudy were as follows: R1a = 0.67 s−1, τ = 1480 mms, ω =1200 ms, α = 0.85, λ = 0.9 g/ml. The T1-weighted imagewas then normalized using SPM8’s unified segmentation-normalization and these parameters were used to reslicethe CBF and T1 images to standard space. Resultant CBFimages were then masked with SPM8’s default brain maskin order to remove non-brain voxels. Average perfusionvalues within masks of bilateral frontal lobes were thenextracted for each of the participants. We used frontallobes as masks because they contain both the primarynode of the DMN (the medial prefrontal cortex) and theregion of the DLPFC implicated in the attentionalnetwork.

Fig. 1 Lesion spatial probability map for the whole patient group (a) and patients with lesions outside of the frontal and parietal lobes (b). Lesionboundaries were first manually delineated, and the resultant lesion masks were then averaged and overlaid on a template volume

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Results

Group differences in functional connectivitybetween the patients and the control group

Compared to the control group (Fig. 2a), the patients (Fig. 2b)showed diminished anti-correlation between the mPFC seedand bilateral dorsolateral prefrontal cortex, bilateral superiorlateral occipital cortex, left middle and inferior temporal gy-rus, and right supramarginal gyrus. Controls showed strongerconnectivity between this seed and a cluster comprising hip-pocampus, parahippocampal gyrus, and thalamus as well asbilateral dorsal prefrontal cortex as well as anterior cingulateand left pre-central gyrus. Connectivity from mPFC to leftDLPFC was correlated with lesion size (r = 0.404, p = 0.56)but this did not reach significance.

The subset of patients with non-frontoparietal lesions(Fig. 2c) showed a similar pattern as the whole patient group:an attenuated anti-correlation between activity in the mPFC

seed and areas in the bilateral dorsolateral prefrontal cortex(DLPFC), superior lateral occipital lobes, and left middle tem-poral gyrus. The control group showed a stronger connectionbetween this seed and a cluster comprising the hippocampus,parahippocampal gyrus, and thalamus.

Group differences in BOLD time–course variation

There were no significant differences in the standard deviationof the BOLD time course between non-lesion areas of thepatients and controls F = 1.32, p = 0.55.

Cortical thickness in dorsolateral prefrontal cortexin patients with non-fronto/parietal lesions

We observed reduced cortical thickness in the patients withnon-fronto/parietal lesions compared to controls in the left andright DLPFC (t = 2.56, df = 30, p = 0.016 and t = 2.84, df = 30,p = 0.008 respectively). Thickness in the left DLPFC

Fig. 2 Resting state connectivity between mPFC seed and the rest of thebrain in controls (a), AVM patients (b), and the subset of patients withnon-fronto-parietal lesions (c). Orange/red clusters are regions where ac-tivity is positively correlated with the seed region; purple/pink clusters

show regions where activity is anti-correlated with the seed region.Above is a volumetric representation of the resting state connectivity,below a surface projection of the same data

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correlated with functional connectivity between the mPFCand this region with patients with thicker cortex showinghigher degree of anti-correlation but this relationship did notreach statistical significance (r = − .506, p = .093).

Relationship between functional connectivityand cerebral blood flow

We found no relationship between cerebral blood flow (CBF)within the left and right frontal lobes and the resting stateconnectivity between the mPFC and left and right DLPFCof the patients with non fronto-parietal lesions (r = 0.138,p = 0.768, and r = − 0.172, p = 0.712).

Discussion

Patients harboring AVMs historically have been considered tohave a localized disease, butmore recent studieswith single caseshave suggested the possibility that AVMs might have a moreglobal impact on brain organization [8, 30]. In the present study,with a large cohort of patients, our results add evidence for aglobal impact on brain functional organization. This was the casewhen the whole group of AVM patients was examined, and wasalso evident even in a subgroup of patients where the AVM felloutside the affected network. The presence of an AVM interferedwith the anti-correlation known to occur between the DMN andattention control network.

The absence of DMN anti-correlation observed in ourgroup of AVM patients with lesions outside of the frontaland parietal lobes (where the most prominent nodes of theattention and default mode networks are located) supportsthe hypothesis that AVMs affect the whole brain rather thana localized region. Despite the small sample size of this sub-analysis, we found the same pattern of disrupted DMNanticorrelations as in the larger dataset. It is unlikely that theAVM in this subset of patients disrupted the connections in theDMN and between the mPFC seed and the DLPFC regionssince the lesions were located outside of the fronto-parietalareas. Of interest, those subjects with thinner cortex in a regionof the DLPFC showed a smaller degree of anti-correlationbetween this region and the mPFC, suggesting that both brainfunction and structure are affected in regions distant from theAVM. Combining information from resting state connectivityand cortical thickness, our results suggest that both structureand function are impacted by the presence of an AVM. Furtherstudies with larger sample sizes are needed to confirm ourfindings and analyses of other brain networks will also con-tribute to our understanding of this phenomenon.

Connectivity results in the subgroup of patients with le-sions outside the fronto-parietal region were found to be un-related to cerebral blood flow, suggesting that the lack of ob-served anti-correlation was not due to hemodynamic

abnormalities. The steal phenomenon is sometimes invokedto explain neurological deficits observed in AVM patients butit does not satisfactorily account for our observations. The factthat we observed disruptions in functional connectivity andcortical thickness in regions remote (contralateral) from thesite of the lesion as well as the lack of observed relationshipbetween perfusion and functional connectivity suggests that acurrent steal phenomenon is not underlying these results. Wealso did not find any differences in the overall variation BOLDtime course between non-lesion areas of the patient sampleand controls, suggesting that disruptions of cerebrovascularreactivity caused by AVMs may be limited to the vicinity ofthe lesion. That said, further exploration with more directmethods for quantifying cerebrovascular reactivity is neces-sary if we are to establish whether BOLD can be used reliablyin this population.

A number of resting-state fMRI studies have demonstratedthat the extent of the anti-correlation between DMN and at-tention networks is predictive of performance on tasks ofworking memory and attention [14, 31]. In a task-basedfMRI paradigm, Weissman and colleagues [32] found atten-tional lapses to be related to a reduced deactivation of thedefault mode network. Attenuated anti-correlation betweendefault mode network and DLPFC has been implicated in awide range of conditions characterized by impairment in at-tention and executive function: Keller and colleagues [14]found an absence of MPFC-DLPFC anticorrelations in olderadults accompanied by a reduced working memory capacityin this group. Patients with bipolar disorder and schizophreniawere also demonstrated to lack the anti-correlation betweenMPFC and DLPFC [12, 15]. In light of this research, theimplication of our resting-state connectivity result is that pa-tients with AVMs could exhibit deficits in attention and exec-utive function. Chai and colleagues [15] found the strength ofanti-correlation between DMN and attention networks to in-crease between ages 8 and 24, possibly reflecting the matura-tion of these systems. It is possible that the maturation ofDMN anticorrelations in the brain coincides with and be in-fluenced by the maturation of AVMs.

Although cognitive function was not tested directly in oursample, anecdotally, many of our patients with AVMs report alower level of functioning and occupational achievement thancomparable groups of patients with non-developmental le-sions (i.e., tumors). To date, there is limited discussion onthe role attention and executive functions may play in patientswith AVMs. The discussion is complicated by the fact thatmany of the studies investigating this question comingle pa-tients with ruptured and unruptured lesions, as well as patientswho have undergone treatment. Some evidence suggests po-tential for cognitive impairments in AVM patients. Steinvorthand colleagues observed significant improvements in generalintelligence, memory, and attention after radiotherapy, with nodifferences between patients with and without prior

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intracranial hemorrhage [33]. Lazar reported that AVM pa-tients were more likely than the comparison group (intra-ce-rebral aneurysms and low-grade tumors) to report a develop-mental learning disorder in school-age years, regardless oflesion size or occurrence of hemorrhage in adulthood [34].Taken together, these papers also suggest non-localized effectsof AVMs on brain function.

While we demonstrated an absence of the anti-correlationbetween resting state DMN and attentional networks accom-panied by differences in brain structure, we did not relate thisdirectly to behavioral performance. The lack of behavioralcorrelates to our imaging findings is the greatest limitationof the study. It is important to mention that this study wasperformed retrospectively. It is standard procedure at our hos-pital for AVM patients to be referred for this functional imag-ing protocol, but they are only referred for neuropsychologicalevaluation if they present with obvious impairments or specif-ic complaints. In order to understand the implications of theresting-state connectivity result, a future direction will be toadminister neuropsychological assessment to all patients whopresent with AVMs, regardless of whether they display a clin-ically evident impairment.

Our study demonstrates the possibility of a global disrup-tion in brain organization in this patient group, and empha-sizes the importance of further research. These preliminaryfindingsmay have important implications for the clinical man-agement of AVM patients in that we recommend that patientsundergo a thorough neuropsychological assessment, in orderto document any deficit that may reflect our data. The treat-ment decision for AVM is based on specific criteria, such asthe risk of bleeding and the presence of progressive neurolog-ical deficits. In this context, the presence of neuropsycholog-ical deficits should be taken into account, but, given the non-negligible risks related to intervention, it is unlikely that theyalone will modify treatment indications.

Conclusion

We have demonstrated that patients with AVMs, traditionallyconsidered to be localized lesions, show a wide scale disrup-tion in brain organization as reflected by the resting-state func-tional connectivity pattern. Patients showed an attenuation ofthe anti-correlation that is normally present between brainareas involved in attention and the default mode network.This result was apparent even when we looked at patients withlesions distant from the main nodes of the DMN. Connectivitydifferences were accompanied by reduced cortical thickness infrontal attention areas. Although preliminary, these findingssuggest that the impact of AVMs extend beyond the bound-aries of the lesion and could cause deficits in global cognitivefunction, which should be tested more formally and docu-mented to facilitate treatment choice.

Funding information This study was funded by an FRQS grant –Research in Radiology (Fund #240019) to Dr. Tampieri.

Compliance with ethical standards

Conflict of interest The authors declare they have no conflict of interest.

Ethics approval All procedures performed in studies involving humanparticipants were in accordance with the ethical standards of the institu-tional and/or national research committee and with the 1964 Helsinkideclaration and its later amendments or comparable ethical standards.

Informed consent Informed consent was obtained from all individualparticipants included in the study.

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