Mapping Functional Connectivity in Patients with Brain Lesions

Adrian G. Guggisberg, MD,^1,2 Susanne M. Honma, RT,¹ Anne M. Findlay, MA,¹ Sarang S. Dalal, PhD,¹ Heidi E. Kirsch, MD,^1,3Mitchel S. Berger, MD,⁴ and Srikantan S. Nagarajan, PhD¹

Objective:The spatial distribution of functional connectivity between brain areas and the disturbance introduced by focal brain lesions are poorly understood. Based on the rationale that damaged brain tissue is disconnected from the physiological interac-tions among healthy areas, this study aimed to map the functionality of brain areas according to their connectivity with other areas.

Methods: Magnetoencephalography recordings of spontaneous cortical activity during resting state were obtained from 15 consecutive patients with focal brain lesions and from 14 healthy control subjects. Neural activity in the brain was estimated using an adaptive spatial filtering technique. The mean imaginary coherence between brain voxels was then calculated as an index of functional connectivity.

Results:Imaginary coherence was greatest in the alpha frequency range corresponding to the human cortical idling rhythm. In healthy subjects, functionally critical brain areas such as the somatosensory and language cortices had the highest alpha coher-ence. When compared with healthy control subjects, all lesion patients had diffuse or scattered brain areas with decreased alpha coherence. Patients with lesion-induced neurological deficits displayed decreased connectivity estimates in the corresponding brain area compared with intact contralateral regions. In tumor patients without preoperative neurological deficits, brain areas showing decreased coherence could be surgically resected without the occurrence of postoperative deficits.

Interpretation:Resting state coherence measured with magnetoencephalography is capable of mapping the functional connec-tivity of the brain, and can therefore offer valuable information for use in planning resective surgeries in patients with brain lesions, as well as investigations into structural-functional relationships in healthy subjects.

Ann Neurol 2008;63:193–203

The ability to map the functions of discrete brain re-gions has led to unprecedented advances in the under-standing of brain physiology and pathology. In addi-tion to the enormous utility of cortical maps in cognitive and behavioral research, they are routinely used in the clinical management of patients. In partic-ular, functional maps of individual patient brains are used during surgical planning, to preserve critical brain tissue¹ and avoid disabling postsurgical functional def-icits. Intraoperative maps based on direct cortical stim-ulation mapping² are highly reliable but have the dis-advantage of being invasive. Functional maps created with noninvasive techniques such as functional mag-netic resonance imaging (fMRI)^3,4 or magnetic source imaging (MSI)^{5– 8}generally correlate well with intraop-erative findings, but their accuracy can depend on pa-tient cooperation and on study paradigms that are ca-pable of reliably activating the brain area of interest. In particular, paradigms for activating language areas are

currently not generally available for clinical functional imaging.

Instead of mapping brain areas using activation or lesion methods, this study aimed to map brain areas by measuring their functionalconnectivitywith other areas.

The underlying rationale is that necrotic or structurally disconnected (ie, nonfunctional) tissue does not partic-ipate in the dynamic interactions occurring between in-tact brain areas and would therefore be expected to show decreased connectivity. Moreover, it is reasonable to speculate that such decreased connectivity might be observed during the resting state and during cognitive tasks. Thus, measurement of functional connectivity of each brain area with sufficiently high spatial resolution, even if done during the resting state, might yield valu-able information about their functionality, and such resting state measurements could be made without de-pendence on patient cooperation or specific activation paradigms. Indeed, a pioneering study assessing

func-From the ¹Biomagnetic Imaging Lab, Department of Radiology, University of California San Francisco, San Francisco, CA;² Depart-ment of Neurology, University of Berne, Inselspital, Bern, Switzer-land; and Departments of ³Neurology and ⁴Neurological Surgery, University of California San Francisco, San Francisco, CA.

Received Jun 13, 2007, and in revised form Jul 24. Accepted for publication Aug 3, 2007.

Published online Sep 25, 2007, in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/ana.21224

Address correspondence to Drs Nagarajan and Guggisberg, Biomag-netic Imaging Lab, Department of Radiology, University of Califor-nia San Francisco (UCSF), 513 Parnassus Avenue, S-362, San Francisco, CA 94143-0628.

E-mail: sri@radiology.ucsf.edu, aguggis@gmail.com

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tional connectivity in patients with brain lesions using electroencephalographic (EEG) coherence found highly significant decreases in EEG coherence in lesion patients during the resting state, and these decreases were most pronounced in the alpha frequency range corresponding to the human idling rhythm.⁹ Furthermore, studies us-ing electrocorticographic coherence demonstrated in-creased short-range coherence between electrodes record-ing directly from brain lesions, but decreased long-range coherence between tumor and intact tissue.¹⁰

However, previous noninvasive EEG- and magne-toencephalography (MEG)-based investigations of the functional connectivity of brain lesions were unable to relate changes measured by extracerebral sensors to structural alterations within the brain, and fMRI-based connectivity studies were insensitive to focal cerebral lesions.¹¹ Therefore, the spatial distribution of lesion-induced connectivity changes remains poorly under-stood. Furthermore, the available data appear to be conflicting. Whereas EEG and electrocorticographic studies showed changes restricted to the affected hemi-sphere⁹ or to the electrodes overlying tumor,¹⁰ MEG studies found that tumors produce diffuse, not focal, decreases in functional connectivity.^12,13

Here, we introduce an MEG-based approach that al-lows a precise relation of changes in functional connec-tivity to structural lesions. It first uses a well-established adaptive spatial filtering technique^{14 –19} to estimate the neural oscillations at each volume element (voxel) in the brain from the signals recorded by the whole MEG sensor array. Then, imaginary coherence (IC) is calculated and used as index of the functional connectivity between voxels that is insensitive to vol-ume conduction and sensor crosstalk.²⁰

Subjects and Methods

Patients and Healthy Control Subjects

All 15 patients with unilateral brain lesions who underwent MEG scanning and MSI at the University of California San Francisco (UCSF) Biomagnetic Imaging Lab between Febru-ary 2006 and FebruFebru-ary 2007 and who had a subsequent cra-niotomy with biopsy and/or resection at UCSF were in-cluded in this retrospective study. One further patient was excluded because of the presence of bilateral lesions. The mean age of the patients was 39.6 (range, 20 –74) years, and 9 were women. In addition, 14 healthy control subjects (mean age, 42; range, 25–75 years; 5 women) were studied.

All participants gave their written informed consent to partic-ipate in the experiments, all procedures were approved by the UCSF Committee on Human Research, and all experiments were conducted according to the Declaration of Helsinki.

Structural Images

Magnetic resonance imaging (MRI) was performed at 1.5 Tesla. The protocol typically included the following sequenc-es: (1) a T1-weighted, three-dimensional spoiled gradient-recalled echo in a steady-state sequence with TR of 34

mil-liseconds, TE of 3 to 8 milmil-liseconds, and flip angle of 30 degrees; and (2) a T2-weighted, three-dimensional fast spin-echo sequence with TR of 3,000 milliseconds and TE of 105 milliseconds. Both sequences had a slice thickness of 1.5mm, matrix 256⫻256⫻(108 –140), and a field view of 260⫻ 260mm with skin-to-skin coverage to include the nasion and preauricular points. Tumor volumes were estimated by re-sampling the MRI volumes into a 1 ⫻ 1 ⫻ 1mm voxel space, delineating the lesions (excluding edema) in three planes, and calculating the number of voxels included. In ad-dition, voxels that were located within 2cm of a tumor or re-lated edema were manually segmented and defined as “tumor voxels.”

Magnetoencephalographic Recordings

The participants were laying awake and with their eyes closed in a magnetically shielded room while their continuous resting state MEG was recorded with a 275-channel whole-head CTF Omega 2000 system (VSM MedTech, Coquitlam, British Co-lumbia, Canada), using a sampling rate of 600Hz. An artifact-free epoch of 1-minute duration was selected for subsequent analysis in each patient and subject. These parameters were found to provide the maximum amount of data within the memory capacity limits of our hardware.

General Algorithms

An adaptive spatial filter (“beamformer”) was used to recon-struct the electromagnetic neural activity at each brain voxel from the signal recorded by the entire MEG sensor array.

The detailed algorithms for this technique are described else-where.^15–19 In brief, the raw MEG data were bandpass fil-tered with a fourth-order Butterworth filter, and the spatial covariance matrix was calculated for the entire recording of 1 minute in duration. We also computed the lead-field matrix, corresponding to the forward solution for a unit dipole at a particular location, for each voxel of interest in the brain.

From the spatial covariance and the forward field matrix, a spatial weight matrix was then obtained for optimal estima-tion of the signal power in each voxel. The activity at each time in each voxel was calculated as the linear combination of the spatial weighting matrix with the sensor data matrix.

Thus, all sensors contribute to some degree to all voxel time series estimates from which we analyze functional connectiv-ity. Most of the commonly used measures of functional con-nectivity such as coherence,²¹ phase locking value,^22,23 and synchronization likelihood²⁴ overestimate the magnitude of true connectivity in this setting because of common refer-ences and crosstalk between voxels (see Fig 1), a problem that is also well known in connectivity estimation from EEG time series.²⁵ In addition, traditional approaches to func-tional connectivity are sensitive to volume conduction²⁶and spatial blur in reconstruction.

Recently, an alternative method for estimating functional connectivity was introduced that overcomes the overestima-tion biases arising from crosstalk or volume conducoverestima-tion.²⁰IC exploits the fact that phase similarities among time series arising from a common reference or volume conduction oc-cur with zero time delay. Thus, by omitting the real compo-nent of coherence, which mostly contains similarities with zero time lag, we remove suspect associations and limit the analysis

194 Annals of Neurology Vol 63 No 2 February 2008

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to the imaginary component of coherence, which represents true interactions between brain areas occurring with a certain time lag. Figure 1 shows that IC effectively eliminates overes-timation biases in deep brain areas inherent to traditional co-herence, and that it has a functionally plausible spatial distri-bution. IC,I, between a given voxel pair for each frequency binfwas calculated according to the following formula²⁰:

I_XY共f兲⫽

|

^Im

_冑

^k⫽1

^冘

^K^k⫽1

^冘

^|X^K^k^X^共^k^f^共^兲^f^|²^兲^k⫽1

^冘

^Y*^K^k^|Y^共^f^k^兲^共^f^兲^|²

|

⁽¹⁾

where X_k and Y_k are Hanning-windowed, Fourier-transformed segments of two virtual time series, and * indi-cates the complex conjugate. Each segmentk was 2 seconds long and overlapped adjacent segments by 1 second. The ab-solute value of the overall IC across segments was used rather than coherency because we were interested in the magnitude of connectivity at each voxel rather than in the directionality of the information flow.²⁰ IC values for frequency bands were calculated from the sum of autospectra and cross-spectra of the corresponding frequency bins.

Estimates of functional connectivity can also be obtained with the previously introduced dynamic imaging of coherent sources (DICS)algorithm²⁷by examining the imaginary part of the cross-spectrum in voxel coordinates. Unlike the pro-posed method, dynamic imaging of coherent sources uses correlations in the frequency domain for source estimation instead of temporal correlations. It first calculates a cross-spectrum density matrix of all physical MEG sensors, and then applies an adaptive spatial filter to reconstruct source power and coherence for a given frequency bin or band. It can be shown that the resulting estimates are computation-ally equivalent.

Adaptive spatial filters are based on the assumption that the reconstructed sources are independent from each other, and the accuracy of spatial reconstructions and of coherence estimates reported here therefore depends on the absence of highly correlated sources. Previous studies have demonstrated that, given a sufficient signal-to-noise ratio, significant errors occur only when reconstructing sources that have a correla-tion coefficient greater than approximately 0.85^14,17,27 and that are highly correlated during more than 40% of the anal-ysis time window,²⁸which is rarely the case in recordings of spontaneous cerebral activity of several seconds to minutes in duration. Furthermore, this study focused on regions with low coherence, which are unaffected by this potential limi-tation.

Functional Images

A three-dimensional grid of voxels covering the entire brain was created for each subject and recording, based on a mul-tisphere head model of co-registered structural MRI scans.

Alignment of structural and functional images was ensured by marking three prominent anatomical points (nasion and preauricular points) of the subject’s head in the MRI images and localizing three fiducials attached to the same points be-fore and after each MEG scan.

The selection of bandpass filter settings (ie, of the fre-quency band used for reconstruction of source time series) is a compromise between providing sufficient bandwidth for stability of the spatial filter whereas optimizing the obtained weighting matrix for a narrow band of interest. At first, a broad 1- to 55Hz band was used for calculation of the spec-tral distribution of IC magnitude. A narrower band of 1 to 20Hz was chosen for calculation of IC in the alpha band.

After calculating the time series at each voxel, pairs of vox-els were subjected to analysis of IC as described earlier. For the creation of IC spectra, frequency bins between 1 and 55Hz were analyzed, using a resolution of 0.59Hz (1,024 frequency bins). For analysis of idling rhythms, alpha fre-quency bins that showed greatest power density during rest-Fig 1. Spatial distribution of average coherence (A) and

imag-inary coherence (B) magnitude in the alpha frequency band of all 14 healthy control subjects. Coherence overestimates connec-tivity, especially in deep brain regions, because of sensor crosstalk and volume conduction. Images are presented in neu-rological convention.

Guggisberg et al: Functional Connectivity Maps 195

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ing state were chosen individually for each subject and aver-aged. A frequency resolution of 1.17Hz (512 frequency bins) was used.

The connectivity,I៮,at each voxel of interest was estimated by averaging across all its Fisher’s Z-transformed connec-tions,C.²⁰

I៮⫽tanh

冉

^C¹

^冘

^C ^tanh^⫺1^共^I^兲

冊

⁽²⁾

Maps of lesion-specific connectivity changes (L-images)were ob-tained by analyzing all connections between “tumor voxels”

(see earlier definition) of 8 ⫻ 8 ⫻ 8mm extension and a centered, equally spaced, whole-brain grid of each fourth voxel within the entire set of 8⫻8⫻8mm voxels (approx-imately 10,000 –100,000 voxel pairs in total, depending on the individual head and tumor size). As a control, the con-nectivityI៮was also calculated for connections between voxels contralateral to tumor voxels and the same whole-brain voxel grid:

I៮共Patient_Tumor,Patient_WholeBrain兲

NI៮共PatientContralateral,PatientWholeBrain兲 (3)

Maps of patient-specific connectivity changes (P-images) were obtained by analyzing all possible connections between 2 voxels of 20⫻ 20 ⫻ 20mm extension for each tumor pa-tient and, as a control, for each healthy subject (approxi-mately 30,000 – 60,000 voxel pairs in total, depending on the individual head size):

I៮共PatientWholeBrain兲NI៮共HealthyControlsWholeBrain兲 (4)

The computation time of the maps could be reduced to about 10 minutes by distributing the processing of batches of voxel pairs to a cluster of 10 Linux workstations.

Three-dimensional renderings of the maps were created with the freely available programs BET²⁹ (http://fsl.fmrib.

ox.ac.uk/fsl/bet2/) and mri3dX (http://www.aston.ac.uk/lhs/

staff/singhkd/mri3dX/mri3dX.jsp).

Clinical Analyses

MSI of the somatosensory and auditory cortices was per-formed for all tumor patients according to standard proce-dures.^8,30 After the MEG scans, all patients underwent cra-niotomy. In 10 patients, intraoperative cortical mapping of the motor cortex, language areas, or both, was performed, depending on the tumor location. Subcortical stimulation was sometimes used to identify the corona radiata and the internal capsule at the border of the resection.³¹Biopsy sam-ples were always obtained. In 14 patients, neoplasms were histologically confirmed and resected whereas avoiding criti-cal tissue. Neurologicriti-cal deficits before and after surgery were assessed by qualified physicians who were blinded to the MEG data.

Statistical Analyses and Classifications

The mean Z-transformed IC values of all alpha frequency bins between 8 and 12Hz were tested for across-subject dif-ferences between tumor voxels and nontumor voxels of pa-tients with brain tumors using two-tailed pairedt tests, and between tumor voxels and voxels of healthy control subjects using two-tailed unpairedttests. In accordance with the hy-pothesis that damaged brain tissue is associated with lower values of functional connectivity, only patients without resection-related postsurgical functional deficits, that is, with-out relevant functional tissue in vicinity of the tumor, were included for this analysis step (n⫽10).

L-images were used to assess tumor voxels of all patients for within-subject differences with contralateral voxels using two-tailed t tests for one sample, which tested the null hy-pothesis that the Z-transformed connectivity I៮ between a given tumor voxel and reference voxels is equal to the mean of theZ-transformed connectivities between all contralateral voxels and the same reference voxels. The resulting probabil-ities can be corrected easily for multiple testing by using a false discovery rate. However, because the uncorrected maps proved to be conservative even in the absence of corrections (ie, no brain area was erroneously mapped as nonfunctional while containing critical tissue), we report here the uncor-rected images.

L-imagesof all patients were classified into three grades of tumor tissue disconnection according to the percentage of tumor voxels that showed significant increases or decreases in connectivity as compared with the contralateral tissue: grade 2 ⫽ ⬎50% of the tumor voxels show significant decrease and 0% show significant increase; grade 1 ⫽ 20 to 50%

show decrease and 0% show increase; grade 0 ⫽ ⬍20%

show decrease or⬎0% show increase. The tumors were clas-sified as invading or being located close to eloquent tissue if at least one of the following criteria was fulfilled: MSI equiv-alent current dipoles located within 5mm of the tumor, neu-rological deficits induced by electrical stimulation over the tumor during intraoperative cortical mapping, or increased neurological deficits after surgery (some of these neurological deficits were transient only and probably caused by swelling but were also considered as a sign of eloquent tissue in vi-cinity of the tumor). Disconnection grades were then tested for significant associations with the presence or absence of eloquent tissue in vicinity of the tumor, and with the risk for postsurgical deficits using Fisher–Freeman–Halton tests.

To testP-imagevoxels of all 15 tumor patients for differ-ences with healthy control subjects, we spatially normalized them to the canonical Montreal Neurological Institute space according to the co-registered structural MRI, using the tool-box Statistical Parametric Mapping 2 (SPM2) for MATLAB (http://www.fil.ion.ucl.ac.uk/spm/software/spm2/). All nor-malizedP-imagevoxels of tumor patients withZ-transformed connectivity estimates greater than or less than the 95% con-fidence interval of the values of healthy control subjects were considered significant. The proportion of “disconnected

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