- Email: [email protected]
Identifying confounds to increase specificity during a “no task condition”
NeuroImage 20 (2003) 1236 –1245
www.elsevier.com/locate/ynimg
Identifying confounds to increase specificity during a “no task condition” Evidence for hippocampal connectivity using fMRI S.A.R.B. Rombouts,a,b,e,* C.J. Stam,c,e J.P.A. Kuijer,a Ph. Scheltens,b,e and F. Barkhof d,e a
Department of Physics and Medical Technology, VU Medical Center, Amsterdam, The Netherlands b Department of Neurology, VU Medical Center, Amsterdam, The Netherlands c Department of Clinical Neurophysiology, VU Medical Center, Amsterdam, The Netherlands d Department of Radiology, VU Medical Center, Amsterdam, The Netherlands e Alzheimer Center, VU Medical Center, Amsterdam, The Netherlands Received 24 February 2003; revised 22 May 2003; accepted 23 June 2003
Abstract Functional MRI can be applied to study connectivity in the brain during a “no task condition.” This study focuses on applying a multiple linear regression analysis to identify spurious connectivity caused by confounding factors such as physiologic noise and to separate these from hippocampal connectivity caused by the blood oxygen level dependent (BOLD) signal during a no-task condition. Regressors of interest (hippocampal time courses) as well as regressors of no interest (respiratory signal and cerebrospinal fluid), were included in the analysis, and each yielded a connectivity map. This method was applied at high sampling rate (limited volume, proper physiologic noise sampling), low sampling rate (whole brain scans possible), and at high and low spatial resolution in five healthy control subjects. Regressors of no interest showed specific connectivity patterns, different from hippocampal regressors. The latter showed connectivity between left and right hippocampus. The current study shows successful application of a multiple regression analysis to study connectivity between left and right hippocampus. Both maps of hippocampal connectivity caused by BOLD signal and connectivity caused by spurious signals could be identified. © 2003 Elsevier Inc. All rights reserved.
Introduction Functional connectivity in the analysis of neuroimaging time-series has been defined as “temporal correlations between spatially remote neurophysiological events” (Friston et al., 1993). Functional connectivity in the human brain has found application in studies where subjects were engaged in a specific behavior (see, for example, Maguire et al., 2000; Herbster et al., 1996; Buchel and Friston, 1997; Grady et al., 2001). However, such task-driven studies may show coactivations resulting from the task itself and are therefore strongly dependent on the task and the specific instructions * Corresponding author. Department of Physics and Medical Technology, VU Medical Center, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands. Fax: ⫹31-20-444-4147. E-mail address: [email protected] (S.A.R.B. Rombouts). 1053-8119/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/S1053-8119(03)00386-0
to the subject. Functional connectivity without any specific behavior of a subject, defined as no-task condition connectivity (NTCC), would be a more direct measure of functional connectivity, independent of tasks and instructions. This has been studied using electroencephalography (EEG) (for example, Stam et al., 2002) and also positron emission tomography (PET) where transcranial magnetic stimulation and PET were combined to assess NTCC, finding evidence for connectivity of the human visual system in agreement with the known anatomic connectivity in monkeys (Paus et al., 1997). Besides the interest in functional connectivity from a neuroscientific point of view in healthy controls, NTCC methods in patients would also be of clinical interest. In patients one often encounters difficulties to apply a (cognitive) paradigm, since the functions under study can be (partly) destroyed by the disease process. NTCC methods
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1237
Fig. 1. Picture illustrating the location of the manually drawn regions of interest (ROIs) in subject 1 for experiment 1 (high temporal resolution, limited spatial coverage) on the subject’s structural images. Left in picture is right in the brain. Seven ROIs were used: the right hippocampus on each of the three slices (shown in red), cerebral white matter (green), cerebellar white matter (blue), CSF (yellow), and the signal outside the brain (cyan).
would offer a practical alternative to these task driven studies. In recent years, functional magnetic resonance imaging (fMRI) using blood oxygen level dependent (BOLD) contrast has also been applied to study NTCC. Functional MRI is not invasive, has a spatial resolution higher than EEG and is a relatively fast imaging technique, allowing the acquisition of hundreds of whole brain scans in only a few minutes. Therefore, fMRI might be well suited to study NTCC. The first NTCC fMRI study showed connectivity of low frequency fluctuations of the primary motor cortices (Biswal et al., 1995), and was followed by others showing NTCC between motor cortices (Cordes et al., 2000; Lowe et al., 1998), visual cortex (Cordes et al., 2000; Lowe et al., 1998), and amygdala (Lowe et al., 1998), regions of language (Cordes et al., 2000; Hampson et al., 2002), and also be-
Fig. 2. Drawing illustrating how regions of interest (ROIs) as drawn in the high resolution structural image are transformed to the low resolution fMRI image for further analysis. The left represents a 6 ⫻ 6 pixel square of the structural image, the right the corresponding part of the fMRI image (2 ⫻ 2 pixel square). Black squares represent the pixels contained in an ROI; white pixels are not contained in the ROI. The ROI in the structural image has to be sampled down to the spatial resolution of the fMRI image. Only voxels in the fMRI image that are covered for more than 40% by the original structural ROI are then used for further analyses. For example, the left bottom voxel in the fMRI image was covered by the original ROI for 67% and therefore is also contained in the fMRI image ROI. The top left and bottom right voxel is not contained in the fMRI image ROI, since only have 11% and 33% coverage.
tween thalamus and hippocampus (Stein et al., 2000). It has also been shown that regions with NTCC were functionally connected during performance of a behavioral task (Cordes et al., 2000; Hampson et al., 2002). Furthermore, in neurological disorders, NTCC measures were decreased, both in multiple sclerosis in the motor cortices (Lowe et al., 2002) and in Alzheimer’s disease within the hippocampi (Li et al., 2002). In most NTCC fMRI studies reported so far, three important steps are part of the data analysis: (1) low pass temporal filtering of the BOLD signal, necessary to remove physiologic noise from the data caused by the cardiac (⬃0.9 Hz) and the respiratory (⬃0.3 Hz) cycle; (2) selection of regions of interest (ROIs); and (3) cross-correlation analysis between time courses in the ROI and all other pixels in the scanned volume. Despite precautions such as filtering of the data, spurious correlations between time courses not caused by BOLD signal may occur. These can be caused by pulsations, motion, and CSF signal. The main goal of the current study was to apply a multiple regression analysis to identify correlations maps of these spurious signals and to identify BOLD connectivity. For this purpose, we focused on connectivity between the hippocampi since there is great interest in studying the functions of this structure and its functional network in studies of memory and dementia.
Materials and methods Experiment 1: high temporal resolution, limited spatial coverage Subjects Resting state data were obtained in three right-handed subjects (subjects 1–3, 1 male, 2 female; ages 26, 26 and 33 years) free from medication and neurologic and psychiatric complaints.
1238
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 3. Example of “no task” connectivity pattern (unclustered) in subject 1 of the respiration signal (A), and CSF signal (B), projected on the structural scan. Left in the image is right in the brain. The regressor in (A) was the recorded respiratory cycle, in (B) the average CSF signal in the CSF ROI indicated by the arrow. Clearly, there is strong interregional correlation of CSF spaces throughout the slices. Note that by including respiration and CSF regressors in the analysis, the variances explained by these time courses in the regions shown in this figure, are removed from the data and cannot cause spurious correlations between these regions and other regressors in the data analysis.
MRI scanning Imaging was performed on a 1.5-T Sonata scanner (Siemens, Erlangen, Germany) using the standard circularly polarized head coil. Foam pads were used to minimize head motion and subjects wore earplugs to reduce the scanner noise. For functional imaging T*2-weighted single shot echo planar imaging (EPI: TR ⫽ 300 ms; TE ⫽ 60 ms; flip angle ⫽ 45°, receiver bandwidth 890 Hz per pixel; field of view ⫽ 192 ⫻ 192 mm; matrix size ⫽ 64 ⫻ 64; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm) was applied. Three slices were placed perpendicular to the long axis of the hippocampus covering the middle part of the hippocampus. For this 17-mm-thick volume, 1029 scans were acquired in 5 min and 9 s. For anatomical reference a structural scan using a T1-weighted sequence was obtained with exactly the same slice position (either a 3D gradient echo with TR ⫽ 2700 ms; TE ⫽ 3.54 ms, flip angle ⫽ 8°, inversion time ⫽
950 ms; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm, or a 2D spin echo with TR ⫽ 595 ms, TE ⫽ 14 ms, flip angle ⫽ 90°; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm). Subjects were instructed to lie awake with their eyes closed during scanning and to refrain from any cognitive, language, or motor tasks as much as possible. Acquisition of cardiac and respiratory signal The respiratory signal was acquired with a flexible pressure belt placed around the upper abdomen of the subject, and the cardiac signal was acquired with a pulse oximeter placed on the middle finger of the subject. The latter signal provides a delayed systolic signal and also includes the oxygenation saturation levels. These signals were sampled with 50 and 200 Hz, respectively, and stored on a computer.
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1239
in each hippocampal region (that is, each slice, giving three hippocampal time courses), and average cerebral and cerebellar white matter, CSF, and background noise. The other regressors were the “time courses” of the six realignment parameters (that is, three translational and three rotational parameters) and the respiratory movement signal registered during scanning. This respiration signal closely resembles the effect of respiration on imaging data (Hu et al., 1995). However, the oximeter signal has a completely different shape than the effect of heart rate on imaging data (Hu et al., 1995). Therefore the oximeter signal was not included in the regression analysis. The respiratory movement signal (50 Hz) was resampled to match the sample rate of the fMRI data. Hence 14 regressors in total were used. This resulted in the following model for the data:
冘 *R 共t兲 11
y共t兲 ⫽ c ⫹ d ⴱ t ⫹
k
k
k⫽1
冘  ⴱ Hippo 共t兲 ⫹ e 14
⫹ Fig. 4. Example of connectivity pattern of white matter (A), white matter in cerebellum (B) in subject 1 projected on the structural scan. Left in image is right in brain. Signal time courses of the colored voxels within the black circle were averaged and used as regressors. There were no clustered regions with connectivity with any of these regressors.
Data analysis Data were analyzed using AFNI 2.5 (Cox, 1996). The first 5 scans were discarded to account for spin saturation effects, leaving 1024 scans for data analysis. EPI scans were realigned using the first volume as a reference, using standard rigid body realignment, and no slice timing correction or spatial smoothing was applied. In the frequency domain, a low pass filter was applied with a cutoff frequency of 0.4 Hz to remove physiologic noise caused by the cardiac cycle (⬃0.8 –1 Hz). Next, on the structural scans the right hippocampus was outlined manually on each of the three slices. Additionally, cerebral regions containing only white matter, cerebellar white matter, CSF, and the signal of scanned regions outside the brain (background noise) were also drawn (Fig. 1). Given the much lower spatial resolution of the fMRI scans compared to the structural images and the outlined regions interest, the ROIs had to be sampled down to the spatial resolution of the fMRI images. Only voxels in fMRI images that were covered for more than 40% by the original structural ROI were used for further analyses (Fig. 2). Of these voxels the signal time courses were averaged. Markers such as brain boundaries, ventricle edges, and so on, and also localization of ROIs were visually inspected for correct registration between structural and fMRI images. Next, time courses from the seven ROIs were used in a multiple linear regression model: the average time courses
k
k
k⫽12
where y(t) is the data (time course of each voxel), t is time, and c and d are constants, and e is the error term. Rk(t) are the 11 regressors other than the hippocampal signal, Hippok(t) are the three hippocampal regressors, and k are the parameter estimates for the regressors. This model was fitted to the data, resulting in estimations of k and corresponding standard error for each voxel, of which the corresponding statistical partial T map was calculated (for further details on multiple regression analysis in the context of fMRI data, see Smith, 2001; Worsley, 2001). Further, a partial R2 map was obtained, representing the proportional reduction of the variation in the data explained by each regressor (R2 can be considered a generalization of the square of a correlation coefficient). The regressors representing the covariates of no interest (i.e., realignment parameters, respiratory signal and CSF) were included to remove these components from the signal and to decrease the error term in the results. The main effects of interest were the partial correlations of the three hippocampal regressors (that is, the average right hippocampal time course per slice). Cerebral white matter, cerebellar white matter, and background signal were included as regressors to serve as internal control, since no partial correlation in any region is expected for these regressors. Partial correlation maps were thresholded at T ⫽ 5 and partial correlation maps of the regressors of the regions of interest (i.e., hippocampus) and internal control regions were additionally clustered, limiting the minimal cluster size to 200 mm3 [giving P ⬍ 0.05 corrected (Forman et al., 1995)].
1240
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 5. Examples of “no task condition connectivity” between left and right hippocampus in subject 1 (A), 2 (B), and 3 (C) projected on their structural scans (A and C: gradient echo, B: spin echo; left in the images is right in the brain). Each image group (that is, A-I, A-II, B-I, B-II, and C) corresponds to the connectivity map of one single regressor: the arrows indicate the voxels that were averaged to obtain a regressor of the right hippocampus. Of each slice, one hippocampal regressor was obtained, giving three in total. Correlations between hippocampi are found in the same slice (A-I, A-II, B-I, the middle image in B-II, and right image in C). Correlation is also found with the hippocampal signal in one of the other slices, as illustrated in subject 2 (B-II, left and right image) and subject 3 (C, left image).
Experiment 2: increased in-plane spatial resolution In subject 1, an EPI sequence with a higher spatial resolution, allowing ROI definition on the functional EPI scan, was also used in a separate experiment. A T*2 EPI sequence was used (TR ⫽ 327 ms; TE ⫽ 60 ms; flip angle ⫽ 45°; receiver bandwidth 1052 Hz per pixel; field of view ⫽ 192 ⫻ 192 mm; matrix size ⫽ 96 ⫻ 128; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm), placed perpendicular to the long axis of the hippocampus covering the middle part of the hippocampus. Of this 17-mm-thick volume, 1029 scans were acquired in 5 min and 36 s. Note that due to the increased spatial resolution, scan time was increased slightly compared to experiment 1. Data analysis was the
same as described in experiment 1, except for the minimal cluster size of the partial correlation maps of the hippocampal regressors, which was decreased to 50 mm3 [P ⬍ 0.05 corrected (Forman et al., 1995)]. Experiment 3: whole brain coverage at low temporal resolution In a third experiment, subject 1 of experiment 1 was scanned again in a separate session, and two new righthanded subjects [subjects 4 and 5: 2 females (age 33 and 34 years)] were included. EPI scans were obtained with the same voxel sizes as in experiment 1, yet the volume was extended to 24 slices (EPI: TR ⫽ 2400 ms s; TE ⫽ 60 ms;
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245 Table 1 Multiple regression analysis of a signal time course results in one partial R2 value for each regressor included in the analysisa Subject3 Regressors2
1: STR 2: STR 3: STR 1: HR 1: LTR 4: LTR 5: LTR
Average motion Respiration Cerebellum WM CSF Hippocampus Full model
0.006 0.009 0.035 0.012 0.003 0.136 0.434
0.008 0.000 0.000 0.000 0.007 0.045 0.162
0.004 0.000 0.009 0.018 0.000 0.095 0.276
0.025 0.005 0.010 0.002 0.013 0.050 0.180
0.042
0.009
0.010
0.003 0.002 0.008 0.224 0.459
0.006 0.050 0.005 0.310 0.690
0.010 0.020 0.006 0.170 0.380
a The table shows for each individual the R2s of one left-sided hippocampal time course that has the most significant connectivity with one of the hippocampal regressors. The “full model” R2 represents the proportion of the variation in the data that is explained by the full regression model. The partial R2 of each regressor is the proportional reduction of the variation in the data that is explained by adding the regressor to the model. Note that these R2 values are not of the whole hippocampal regions, but only represent the most significant hippocampal voxels. STR ⫽ short TR; HR ⫽ high resolution; LTR ⫽ long TR.
flip angle ⫽ 90°, receiver bandwidth 890 Hz per pixel); 517 scans were acquired in 20 min and 41 s. For anatomical reference, a 2D spin echo (TR ⫽ 595 ms, TE ⫽ 14 ms, flip angle ⫽ 90°; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm) was used. In experiment 3, a low pass filter was applied in the frequency domain with a cutoff frequency of 0.1 Hz. The band pass we applied is similar to other studies applying a long TR (Lowe et al., 1998; Lowe et al., 2002; Li et al., 2000; Hampson et al., 2002). Note also that frequency components of both of the cardiac and the respiratory signal will be aliased in the passband. Other data analyses parameters were the same as in experiment 1, except that more hippocampal ROIs could be determined: in each subject, five hippocampal ROIs were determined, and their average signal of each ROI included in the analysis, yielding five hippocampal regressors.
1241
control regions where the signal was not expected to have connectivity with other regions (cerebral and cerebellar white matter, background noise) did indeed not show a clear connectivity with any other regions (Fig. 4). The regressors of interest on the other hand (i.e., the three right hippocampal ROIs in each subject) showed definite connectivity with the left hippocampus in all three subjects (Fig. 5). Note that this hippocampal connectivity pattern is “corrected for” spurious signals of no interest as shown in Fig. 3 (i.e., those components were removed from the signal). Table 1, provided to give an impression of the R2 values in this study, shows that the variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 16 – 43% in the three subjects (that is, R2 values of 0.16 – 0.43, Table 1). Regressors of no interest (CSF, respiration, motion) explained variance in the hippocampal signal for less than 1%, while the hippocampal regressor explained variance for 5–14% (Table 1). The dominant frequency of correlated hippocampal time courses indicated slow wave oscillations (0.03– 0.08 Hz, example of one subject shown in Fig. 6). Experiment 2: short TR, increased inplane spatial resolution Although the results of connectivity are noisier due to the decreased signal-to-noise ratio in the higher resolution scans, the results of experiment 1 were repeated: CSF signal showed strong interregional connectivity, while some regions’ time course variances were explained by the respiratory signal. Cerebral and cerebellar white matter did not show any connectivity.
Results Experiment 1 The frequency of the cardiac signal was between 0.9 and 1.2 Hz in the three subjects. Given our sample rate (1/TR) of 3.33 Hz, the cardiac signal therefore was not undersampled and we are confident it has been appropriately filtered from of the data. The respiration frequency was between 0.25 and 0.33 Hz. Variance in some regions could be explained by the respiratory signal (Fig. 3a), while the partial correlation map of the CSF regressor shows a clear connectivity of CSF signal throughout the scanned volume (Fig. 3b). Internal
Fig. 6. Example of frequency power spectrum in subject 1 (short TR experiment) of the average signal of voxels in the left hippocampus with a significant correlation with any hippocampal regressor. The voxels showed significant connectivity as determined in a multiple regression analysis after low pass filtering the data at 0.4 Hz. The voxels show peak frequencies at 0.03 Hz, indicating slow wave oscillations are the dominant frequencies in the time courses. Also in other subjects the peak of the power frequency of hippocampal signal with a correlation appeared to be in the range of 0.01– 0.08 Hz.
1242
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 7. Partial connectivity maps in subject 1 with increased spatial resolution of the EPI functional scan, shown on the first EPI scan. Left in the images is right in the brain. This experiment was performed to check whether the connectivity between the hippocampi as seen in experiment 1 (see Fig. 3A), were really located in the hippocampus, as seen on the EPI scan. (A–B) Partial connectivity map of the three hippocampal ROI regressors. The arrows denote the region used for averaging to obtain the regressor. Although the connectivity pattern is noisier than in experiment 1 due to decreased signal to noise ratio and possibly due to increased number of false positives, connectivity between the left and right hippocampus is still clearly visible.
Most importantly, in each slice, independent connectivity between left and right hippocampus could be detected (Fig. 7). The variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 18% (Table 1). Regressors of no interest (CSF, respiration, motion) explained variance in the hippocampal signal for less than 1%, while the hippocampal regressor explained variance for 5% (Table 1). Again, dominant frequencies of hippocampal signal with left right correlations indicated slow wave oscillations (0.02– 0.06 Hz).
three subjects again resting state connectivity between the hippocampi could be detected (Figs. 8 –10). The variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 38 – 69% in the three subjects (Table 1). Regressors of no interest (CSF, motion) explained variance in the hippocampal signal for less than 1% to 4 %, while the hippocampal regressor explained variance for 17–31% (Table 1). Dominant frequencies in hippocampal regions of left-right correlations showed dominant frequencies in the range of 0.01– 0.06 Hz.
Experiment 3: long TR, low spatial resolution Discussion The partial correlation map of CSF again results in a clear connectivity map of CSF signal throughout the scanned volume. Internal control regions where the signal was not expected to have connectivity with other regions (cerebral and cerebellar white matter, background noise) did indeed not show a clear connectivity with any other regions. Despite the longer TR and therefore the expected increased presence of physiological noise in the data, in all
In this study we address the specificity of “no task condition connectivity” (NTCC) in BOLD fMRI time series by using a multiple linear regression approach. The advantage of this approach, as opposed to other methods used for NTCC, is that in one analysis different regressors, both those of interest and those of no interest, are included and each gives a partial correlation map showing variance that is
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1243
Fig. 8. No task condition connectivity in subject 1 using a long TR (TR ⫽ 2400 ms). Left in the images is right in the brain. Despite the long TR resulting probably in increased physiological noise, connectivity both within the right and between the left and right hippocampus is clearly present. (A) The colored hippocampal regions show correlation with the hippocampal region denoted by the arrow, illustrating through-plane connectivity. (B) The colored hippocampal regions show correlation with the hippocampal region denoted by the arrow, illustrating within-plane hippocampal connectivity. Note that the structural scan in this figure was obtained with a different technique than the structural scan of subject 1 in Figs. 3, 4, and 5.
explained by a single regressor. This method was applied in BOLD fMRI time series data, where the time signal of the hippocampus was the main interest. In a set of experiments with fast and slow sampling (that is, short and long TR) and relatively low and also high spatial resolution, we showed NTCC between the left and right hippocampus. Importantly, by including CSF and the respiration signal as regressors, we are confident that these signals are not the underlying cause of the observed hippocampal connectivity. If simple cross-correlations would have been used in the current study, this alternative explanation could not have been excluded. Furthermore, other regions where no connectivity was expected (white matter, background noise) did not show any connectivity, evidencing the specificity of the hippocampal NTCC. It has been argued that in NTCC fMRI studies, short TRs (that is, fast sampling) should be applied to make sure that the signals of physiologic noise are sampled fast enough so that they can be removed from the data. This is necessary to make sure that connectivity, if present, is not caused by simple pulsations or oxygenation changes caused by the respiratory and cardiac cycle but rather have a neuronal origin. The short TR limits the scanned volume to two or three slices, which is obviously a disadvantage. Yet, NTCC has been proven to be detectable using fMRI both using a short TR and a long TR, although long TR results were less
specific (Lowe et al., 1998). Furthermore, it has also been suggested that increasing the TR is in fact beneficial for detecting resting state correlations, since it “removes” the cardiac component from the CSF signal (Li, 2001). In the current study data were filtered to remove the cardiac component and the respiratory signal was used as a regressor of no interest. Although these physiologic signals are aliased into the passband of the low pass filter when a long TR is applied and therefore not completely removed, the long TR results in the current study are in agreement with the short TR results, showing connectivity between left and right hippocampus. This suggests that these NTCC methods can also be applied using whole brain scans (long TRs). Clearly, resting state connectivity studies would strongly benefit from whole brain data since analysis of NTCC would be unrestricted regarding spatial localization. Although NTCC methods and the interpretation of the resulting connectivity patterns are a topic of debate, the number of studies showing convincing presence of NTCC in different networks in the brain is growing. Of specific clinical interest is the loss of integrity of networks that can be detected using NTCC methods in diseased states (Li et al., 2002; Lowe et al., 2002). The detection of hippocampal connectivity using NTCC as found in our study might be of particularly interest for (functional) imaging studies of medial temporal lobe epilepsy and early Alzheimer’s disease.
1244
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
In Alzheimer’s disease, the focus in such studies is often the hippocampal complex, where neuronal degeneration is believed to start. However, targeting the hippocampal complex with fMRI is often impractical since it requires patient cooperation during a complex memory task. This problem becomes more severe when patients are studied in a progressed state of the disease. As an alternative to the use of cognitive tasks, the resting state metabolism can be targeted in the hippocampal complex, using methods of resting state oxygenation (Small et al., 2000), or methods of average resting state connectivity (Li et al., 2002), methods of independent component analysis (Beckmann et al., 2001; Mckeown et al., 1998), or multiple regression NTCC as presented in the current study. Further studies applying these methods in early dementia will have to show whether such methods are sensitive to early neuronal changes in Alzheimer’s disease. To conclude, we have successfully applied a multiple regression analysis to analyze connectivity between the left and right hippocampus. Both maps of BOLD hippocampal connectivity and connectivity caused by spurious signals could be identified with this method.
Fig. 9. “No task condition” connectivity using a long TR (TR ⫽ 2400 ms) in subject 4 (that is, the second subject of experiment 2). Left in the images is right in the brain. Arrows indicate the ROIs that were used as hippocampal regressors. The images A and B show within-plane connectivity between left and right hippocampus.
Fig. 10. Resting state connectivity using a long TR (TR ⫽ 2400 ms) in subject 5 (that is, the third subject of experiment 3). Left in the images is right in the brain. The arrow indicates the ROI used as hippocampal regressor of this correlation map. Less specific, but also in this subject resting state connectivity is detected between left and right hippocampus. The images show both through-plane hippocampal connectivity (left image), and also within-plane hippocampal connectivity (right image).
References Beckmann, C.F., Noble, J.A., Smith, S.M., 2001. Spatio-temporal accuracy of ICA for FMRI. Neuroimage 13, S75. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magn. Reson. Med. 34, 537–541. Buchel, C., Friston, K.J., 1997. Modulation of connectivity in visual pathways by attention— cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7, 768 –778. Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2000. Mapping functionally related regions of brain withfunctional connectivity MR imaging. AJNR Am. J. Neuroradiol 21, 1636 –1644. Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173. Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., Noll, D.C., 1995. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33, 636 – 647. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J., 1993. Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14. Grady, C.L., Furey, M.L., Pietrini, P., Horwitz, B., Rapoport, S.I., 2001. Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease. Brain 124, 739 –756. Hampson, M., Peterson, B.S., Skudlarski, P., Gatenby, J.C., Gore, J.C., 2002. Detection of functional connectivity using temporal correlations in MR images. Hum. Brain Mapp. 15, 247–262. Herbster, A.N., Nichols, T., Wiseman, M.B., Mintun, M.A., Dekosky, S.T., Becker, J.T., 1996. Functional connectivity in auditory-verbal shortterm memory in Alzheimer’s disease. Neuroimage 4, 67–77. Hu, X., Le, T.H., Parrish, T., Erhard, P., 1995. Retrospective estimation and correction of physiological fluctuation in functional MRI. Magn. Reson. Med. 34, 201–212. Li, S.J., 2001. Reducing cardiac noise in BOLD-weighted voxel time courses in an fMRI dataset by increasing TR and/or applying a crusher gradient in an EPI acquisition pulse. Magn. Reson. Med. 46, 629. Li, S.J., Biswal, B., Li, Z., Risinger, R., Rainey, C., Cho, J.K., Salmeron, B.J., Stein, E.A., 2000. Cocaine administration decreases functional connectivity in human primary visual and motor cortex as detected by functional MRI. Magn. Reson. Med. 43, 45–51. Li, S.J., Li, Z., Wu, G., Zhang, M., Franczak, M., Antuono, P., 2002. Alzheimer disease: evaluation of a functional MR imaging index as a marker. Radiology 225, 253–259.
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245 Lowe, M.J., Mock, B.J., Sorenson, J.A., 1998. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage 7, 119 –132. Lowe, M.J., Phillips, M.D., Lurito, J.T., Mattson, D., Dzemidzic, M., Mathews, V.P., 2002. Multiple sclerosis: low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity—initial results. Radiology 224, 184 –192. Maguire, E.A., Mummery, C.J., Buchel, C., 2000. Patterns of hippocampal-cortical interaction dissociate temporal lobe memory subsystems. Hippocampus 10, 475– 482. Mckeown, M.J., Jung, T.P., Makeig, S., Brown, G., Kindermann, S.S., Lee, T.W., Sejnowski, T.J., 1998. Spatially independent activity patterns in functional MRI data during the Stroop color-naming task. Proc. Acad. Natl. Sci. USA 95, 803– 810. Paus, T., Jech, R., Thompson, C.J., Comeau, R., Peters, T., Evans, A.C., 1997. Transcranial magnetic stimulation during positron emission tomography: a new method for studying connectivity of the human cerebral cortex. J. Neurosci. 17, 3178 –3184.
1245
Small, S.A., Wu, E.X., Bartsch, D., Perera, G.M., Lacefield, C.O., DeLaPaz, R., Mayeux, R., Stern, Y., Kandel, E.R., 2000. Imaging physiologic dysfunction of individual hippocampal subregions in humans and genetically modified mice. Neuron 28, 653– 664. Smith, S.M., 2001. Overview of fMRI analysis, in: Jezzard, P., Matthews, P.M., Smith, S.M. (Eds.), Functional MRI. An Introduction to Methods. Oxford University Press, Oxford, pp. 215–227. Stam, C.J., van Walsum, A.M.V., Pijnenburg, Y.A.L., Berendse, H.W., de Munck, J.C., Scheltens, P., van Dijk, B.W., 2002. Generalized synchronization of MEG recordings in Alzheimer’s disease: evidence for involvement of the gamma band. J. Clin. Neurophysiol 19, 562–574. Stein, T., Moritz, C., Quigley, M., Cordes, D., Haughton, V., Meyerand, E., 2000. Functional connectivity in the thalamus and hippocampus studied with functional MR imaging. AJNR Am. J. Neuroradiol 21, 1397– 1401. Worsley, K.J., 2001. Statistical analysis of activation images, in: Jezzard, P., Matthews, P.M., Smith, S.M. (Eds.), Functional MRI. An Introduction to Methods. Oxford University Press, Oxford, pp. 251–270.
www.elsevier.com/locate/ynimg
Identifying confounds to increase specificity during a “no task condition” Evidence for hippocampal connectivity using fMRI S.A.R.B. Rombouts,a,b,e,* C.J. Stam,c,e J.P.A. Kuijer,a Ph. Scheltens,b,e and F. Barkhof d,e a
Department of Physics and Medical Technology, VU Medical Center, Amsterdam, The Netherlands b Department of Neurology, VU Medical Center, Amsterdam, The Netherlands c Department of Clinical Neurophysiology, VU Medical Center, Amsterdam, The Netherlands d Department of Radiology, VU Medical Center, Amsterdam, The Netherlands e Alzheimer Center, VU Medical Center, Amsterdam, The Netherlands Received 24 February 2003; revised 22 May 2003; accepted 23 June 2003
Abstract Functional MRI can be applied to study connectivity in the brain during a “no task condition.” This study focuses on applying a multiple linear regression analysis to identify spurious connectivity caused by confounding factors such as physiologic noise and to separate these from hippocampal connectivity caused by the blood oxygen level dependent (BOLD) signal during a no-task condition. Regressors of interest (hippocampal time courses) as well as regressors of no interest (respiratory signal and cerebrospinal fluid), were included in the analysis, and each yielded a connectivity map. This method was applied at high sampling rate (limited volume, proper physiologic noise sampling), low sampling rate (whole brain scans possible), and at high and low spatial resolution in five healthy control subjects. Regressors of no interest showed specific connectivity patterns, different from hippocampal regressors. The latter showed connectivity between left and right hippocampus. The current study shows successful application of a multiple regression analysis to study connectivity between left and right hippocampus. Both maps of hippocampal connectivity caused by BOLD signal and connectivity caused by spurious signals could be identified. © 2003 Elsevier Inc. All rights reserved.
Introduction Functional connectivity in the analysis of neuroimaging time-series has been defined as “temporal correlations between spatially remote neurophysiological events” (Friston et al., 1993). Functional connectivity in the human brain has found application in studies where subjects were engaged in a specific behavior (see, for example, Maguire et al., 2000; Herbster et al., 1996; Buchel and Friston, 1997; Grady et al., 2001). However, such task-driven studies may show coactivations resulting from the task itself and are therefore strongly dependent on the task and the specific instructions * Corresponding author. Department of Physics and Medical Technology, VU Medical Center, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands. Fax: ⫹31-20-444-4147. E-mail address: [email protected] (S.A.R.B. Rombouts). 1053-8119/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/S1053-8119(03)00386-0
to the subject. Functional connectivity without any specific behavior of a subject, defined as no-task condition connectivity (NTCC), would be a more direct measure of functional connectivity, independent of tasks and instructions. This has been studied using electroencephalography (EEG) (for example, Stam et al., 2002) and also positron emission tomography (PET) where transcranial magnetic stimulation and PET were combined to assess NTCC, finding evidence for connectivity of the human visual system in agreement with the known anatomic connectivity in monkeys (Paus et al., 1997). Besides the interest in functional connectivity from a neuroscientific point of view in healthy controls, NTCC methods in patients would also be of clinical interest. In patients one often encounters difficulties to apply a (cognitive) paradigm, since the functions under study can be (partly) destroyed by the disease process. NTCC methods
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1237
Fig. 1. Picture illustrating the location of the manually drawn regions of interest (ROIs) in subject 1 for experiment 1 (high temporal resolution, limited spatial coverage) on the subject’s structural images. Left in picture is right in the brain. Seven ROIs were used: the right hippocampus on each of the three slices (shown in red), cerebral white matter (green), cerebellar white matter (blue), CSF (yellow), and the signal outside the brain (cyan).
would offer a practical alternative to these task driven studies. In recent years, functional magnetic resonance imaging (fMRI) using blood oxygen level dependent (BOLD) contrast has also been applied to study NTCC. Functional MRI is not invasive, has a spatial resolution higher than EEG and is a relatively fast imaging technique, allowing the acquisition of hundreds of whole brain scans in only a few minutes. Therefore, fMRI might be well suited to study NTCC. The first NTCC fMRI study showed connectivity of low frequency fluctuations of the primary motor cortices (Biswal et al., 1995), and was followed by others showing NTCC between motor cortices (Cordes et al., 2000; Lowe et al., 1998), visual cortex (Cordes et al., 2000; Lowe et al., 1998), and amygdala (Lowe et al., 1998), regions of language (Cordes et al., 2000; Hampson et al., 2002), and also be-
Fig. 2. Drawing illustrating how regions of interest (ROIs) as drawn in the high resolution structural image are transformed to the low resolution fMRI image for further analysis. The left represents a 6 ⫻ 6 pixel square of the structural image, the right the corresponding part of the fMRI image (2 ⫻ 2 pixel square). Black squares represent the pixels contained in an ROI; white pixels are not contained in the ROI. The ROI in the structural image has to be sampled down to the spatial resolution of the fMRI image. Only voxels in the fMRI image that are covered for more than 40% by the original structural ROI are then used for further analyses. For example, the left bottom voxel in the fMRI image was covered by the original ROI for 67% and therefore is also contained in the fMRI image ROI. The top left and bottom right voxel is not contained in the fMRI image ROI, since only have 11% and 33% coverage.
tween thalamus and hippocampus (Stein et al., 2000). It has also been shown that regions with NTCC were functionally connected during performance of a behavioral task (Cordes et al., 2000; Hampson et al., 2002). Furthermore, in neurological disorders, NTCC measures were decreased, both in multiple sclerosis in the motor cortices (Lowe et al., 2002) and in Alzheimer’s disease within the hippocampi (Li et al., 2002). In most NTCC fMRI studies reported so far, three important steps are part of the data analysis: (1) low pass temporal filtering of the BOLD signal, necessary to remove physiologic noise from the data caused by the cardiac (⬃0.9 Hz) and the respiratory (⬃0.3 Hz) cycle; (2) selection of regions of interest (ROIs); and (3) cross-correlation analysis between time courses in the ROI and all other pixels in the scanned volume. Despite precautions such as filtering of the data, spurious correlations between time courses not caused by BOLD signal may occur. These can be caused by pulsations, motion, and CSF signal. The main goal of the current study was to apply a multiple regression analysis to identify correlations maps of these spurious signals and to identify BOLD connectivity. For this purpose, we focused on connectivity between the hippocampi since there is great interest in studying the functions of this structure and its functional network in studies of memory and dementia.
Materials and methods Experiment 1: high temporal resolution, limited spatial coverage Subjects Resting state data were obtained in three right-handed subjects (subjects 1–3, 1 male, 2 female; ages 26, 26 and 33 years) free from medication and neurologic and psychiatric complaints.
1238
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 3. Example of “no task” connectivity pattern (unclustered) in subject 1 of the respiration signal (A), and CSF signal (B), projected on the structural scan. Left in the image is right in the brain. The regressor in (A) was the recorded respiratory cycle, in (B) the average CSF signal in the CSF ROI indicated by the arrow. Clearly, there is strong interregional correlation of CSF spaces throughout the slices. Note that by including respiration and CSF regressors in the analysis, the variances explained by these time courses in the regions shown in this figure, are removed from the data and cannot cause spurious correlations between these regions and other regressors in the data analysis.
MRI scanning Imaging was performed on a 1.5-T Sonata scanner (Siemens, Erlangen, Germany) using the standard circularly polarized head coil. Foam pads were used to minimize head motion and subjects wore earplugs to reduce the scanner noise. For functional imaging T*2-weighted single shot echo planar imaging (EPI: TR ⫽ 300 ms; TE ⫽ 60 ms; flip angle ⫽ 45°, receiver bandwidth 890 Hz per pixel; field of view ⫽ 192 ⫻ 192 mm; matrix size ⫽ 64 ⫻ 64; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm) was applied. Three slices were placed perpendicular to the long axis of the hippocampus covering the middle part of the hippocampus. For this 17-mm-thick volume, 1029 scans were acquired in 5 min and 9 s. For anatomical reference a structural scan using a T1-weighted sequence was obtained with exactly the same slice position (either a 3D gradient echo with TR ⫽ 2700 ms; TE ⫽ 3.54 ms, flip angle ⫽ 8°, inversion time ⫽
950 ms; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm, or a 2D spin echo with TR ⫽ 595 ms, TE ⫽ 14 ms, flip angle ⫽ 90°; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm). Subjects were instructed to lie awake with their eyes closed during scanning and to refrain from any cognitive, language, or motor tasks as much as possible. Acquisition of cardiac and respiratory signal The respiratory signal was acquired with a flexible pressure belt placed around the upper abdomen of the subject, and the cardiac signal was acquired with a pulse oximeter placed on the middle finger of the subject. The latter signal provides a delayed systolic signal and also includes the oxygenation saturation levels. These signals were sampled with 50 and 200 Hz, respectively, and stored on a computer.
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1239
in each hippocampal region (that is, each slice, giving three hippocampal time courses), and average cerebral and cerebellar white matter, CSF, and background noise. The other regressors were the “time courses” of the six realignment parameters (that is, three translational and three rotational parameters) and the respiratory movement signal registered during scanning. This respiration signal closely resembles the effect of respiration on imaging data (Hu et al., 1995). However, the oximeter signal has a completely different shape than the effect of heart rate on imaging data (Hu et al., 1995). Therefore the oximeter signal was not included in the regression analysis. The respiratory movement signal (50 Hz) was resampled to match the sample rate of the fMRI data. Hence 14 regressors in total were used. This resulted in the following model for the data:
冘 *R 共t兲 11
y共t兲 ⫽ c ⫹ d ⴱ t ⫹
k
k
k⫽1
冘  ⴱ Hippo 共t兲 ⫹ e 14
⫹ Fig. 4. Example of connectivity pattern of white matter (A), white matter in cerebellum (B) in subject 1 projected on the structural scan. Left in image is right in brain. Signal time courses of the colored voxels within the black circle were averaged and used as regressors. There were no clustered regions with connectivity with any of these regressors.
Data analysis Data were analyzed using AFNI 2.5 (Cox, 1996). The first 5 scans were discarded to account for spin saturation effects, leaving 1024 scans for data analysis. EPI scans were realigned using the first volume as a reference, using standard rigid body realignment, and no slice timing correction or spatial smoothing was applied. In the frequency domain, a low pass filter was applied with a cutoff frequency of 0.4 Hz to remove physiologic noise caused by the cardiac cycle (⬃0.8 –1 Hz). Next, on the structural scans the right hippocampus was outlined manually on each of the three slices. Additionally, cerebral regions containing only white matter, cerebellar white matter, CSF, and the signal of scanned regions outside the brain (background noise) were also drawn (Fig. 1). Given the much lower spatial resolution of the fMRI scans compared to the structural images and the outlined regions interest, the ROIs had to be sampled down to the spatial resolution of the fMRI images. Only voxels in fMRI images that were covered for more than 40% by the original structural ROI were used for further analyses (Fig. 2). Of these voxels the signal time courses were averaged. Markers such as brain boundaries, ventricle edges, and so on, and also localization of ROIs were visually inspected for correct registration between structural and fMRI images. Next, time courses from the seven ROIs were used in a multiple linear regression model: the average time courses
k
k
k⫽12
where y(t) is the data (time course of each voxel), t is time, and c and d are constants, and e is the error term. Rk(t) are the 11 regressors other than the hippocampal signal, Hippok(t) are the three hippocampal regressors, and k are the parameter estimates for the regressors. This model was fitted to the data, resulting in estimations of k and corresponding standard error for each voxel, of which the corresponding statistical partial T map was calculated (for further details on multiple regression analysis in the context of fMRI data, see Smith, 2001; Worsley, 2001). Further, a partial R2 map was obtained, representing the proportional reduction of the variation in the data explained by each regressor (R2 can be considered a generalization of the square of a correlation coefficient). The regressors representing the covariates of no interest (i.e., realignment parameters, respiratory signal and CSF) were included to remove these components from the signal and to decrease the error term in the results. The main effects of interest were the partial correlations of the three hippocampal regressors (that is, the average right hippocampal time course per slice). Cerebral white matter, cerebellar white matter, and background signal were included as regressors to serve as internal control, since no partial correlation in any region is expected for these regressors. Partial correlation maps were thresholded at T ⫽ 5 and partial correlation maps of the regressors of the regions of interest (i.e., hippocampus) and internal control regions were additionally clustered, limiting the minimal cluster size to 200 mm3 [giving P ⬍ 0.05 corrected (Forman et al., 1995)].
1240
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 5. Examples of “no task condition connectivity” between left and right hippocampus in subject 1 (A), 2 (B), and 3 (C) projected on their structural scans (A and C: gradient echo, B: spin echo; left in the images is right in the brain). Each image group (that is, A-I, A-II, B-I, B-II, and C) corresponds to the connectivity map of one single regressor: the arrows indicate the voxels that were averaged to obtain a regressor of the right hippocampus. Of each slice, one hippocampal regressor was obtained, giving three in total. Correlations between hippocampi are found in the same slice (A-I, A-II, B-I, the middle image in B-II, and right image in C). Correlation is also found with the hippocampal signal in one of the other slices, as illustrated in subject 2 (B-II, left and right image) and subject 3 (C, left image).
Experiment 2: increased in-plane spatial resolution In subject 1, an EPI sequence with a higher spatial resolution, allowing ROI definition on the functional EPI scan, was also used in a separate experiment. A T*2 EPI sequence was used (TR ⫽ 327 ms; TE ⫽ 60 ms; flip angle ⫽ 45°; receiver bandwidth 1052 Hz per pixel; field of view ⫽ 192 ⫻ 192 mm; matrix size ⫽ 96 ⫻ 128; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm), placed perpendicular to the long axis of the hippocampus covering the middle part of the hippocampus. Of this 17-mm-thick volume, 1029 scans were acquired in 5 min and 36 s. Note that due to the increased spatial resolution, scan time was increased slightly compared to experiment 1. Data analysis was the
same as described in experiment 1, except for the minimal cluster size of the partial correlation maps of the hippocampal regressors, which was decreased to 50 mm3 [P ⬍ 0.05 corrected (Forman et al., 1995)]. Experiment 3: whole brain coverage at low temporal resolution In a third experiment, subject 1 of experiment 1 was scanned again in a separate session, and two new righthanded subjects [subjects 4 and 5: 2 females (age 33 and 34 years)] were included. EPI scans were obtained with the same voxel sizes as in experiment 1, yet the volume was extended to 24 slices (EPI: TR ⫽ 2400 ms s; TE ⫽ 60 ms;
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245 Table 1 Multiple regression analysis of a signal time course results in one partial R2 value for each regressor included in the analysisa Subject3 Regressors2
1: STR 2: STR 3: STR 1: HR 1: LTR 4: LTR 5: LTR
Average motion Respiration Cerebellum WM CSF Hippocampus Full model
0.006 0.009 0.035 0.012 0.003 0.136 0.434
0.008 0.000 0.000 0.000 0.007 0.045 0.162
0.004 0.000 0.009 0.018 0.000 0.095 0.276
0.025 0.005 0.010 0.002 0.013 0.050 0.180
0.042
0.009
0.010
0.003 0.002 0.008 0.224 0.459
0.006 0.050 0.005 0.310 0.690
0.010 0.020 0.006 0.170 0.380
a The table shows for each individual the R2s of one left-sided hippocampal time course that has the most significant connectivity with one of the hippocampal regressors. The “full model” R2 represents the proportion of the variation in the data that is explained by the full regression model. The partial R2 of each regressor is the proportional reduction of the variation in the data that is explained by adding the regressor to the model. Note that these R2 values are not of the whole hippocampal regions, but only represent the most significant hippocampal voxels. STR ⫽ short TR; HR ⫽ high resolution; LTR ⫽ long TR.
flip angle ⫽ 90°, receiver bandwidth 890 Hz per pixel); 517 scans were acquired in 20 min and 41 s. For anatomical reference, a 2D spin echo (TR ⫽ 595 ms, TE ⫽ 14 ms, flip angle ⫽ 90°; field of view ⫽ 250 ⫻ 250 mm; matrix size ⫽ 256 ⫻ 256; slice thickness ⫽ 5 mm; interslice gap ⫽ 1 mm) was used. In experiment 3, a low pass filter was applied in the frequency domain with a cutoff frequency of 0.1 Hz. The band pass we applied is similar to other studies applying a long TR (Lowe et al., 1998; Lowe et al., 2002; Li et al., 2000; Hampson et al., 2002). Note also that frequency components of both of the cardiac and the respiratory signal will be aliased in the passband. Other data analyses parameters were the same as in experiment 1, except that more hippocampal ROIs could be determined: in each subject, five hippocampal ROIs were determined, and their average signal of each ROI included in the analysis, yielding five hippocampal regressors.
1241
control regions where the signal was not expected to have connectivity with other regions (cerebral and cerebellar white matter, background noise) did indeed not show a clear connectivity with any other regions (Fig. 4). The regressors of interest on the other hand (i.e., the three right hippocampal ROIs in each subject) showed definite connectivity with the left hippocampus in all three subjects (Fig. 5). Note that this hippocampal connectivity pattern is “corrected for” spurious signals of no interest as shown in Fig. 3 (i.e., those components were removed from the signal). Table 1, provided to give an impression of the R2 values in this study, shows that the variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 16 – 43% in the three subjects (that is, R2 values of 0.16 – 0.43, Table 1). Regressors of no interest (CSF, respiration, motion) explained variance in the hippocampal signal for less than 1%, while the hippocampal regressor explained variance for 5–14% (Table 1). The dominant frequency of correlated hippocampal time courses indicated slow wave oscillations (0.03– 0.08 Hz, example of one subject shown in Fig. 6). Experiment 2: short TR, increased inplane spatial resolution Although the results of connectivity are noisier due to the decreased signal-to-noise ratio in the higher resolution scans, the results of experiment 1 were repeated: CSF signal showed strong interregional connectivity, while some regions’ time course variances were explained by the respiratory signal. Cerebral and cerebellar white matter did not show any connectivity.
Results Experiment 1 The frequency of the cardiac signal was between 0.9 and 1.2 Hz in the three subjects. Given our sample rate (1/TR) of 3.33 Hz, the cardiac signal therefore was not undersampled and we are confident it has been appropriately filtered from of the data. The respiration frequency was between 0.25 and 0.33 Hz. Variance in some regions could be explained by the respiratory signal (Fig. 3a), while the partial correlation map of the CSF regressor shows a clear connectivity of CSF signal throughout the scanned volume (Fig. 3b). Internal
Fig. 6. Example of frequency power spectrum in subject 1 (short TR experiment) of the average signal of voxels in the left hippocampus with a significant correlation with any hippocampal regressor. The voxels showed significant connectivity as determined in a multiple regression analysis after low pass filtering the data at 0.4 Hz. The voxels show peak frequencies at 0.03 Hz, indicating slow wave oscillations are the dominant frequencies in the time courses. Also in other subjects the peak of the power frequency of hippocampal signal with a correlation appeared to be in the range of 0.01– 0.08 Hz.
1242
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
Fig. 7. Partial connectivity maps in subject 1 with increased spatial resolution of the EPI functional scan, shown on the first EPI scan. Left in the images is right in the brain. This experiment was performed to check whether the connectivity between the hippocampi as seen in experiment 1 (see Fig. 3A), were really located in the hippocampus, as seen on the EPI scan. (A–B) Partial connectivity map of the three hippocampal ROI regressors. The arrows denote the region used for averaging to obtain the regressor. Although the connectivity pattern is noisier than in experiment 1 due to decreased signal to noise ratio and possibly due to increased number of false positives, connectivity between the left and right hippocampus is still clearly visible.
Most importantly, in each slice, independent connectivity between left and right hippocampus could be detected (Fig. 7). The variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 18% (Table 1). Regressors of no interest (CSF, respiration, motion) explained variance in the hippocampal signal for less than 1%, while the hippocampal regressor explained variance for 5% (Table 1). Again, dominant frequencies of hippocampal signal with left right correlations indicated slow wave oscillations (0.02– 0.06 Hz).
three subjects again resting state connectivity between the hippocampi could be detected (Figs. 8 –10). The variance in the signal in the left hippocampal voxel with the most significant correlation with the right hippocampus was explained by the full model for 38 – 69% in the three subjects (Table 1). Regressors of no interest (CSF, motion) explained variance in the hippocampal signal for less than 1% to 4 %, while the hippocampal regressor explained variance for 17–31% (Table 1). Dominant frequencies in hippocampal regions of left-right correlations showed dominant frequencies in the range of 0.01– 0.06 Hz.
Experiment 3: long TR, low spatial resolution Discussion The partial correlation map of CSF again results in a clear connectivity map of CSF signal throughout the scanned volume. Internal control regions where the signal was not expected to have connectivity with other regions (cerebral and cerebellar white matter, background noise) did indeed not show a clear connectivity with any other regions. Despite the longer TR and therefore the expected increased presence of physiological noise in the data, in all
In this study we address the specificity of “no task condition connectivity” (NTCC) in BOLD fMRI time series by using a multiple linear regression approach. The advantage of this approach, as opposed to other methods used for NTCC, is that in one analysis different regressors, both those of interest and those of no interest, are included and each gives a partial correlation map showing variance that is
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
1243
Fig. 8. No task condition connectivity in subject 1 using a long TR (TR ⫽ 2400 ms). Left in the images is right in the brain. Despite the long TR resulting probably in increased physiological noise, connectivity both within the right and between the left and right hippocampus is clearly present. (A) The colored hippocampal regions show correlation with the hippocampal region denoted by the arrow, illustrating through-plane connectivity. (B) The colored hippocampal regions show correlation with the hippocampal region denoted by the arrow, illustrating within-plane hippocampal connectivity. Note that the structural scan in this figure was obtained with a different technique than the structural scan of subject 1 in Figs. 3, 4, and 5.
explained by a single regressor. This method was applied in BOLD fMRI time series data, where the time signal of the hippocampus was the main interest. In a set of experiments with fast and slow sampling (that is, short and long TR) and relatively low and also high spatial resolution, we showed NTCC between the left and right hippocampus. Importantly, by including CSF and the respiration signal as regressors, we are confident that these signals are not the underlying cause of the observed hippocampal connectivity. If simple cross-correlations would have been used in the current study, this alternative explanation could not have been excluded. Furthermore, other regions where no connectivity was expected (white matter, background noise) did not show any connectivity, evidencing the specificity of the hippocampal NTCC. It has been argued that in NTCC fMRI studies, short TRs (that is, fast sampling) should be applied to make sure that the signals of physiologic noise are sampled fast enough so that they can be removed from the data. This is necessary to make sure that connectivity, if present, is not caused by simple pulsations or oxygenation changes caused by the respiratory and cardiac cycle but rather have a neuronal origin. The short TR limits the scanned volume to two or three slices, which is obviously a disadvantage. Yet, NTCC has been proven to be detectable using fMRI both using a short TR and a long TR, although long TR results were less
specific (Lowe et al., 1998). Furthermore, it has also been suggested that increasing the TR is in fact beneficial for detecting resting state correlations, since it “removes” the cardiac component from the CSF signal (Li, 2001). In the current study data were filtered to remove the cardiac component and the respiratory signal was used as a regressor of no interest. Although these physiologic signals are aliased into the passband of the low pass filter when a long TR is applied and therefore not completely removed, the long TR results in the current study are in agreement with the short TR results, showing connectivity between left and right hippocampus. This suggests that these NTCC methods can also be applied using whole brain scans (long TRs). Clearly, resting state connectivity studies would strongly benefit from whole brain data since analysis of NTCC would be unrestricted regarding spatial localization. Although NTCC methods and the interpretation of the resulting connectivity patterns are a topic of debate, the number of studies showing convincing presence of NTCC in different networks in the brain is growing. Of specific clinical interest is the loss of integrity of networks that can be detected using NTCC methods in diseased states (Li et al., 2002; Lowe et al., 2002). The detection of hippocampal connectivity using NTCC as found in our study might be of particularly interest for (functional) imaging studies of medial temporal lobe epilepsy and early Alzheimer’s disease.
1244
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245
In Alzheimer’s disease, the focus in such studies is often the hippocampal complex, where neuronal degeneration is believed to start. However, targeting the hippocampal complex with fMRI is often impractical since it requires patient cooperation during a complex memory task. This problem becomes more severe when patients are studied in a progressed state of the disease. As an alternative to the use of cognitive tasks, the resting state metabolism can be targeted in the hippocampal complex, using methods of resting state oxygenation (Small et al., 2000), or methods of average resting state connectivity (Li et al., 2002), methods of independent component analysis (Beckmann et al., 2001; Mckeown et al., 1998), or multiple regression NTCC as presented in the current study. Further studies applying these methods in early dementia will have to show whether such methods are sensitive to early neuronal changes in Alzheimer’s disease. To conclude, we have successfully applied a multiple regression analysis to analyze connectivity between the left and right hippocampus. Both maps of BOLD hippocampal connectivity and connectivity caused by spurious signals could be identified with this method.
Fig. 9. “No task condition” connectivity using a long TR (TR ⫽ 2400 ms) in subject 4 (that is, the second subject of experiment 2). Left in the images is right in the brain. Arrows indicate the ROIs that were used as hippocampal regressors. The images A and B show within-plane connectivity between left and right hippocampus.
Fig. 10. Resting state connectivity using a long TR (TR ⫽ 2400 ms) in subject 5 (that is, the third subject of experiment 3). Left in the images is right in the brain. The arrow indicates the ROI used as hippocampal regressor of this correlation map. Less specific, but also in this subject resting state connectivity is detected between left and right hippocampus. The images show both through-plane hippocampal connectivity (left image), and also within-plane hippocampal connectivity (right image).
References Beckmann, C.F., Noble, J.A., Smith, S.M., 2001. Spatio-temporal accuracy of ICA for FMRI. Neuroimage 13, S75. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magn. Reson. Med. 34, 537–541. Buchel, C., Friston, K.J., 1997. Modulation of connectivity in visual pathways by attention— cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7, 768 –778. Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2000. Mapping functionally related regions of brain withfunctional connectivity MR imaging. AJNR Am. J. Neuroradiol 21, 1636 –1644. Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173. Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., Noll, D.C., 1995. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33, 636 – 647. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J., 1993. Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14. Grady, C.L., Furey, M.L., Pietrini, P., Horwitz, B., Rapoport, S.I., 2001. Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease. Brain 124, 739 –756. Hampson, M., Peterson, B.S., Skudlarski, P., Gatenby, J.C., Gore, J.C., 2002. Detection of functional connectivity using temporal correlations in MR images. Hum. Brain Mapp. 15, 247–262. Herbster, A.N., Nichols, T., Wiseman, M.B., Mintun, M.A., Dekosky, S.T., Becker, J.T., 1996. Functional connectivity in auditory-verbal shortterm memory in Alzheimer’s disease. Neuroimage 4, 67–77. Hu, X., Le, T.H., Parrish, T., Erhard, P., 1995. Retrospective estimation and correction of physiological fluctuation in functional MRI. Magn. Reson. Med. 34, 201–212. Li, S.J., 2001. Reducing cardiac noise in BOLD-weighted voxel time courses in an fMRI dataset by increasing TR and/or applying a crusher gradient in an EPI acquisition pulse. Magn. Reson. Med. 46, 629. Li, S.J., Biswal, B., Li, Z., Risinger, R., Rainey, C., Cho, J.K., Salmeron, B.J., Stein, E.A., 2000. Cocaine administration decreases functional connectivity in human primary visual and motor cortex as detected by functional MRI. Magn. Reson. Med. 43, 45–51. Li, S.J., Li, Z., Wu, G., Zhang, M., Franczak, M., Antuono, P., 2002. Alzheimer disease: evaluation of a functional MR imaging index as a marker. Radiology 225, 253–259.
S.A.R.B. Rombouts et al. / NeuroImage 20 (2003) 1236 –1245 Lowe, M.J., Mock, B.J., Sorenson, J.A., 1998. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage 7, 119 –132. Lowe, M.J., Phillips, M.D., Lurito, J.T., Mattson, D., Dzemidzic, M., Mathews, V.P., 2002. Multiple sclerosis: low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity—initial results. Radiology 224, 184 –192. Maguire, E.A., Mummery, C.J., Buchel, C., 2000. Patterns of hippocampal-cortical interaction dissociate temporal lobe memory subsystems. Hippocampus 10, 475– 482. Mckeown, M.J., Jung, T.P., Makeig, S., Brown, G., Kindermann, S.S., Lee, T.W., Sejnowski, T.J., 1998. Spatially independent activity patterns in functional MRI data during the Stroop color-naming task. Proc. Acad. Natl. Sci. USA 95, 803– 810. Paus, T., Jech, R., Thompson, C.J., Comeau, R., Peters, T., Evans, A.C., 1997. Transcranial magnetic stimulation during positron emission tomography: a new method for studying connectivity of the human cerebral cortex. J. Neurosci. 17, 3178 –3184.
1245
Small, S.A., Wu, E.X., Bartsch, D., Perera, G.M., Lacefield, C.O., DeLaPaz, R., Mayeux, R., Stern, Y., Kandel, E.R., 2000. Imaging physiologic dysfunction of individual hippocampal subregions in humans and genetically modified mice. Neuron 28, 653– 664. Smith, S.M., 2001. Overview of fMRI analysis, in: Jezzard, P., Matthews, P.M., Smith, S.M. (Eds.), Functional MRI. An Introduction to Methods. Oxford University Press, Oxford, pp. 215–227. Stam, C.J., van Walsum, A.M.V., Pijnenburg, Y.A.L., Berendse, H.W., de Munck, J.C., Scheltens, P., van Dijk, B.W., 2002. Generalized synchronization of MEG recordings in Alzheimer’s disease: evidence for involvement of the gamma band. J. Clin. Neurophysiol 19, 562–574. Stein, T., Moritz, C., Quigley, M., Cordes, D., Haughton, V., Meyerand, E., 2000. Functional connectivity in the thalamus and hippocampus studied with functional MR imaging. AJNR Am. J. Neuroradiol 21, 1397– 1401. Worsley, K.J., 2001. Statistical analysis of activation images, in: Jezzard, P., Matthews, P.M., Smith, S.M. (Eds.), Functional MRI. An Introduction to Methods. Oxford University Press, Oxford, pp. 251–270.