Conceived and designed the experiments: FC KS SD DC GCG SC. Performed the experiments: FC KS SD DC SC. Analyzed the data: FC KS FD SC. Contributed reagents/materials/analysis tools: FC KS SD DC FD GCG SC. Wrote the paper: FC KS SD DC FD GCG SC.
The authors have declared that no competing interests exist.
The spontaneous component of neuropathic pain (NP) has not been explored sufficiently with neuroimaging techniques, given the difficulty to coax out the brain components that sustain background ongoing pain. Here, we address for the first time the correlates of this component in an fMRI study of a group of eight patients suffering from diabetic neuropathic pain and eight healthy control subjects. Specifically, we studied the functional connectivity that is associated with spontaneous neuropathic pain with spatial independent component analysis (sICA).
Functional connectivity analyses revealed a cortical network consisting of two anti-correlated patterns: one includes the left fusiform gyrus, the left lingual gyrus, the left inferior temporal gyrus, the right inferior occipital gyrus, the dorsal anterior cingulate cortex bilaterally, the pre and postcentral gyrus bilaterally, in which its activity is correlated negatively with pain and positively with the controls; the other includes the left precuneus, dorsolateral prefrontal, frontopolar cortex (both bilaterally), right superior frontal gyrus, left inferior frontal gyrus, thalami, both insulae, inferior parietal lobuli, right mammillary body, and a small area in the left brainstem, in which its activity is correlated positively with pain and negatively with the controls. Furthermore, a power spectra analyses revealed group differences in the frequency bands wherein the sICA signal was decomposed: patients' spectra are shifted towards higher frequencies.
In conclusion, we have characterized here for the first time a functional network of brain areas that mark the spontaneous component of NP. Pain is the result of aberrant default mode functional connectivity.
Living with chronic pain is highly maladaptive: several studies have shown that chronic pain can modify the way in which one perceives their everyday experience, in turn generating negative emotions and thoughts and changing the physiological and psychological processes
Over the past 15 years, the brain correlates of neuropathic pain (NP) have been characterized in several neuroimaging studies
Few studies have explored the effect of pain on brain functional connectivity
Several cortical areas have been implicated in pain processing but, puzzlingly enough, both activations and deactivations have been observed, the significance of which in the genesis of background NP has not been coherently explained
Pain perception emerges from the flow and integration of information between specific brain areas and, therefore, lends itself to connectivity analyses. The underlying assumption of the present study is that, under conditions that generate background NP, the resting, “default” state is altered. Thus, we compared the DMN of a sample of diabetic neuropathic pain patients with that of a group of matched normal controls adopting a spatial Independent Component Analysis (sICA) approach
All of the subjects gave their informed written consent, in line with the Declaration of Helsinki, and the study was approved by the local ethics committee.
Eight right-handed consecutive patients suffering from peripheral NP (diabetic pain) (four women and four men; age range = 51–78, mean age = 61 years) were enrolled from our multidisciplinary pain unit (
Patient | Pain duration | Paresthesias | Dysesthesias | Quality of pain | VAS (0–10) | Distribution |
1 | 2 yrs | + | Pulsating-burning | 6 | Lower limbs | |
2 | 2 yrs | + | + | Burning - | 5 | Lower limbs |
3 | 3 yrs | + | Burning-squeezing | 5 | Lower limbs | |
4 | 3 yrs | + | + | Burning-squeezing | 6 | Lower limbs |
5 | 2 yrs | + | Lancinating-burning | 6 | Lower limbs | |
6 | 2 yrs | + | Burning-squeezing | 4 | Lower limbs | |
7 | 3 yrs | + | Burning-piercing | 9 | Lower limbs | |
8 | 5 yrs | + | Burning-piercing | 4 | Lower limbs |
All patients underwent a complete neurological and psychological examination as well as standard MR brain scanning by an experienced Neuroradiologist (SD) to exclude structural/white-matter abnormalities on anatomical MR-images. Patients suffering from significant psychiatric disorders were excluded. All of the patients were assessed using standardized pain scales (Visual Analog Scale - VAS, Numerical Rating Scale - NRS, McGill Pain Questionnaire - Italian version). The spontaneous component of their pain syndrome was evaluated on the MPQ checklist. VAS readings were obtained from their clinical records both the day before and on the day of the study. In all cases, pain was restricted to the lower limbs. The duration of pain was >2 years in all the cases. Patients were washed out of their medications at least one month before imaging (opioids or cannabinoids were never administered). At the time of the scanning, the pain intensity had reached the pre-treatment levels. Maximum care was taken to avoid situations that could actually trigger evoked pain during the imaging sessions. Eight age- and gender-matched right-handed healthy volunteers (four women and four men; age range = 47–79, mean age = 59 years) acted as controls. None suffered from any neurological or psychiatric disorders, including chronic pain of any kind, and they also did not have a history of drug or alcohol abuse. None were on medications that are known to alter brain activity. All of the females participating in the study were menopausal.
All of the subjects were instructed simply to keep their eyes closed, think of nothing in particular, and not to fall asleep.
Data acquisition was performed on a 1.5 Tesla INTERA™ scanner (Philips Medical Systems) with a SENSE high-field, high resolution (MRIDC) head coil that was optimized for functional imaging. Resting state functional T2*-weighted images were acquired using echoplanar (EPI) sequences, with a repetition time (TR) of 2000 ms, an echo time (TE) of 50 ms, and a 90° flip angle. The acquisition matrix was 64×64, and the field of view (FoV) 200 mm. A total of 200 volumes were acquired; each volume consisted of 19 axial slices, parallel to the anterior-posterior (AC-PC) commissure line and covering the whole brain; slice thickness was 4.5 mm with a 0.5 mm gap. Two scans were added at the beginning of the functional scanning and the data was discarded to reach a steady-state magnetization before acquiring the experimental data.
In the same session, a set of three-dimensional high-resolution T1-weighted structural images was acquired for each participant. This data-set was acquired using a Fast Field Echo (FFE) sequence, with a repetition time (TR) of 25 ms, ultra-short echo time (TE), and a 30° flip angle. The acquisition matrix was 256×256, and the field of view (FoV) 256 mm. The set consisted of 160 contiguous sagittal images covering the whole brain. In-plane resolution was 1×1 mm and slice thickness 1 mm (1×1×1 mm voxels).
BOLD imaging data were analyzed using the Brain Voyager QX software (Brain Innovation, Maastricht, Holland); a plug-in extension of this software was used to compute the individual functional connectivity analyses. The functional data of each subject underwent the following pre-processing steps: 1) Mean intensity adjustment to prevent global signal variability; 2) slice scan time correction, using a sinc interpolation algorithm; 3) 3D motion correction: all of the volumes were aligned spatially to the first volume by rigid body transformations, using a trilinear interpolation algorithm; 4) spatial smoothing using a Gaussian kernel of 4 mm FWHM; 5) temporal filters (i.e. linear trend removal and non-linear trend removal using a temporal high-pass filter [frequency pass = 0.008 Hz]) were applied to remove drifts due to scanner, and other, low frequency noises.
After pre-processing, a series of steps were followed in order to allow for the precise anatomical location of brain activity to facilitate inter-subject averaging. First, each subject's slice-based functional scan was co-registered on his or her 3D high-resolution structural scan. This process involved mathematical co-registration exploiting the slice positioning stored in the headers of the raw data, as well as fine adjustments that were computed by comparing the data sets based on their intensity values; if needed, manual adjustments were also performed. Second, the 3D structural data set of each subject was transformed into Talairach space: the cerebrum was translated and rotated into the anterior-posterior commissure plane and then the borders of the cerebrum were identified. Third, using the anatomo-functional coregistration matrix and the determined Talairach reference points, the functional time course of each subject was transformed into Talairach space and the volume time course was created. The Talairach transformation was performed in two steps. The first step consisted of rotating the 3D data set of each subject to align it with the stereotactic axes. In the second step, the extreme points of the cerebrum were specified. These points were then used to scale the 3D data sets to the dimensions of the standard brain of the Talairach and Tournoux atlas using a piecewise affine and continuous transformation for each of the 12 defined subvolumes. The individual maps were projected onto the average volumetric image to be displayed using volumetric anatomy.
Functional connectivity was measured using independent component analysis (ICA), which is a statistical technique that separates a set of signals into independent uncorrelated and non-Gaussian spatio-temporal components
The ICA decomposition was calculated using the single-subject ICA plug-in, which corresponded to a C++ implementation of the fast-ICA algorithm
After the ICA was performed, three criteria were used to select the component that most closely matched the default-mode network. 1) ICA components were first screened for the presence of frequencies lower than 0.008Hz and higher than 0.1 Hz by rejecting those components having more than 25% of their total power spectrum outside the frequency range of 0.008–0.1 Hz . 2) ICA components were then screened for the presence of the remaining motion or global signal variability by calculating the regression between those nuisance factors and each IC time course; the components with a correlation of r>0.20 with the nuisance factors were discarded. 3) A spatial template of the default mode network, based on previous related studies, e.g.
The group components were calculated as random effects maps. The random effects statistic for each voxel of the z-maps was generated by the ICA plugin and was calculated as the mean z-value of that voxel across the individual maps divided by its standard error, in turn resulting in a t-statistic; the resulting map of the t-values was visualized by using a (p<0.05) one-sided FDR corrected threshold. A two-sample t-test was used to compare the healthy subjects' and the patients' group maps.
Significant clusters of activation for the two sample t tests were determined by using a (p<0.05) FDR corrected threshold.
Surface-rendered projection of the Default Mode Network components found in the control subjects.
Surface-rendered projection of the Default Mode Network components found in the patients.
Functional connectivity between-groups comparison (
Surface-rendered projection results of a two-sample t-test contrasting the Default Mode Network in the healthy group vs. the pain group. The blue foci indicate the areas that showed significantly less correlational activity in the pain group than in the healthy group. Vice versa the yellow/red foci indicate the areas that showed significantly more correlational activity in the pain group than in the healthy group.
x | y | z | Brodmann's area | Location | Cluster size | Hemisphere |
49 | 16 | 19 | 9 | Inferior Frontal Gyrus | 865 | R |
−53 | 19 | 15 | 45 | Inferior Frontal Gyrus | 1429 | L |
30 | 54 | 4 | 10 | Middle Frontal Gyrus | 1356 | R |
−22 | 57 | 8 | 10 | Middle Frontal Gyrus | 5012 | L |
−29 | 31 | 22 | 9 | Middle Frontal Gyrus | 1311 | L |
33 | 16 | 41 | 8 | Right Middle Frontal Gyrus | 2041 | R |
10 | 42 | 42 | 8 | Right Superior Frontal Gyrus | 1139 | R |
33 | 21 | 9 | 13 | Insula | 418 | R |
−35 | 17 | 2 | 13 | Insula | 1532 | L |
10 | −29 | 5 | Thalamus | 322 | R | |
−8 | −27 | 3 | Thalamus | 319 | L | |
56 | −41 | 29 | 40 | Inferior Parietal Lobule | 3857 | R |
−54 | −42 | 25 | 40 | Inferior Parietal Lobule | 6198 | L |
−11 | −43 | 37 | 31 | Precuneus | 964 | L |
4 | −10 | −6 | Mammillary body | 858 | R | |
−4 | −33 | −38 | Brainstem | 889 | L |
x | y | z | Brodmann's area | Location | Cluster size | Hemisphere |
34 | −25 | 58 | 4 | Precentral Gyrus | 5334 | R |
36 | −33 | 60 | 3 | Postcentral Gyrus | 4911 | R |
−51 | −11 | 44 | 3 | Postcentral Gyrus | 398 | L |
50 | −3 | 22 | 6 | Precentral Gyrus | 10832 | R |
9 | −12 | 68 | 6 | Superior Frontal Gyrus | 1117 | R/L |
−31 | −36 | 63 | 2 | Postcentral Gyrus | 1014 | L |
−52 | −12 | 46 | 3 | Postcentral Gyrus | 399 | L |
1 | 32 | 3 | 32 | Anterior Cingulate | 3632 | R/L |
39 | −77 | −5 | 19 | Inferior Occipital Gyrus | 8695 | R |
−44 | −73 | 1 | 19 | Inferior Temporal Gyrus | 5642 | L |
−18 | −64 | −5 | 19 | Lingual Gyrus | 3101 | L |
−45 | −10 | −22 | 20 | Fusiform Gyrus | 596 | L |
−39 | −61 | −13 | 37 | Fusiform Gyrus | 673 | L |
Patients displayed a reduced connectivity in the left fusiform gyrus, left lingual gyrus, left inferior temporal gyrus, right inferior occipital gyrus, dorsal anterior cingulate cortex (dACC) bilaterally, but also the pre and postcentral gyri (SI/MI) bilaterally. Vice versa, patients displayed a greater connectivity between the left precuneus, dorsolateral prefrontal (DLPF) and frontopolar cortex (both bilaterally), right superior and left inferior frontal gyri, both thalami, both insulae, inferior parietal lobuli, right mammillary body, and a small area in the left brainstem.
In order to explore the possible differences in the signal time course power density
Power spectral analysis
Power spectrum graph of the distribution of the IC power density plot averaged across all healthy group and pain group subjects.
A one-way ANOVA for each single frequency band shows a significant difference between the patients and controls in band 1 (0.008–0.02 Hz; F = 4.834; P = 0.03) and in band 3 (0.05–0.1 Hz; F = 5.578; P = 0.02).
In sum, the power levels in the four frequency bands demonstrated higher frequency oscillations in the pain group's IC: among the patients, the intensity curve is lower at lower frequencies and higher at higher frequencies. Although in both groups' power cluster in the 0.02–0.05 Hz range, the patients' spectra are centered at a higher frequency than those of controls.
The main result of the present work is that the default mode maps of those patients suffering from neuropathic pain differ significantly from those of the healthy controls. Moreover, the power spectra are shifted towards higher frequencies.
Within the NP group, a reduced DMN connectivity and a greater interconnection between some pain-related areas emerged. Specifically, the all-important sensori-motor areas (SI-MI) showed reduced connectivity bilaterally, as expected, while several frontal areas, insulae, and thalami showed greater connectivity, which is a sign of the postulated stronger cognitive-emotional components
Studies show that chronic NP entails a reconfiguration of network connectivity, with fragmentation of cortical processing into “islands” of decorrelated activity
In the present study, RSN in the pain group displayed higher frequency compared to healthy subjects. Functional connectivity studies, such as the present one, only assess a very-low frequency domain, in turn making a comparison with related studies problematic.
Oscillations between 3–10 Hz are observed mostly in SI and more variably in other cortical areas after intra-arterial, pain-inducing injection of bradykinin
Our study addressed functional connectivity. Functional connectivity refers to the arbitrary relationships that might exist between the co-varying activity of distinct and often well-separated neuronal populations, without any reference to the physical connections or an underlying causal model, during a specific condition. A common criticism of neuroimaging is that the information obtained is correlative: Studies rarely describe causation, in which symptoms are merely being related to changes in brain activity within specialized regions. By contrast, effective connectivity refers to causal effects that one neuronal population exerts on another, and is based on an underlying model of the way the different neuronal populations are physically connected
Secondly, our analysis was mainly cortical. Of course, several subcortical areas play a role in the genesis of chronic NP
Finally, peripheral NP has similar features independent of the causative factor (whether this is diabetes, herpes, or trauma). In this regard, we do not expect there to be major differences with other peripheral NP states. It will be of much interest to compare patients with central pain and PNP in future studies, as evidence suggests a difference may exist
These findings suggest that the brain of chronic pain patients differs from that of healthy subjects by showing a reduced default mode network and an increased resting functional connectivity in some pain related areas. This can speculatively suggest a “signature”, or marker of the spontaneous component of NP.
We would like to thank Fabrizio Esposito, Marco Maggiora, Luca Nanetti, and Marco Del Giudice for their methodological support, and all the subjects who participated in this study.