Introduction
Despite advancements in neuroimaging, the use of resting-state functional MRI (rs-fMRI) for understanding cancer pain has seen limited application. Liu et al20 conducted recent studies focusing on amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) among individuals suffering from bone metastasis due to lung cancer. These studies unearthed a notable decrease in ALFF within the prefrontal cortex (PFC) and anterior cingulate cortex (ACC), contrasted by an increase in ReHo in the bilateral thalamus, indicative of disrupted brain activity related to pain processing. In a parallel investigation, Zhou et al. further examined FC patterns in patients with the same condition, revealing increased connectivity between the left ACC and amygdala (AMY) and discovering a positive correlation between FC in the right PFC and ACC with the duration of cancer pain experienced. These collective findings point to abnormal activity within the brain’s pain-processing pathways among cancer pain (CP) patients. However, it is essential to note that existing rs-fMRI research has primarily concentrated on a specific cohort—patients with lung cancer-induced bone metastasis—thus limiting the generalizability of findings across broader cancer pain populations. Additional inquiry is warranted to ascertain the involvement of other critical brain regions in cancer-related pain experiences.
Materials and Methods
Participants
This study received ethical approval from the Research Ethics Committee of Lishui Central Hospital, affiliated with Wenzhou Medical University, ensuring compliance with the Declaration of Helsinki. Each participant provided informed consent prior to participating in the MRI examination. From November 4, 2022, to June 1, 2023, a cohort of 25 CP patients was consecutively recruited from hospitalized patients alongside 25 healthy controls (HC) matched for age and gender. Notably, four of the initial 25 CP patients were later identified as having brain metastases, leading to their exclusion from the study. Ultimately, a total of 21 CP patients and 25 healthy volunteers were included. The inclusion criteria for the CP group comprised: ① Participants aged 18 years or older; ② Documented pathological results or clinical diagnoses confirming cancer pain; ③ Numeric Rating Scale (NRS) scores of ≥1; ④ Ability to endure MRI examinations; ⑤ Only right-handed patients were selected.
Exclusion criteria for the CP cohort included: ① Evidence of abnormal brain structure or intracranial metastasis; ② Existing contraindications to MRI procedures; ③ Pain stemming from conditions unrelated to cancer; ④ The presence of intellectual disabilities or severe neurological or psychiatric disorders.
The inclusion and exclusion criteria for the HC group were as follows: ① Matched for gender, age, and education level with the cancer pain group, all right-handed; ② Absence of intellectual disabilities or significant neurological or psychiatric symptoms and lesions; ③ No contraindications to MRI examination.
Clinical Assessments
On the MRI scan day, under expert supervision, each participant provided demographic data alongside clinical assessments of pain and depression. The Numeric Rating Scale (NRS) was employed to quantify pain intensity, where ‘0’ indicated no pain, and ‘10’ represented the worst pain imaginable. Disease duration, along with the length of cancer pain and opioid usage, were documented based on clinical records for the CP group. Depression levels were evaluated using the Patient Health Questionnaire-9 (PHQ-9). Demographic information was compiled via self-reported questionnaires from all participants.
MRI Image Acquisition
Imaging was conducted using a Philips Ingenia 3.0T MRI scanner, equipped with a 64-channel head-neck coil. During the scanning process, participants were instructed to remain motionless, keep their eyes closed, and to avoid any specific thoughts to maintain a relaxed state, while earplugs helped mitigate scanner noise and pads were used to help reduce head movement. T1-weighted structural MRIs were obtained via a three-dimensional magnetization-prepared rapid gradient echo imaging sequence, utilizing parameters such as: repetition time (TR) of 7.599 ms, echo time (TE) of 3.501 ms, flip angle (FA) of 8°, field of view (FOV) of 256 × 256 mm, a matrix of 256 × 256, and a slice thickness of 1 mm with no slice gap, summing to 192 sagittal slices. Resting-state fMRIs were recorded using an echo planar imaging (EPI) sequence with parameters including TR = 2000 ms, TE = 30 ms, FA = 90°, FOV = 220 × 220 mm, matrix = 64 × 54, slice thickness = 4 mm, slice gap = 0.6 mm, yielding a total of 240 volumes acquired over a duration of 8 minutes and 8 seconds. The MRI images were reviewed by two radiologists with a minimum of five years of experience.
Image Preprocessing
ALFF and ReHo Analysis
The analysis of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) was performed using normalized images. The DPARSF toolbox facilitated the ALFF analysis, effectively removing linear trends and mitigating low- and high-frequency physiological noise through a band-pass filter set between 0.01–0.1 Hz. The filtered time series for each voxel underwent a transformation into the frequency domain via Fast Fourier Transform, enabling the computation of the power spectrum. ALFF values for each subject were derived by calculating the square root of the signal across the 0.01–0.1 Hz band, subsequently normalized by dividing by the global mean ALFF value. For ReHo analysis, Kendall’s coefficient of concordance (KCC) was utilized to evaluate the similarity of time series between a seed voxel and its 26 neighboring voxels. Similar to the ALFF standardization method, ReHo was measured by normalizing the values of each voxel concerning the global mean within the whole-brain mask. Preprocessing included smoothing before ALFF calculations and after ReHo calculations through the application of a Gaussian kernel with a full-width at half-maximum (FWHM) value of 6 mm.
Seed-Based FC Analysis
Voxels surpassing established statistical thresholds during ALFF and ReHo analyses were selected as seed regions. The averaged time series of these seeds were extracted to compute Pearson correlation coefficients between the selected seed regions and the remaining voxels within the whole brain. Subsequently, the distribution of correlation coefficients was normalized using the Fisher r-to-z transformation.
Machine Learning Model
The statistically significant ALFF, ReHo, and FC metrics formed the basis for training the classification model. Utilizing the open-source AutoGluon platform, we effectively categorized cancer pain patients in comparison to healthy controls. The AutoGluon model employs a sophisticated multilayer stack ensembling approach. In its initial layer, AutoGluon utilizes *n* forms of base learners, such as extremely randomized trees, k-nearest neighbors, gradient boosting machines (including XGBoost and CatBoost), random forests, and neural networks. The outputs from these base learners are subsequently compiled into the next layer, where a weighted sum is computed, and weights are determined through training protocols. Additionally, AutoGluon incorporates random search for hyperparameter optimization and model selection.
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Figure 1 The flowchart of the image processing and classification based on resting-state fMRIs. |
Statistical Analysis
This study performed analyses on demographic and neuropsychological data using SPSS statistical software (version 25.0; IBM Corp., Armonk, NY, USA). Quantitative data were expressed as mean ± standard deviation (x ± s). Group comparisons entailed the application of two-sample t-tests, while gender differences were assessed via chi-square analysis. A p-value of
A Jarque–Bera goodness-of-fit test ensured the adherence to normal distribution for ALFF, ReHo, and FC data prior to conducting t-tests. The DPABI software facilitated the analysis processes encompassing the following steps: (1) one-sample t-tests on functional activity and connectivity patterns specific to each group; (2) adjusting for covariates while analyzing t-statistics to evaluate group effects; (3) correction for multiple comparisons utilizing Gaussian random-field theory (GRF) with a voxel-level threshold of p . All subjects were stratified into a training dataset and a testing dataset in a 9:1 ratio. To mitigate potential overestimation within the training dataset, a leave-one-out cross-validation method was employed. This process of splitting training and testing datasets underwent three repetitions, and the averaged results from the three testing datasets were compiled to yield the final classification outcomes. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) served as evaluation metrics for determining classification performance in distinguishing cancer pain patients from healthy controls.
Result
Demographic and Clinical Information
CP and HC patients did not show significant differences in age, gender, education level (p>0.05). The CP group exhibited significantly elevated PHQ-9 depression scores compared to the HC group, with a significant difference noted (p=0.00). Of the CP participants, three displayed no depression, while the remaining 18 ranged from mild to severe depression. The CP cohort had an average illness duration of 679 days, with cancer pain persisting for an average of 271 days. The Oral Morphine Equivalent averaged 121mg, and 61.9% of CP patients reported experiencing breakthrough cancer pain. Additionally, a remarkable 95.24% of CP patients reported mild-to-moderate pain, with NRS scores ranging from 2 to 8. For additional details, refer to Table 1.
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Table 1 Sociodemographic and Clinical Characteristics |
Functional Activity and Connectivity Analysis
The one-sample t-test highlighted that both ALFF and ReHo presented elevated activity primarily within the temporal lobes, whereas lower levels were observed in the parietal and occipital lobes across both groups. The CP patient group exhibited marked reductions in ALFF predominantly in the bilateral inferior temporal gyrus, alongside increased ReHo in the right middle temporal gyrus, and a notable decrease in the left cerebellum Crus2.
Utilizing the two-sample t-test to identify regions with abnormal activity allowed for establishing seed regions for FC calculations. Notably, the CP group demonstrated higher connectivity in the parietal and occipital lobes when compared to the temporal lobe. In contrast, lower FC was observed in the right precentral gyrus, right superior frontal gyrus, middle frontal gyrus, middle orbitofrontal cortex of the frontal lobe, left postcentral gyrus of the parietal lobe, and left cerebellum Crus1 of the cerebellum when compared to HCs. For comprehensive details, please refer to Table 2 and visualizations in Figures 2 and 3.
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Table 2 Brain Regions with Aberrant ALFF, ReHo, and FC in Cancer Pain Patients |
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Figure 2 ALFF, ReHo, and FC maps of intra-group and inter-group comparisons along with ROI-correlation of FC Correlation. Abbreviations: ALFF, amplitude of low-frequency fluctuation; ReHo, regional homogeneity; FC, functional connectivity. |
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Figure 3 Brain regions with significant differences in (a) ALFF, (b) ReHo, and (c) FC. Abbreviations: ALFF, amplitude of low-frequency fluctuation, ReHo, regional homogeneity, FC, functional connectivity. |
Correlation and ROC Curve Analysis
ALFF and ReHo values extracted from individual brain regions yielded moderate discriminative abilities, while FC Z-scores presented superior discriminative ability versus ALFF or ReHo values sourced from individual regions. Notably, the FC linked to the right precentral gyrus produced stronger classification results (AUC = 0.804), as illustrated in Figure 4 and detailed in Table 3. Interestingly, no significant correlation was observed between neuroimaging indices within the CP cohort and their pain scores (P > 0.05) as noted in Table 4.
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Table 3 Classification Performance of Neuroimaging Indices in Discriminating Between Cancer Pain Patients and Healthy Controls |
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Table 4 Results of Spearman Correlation Between Resting-State fMRI Features and Pain Scores in Cancer Pain Patients |
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Figure 4 The ROC curve of classification between cancer pain patients and healthy controls using discriminative ALFF, ReHo, and FC features respectively. Abbreviations: ALFF, amplitude of low-frequency fluctuation; ReHo, regional homogeneity, FC, functional connectivity; AUC, Area Under Curve. |
Discussion
Recent investigations have underscored the role of neuroimaging indices as potential biomarkers for differentiating pain patients from healthy controls. For instance, Liu et al20 demonstrated that integrating ALFF and ReHo from resting-state MRI achieved impressive classification performance (AUC = 0.963) in distinguishing patients with lung cancer-related bone metastasis from healthy individuals. Furthermore, Liu et al29 identified the left lentiform nucleus (AUC = 0.826) as an effective differentiator between herpes zoster (HZ) and postherpetic neuralgia (PHN) patients. Our study observed that FC Z-scores exhibited superior discriminative power in comparison to ALFF or ReHo values derived from individual brain regions, particularly demonstrating strong classification performance (AUC = 0.804) when measured against the right precentral gyrus. This data suggests substantial diagnostic significance with an AUC value exceeding 0.641. The precentral gyrus has frequently appeared in numerous chronic pain fMRI studies, highlighting its reduced gray matter volume and abnormal FC42–44. Additionally, the analgesic effects of high-frequency (>5Hz) repeated TMS (rTMS) on the motor cortex of the precentral gyrus (area M1) are well-documented for treating neuropathic pain, establishing a high level of clinical efficacy. The influential role of the precentral gyrus extends to the management of cancer pain, as evidenced by cases reported by Julien Nizard et al in 201546. In these instances, refractory CP patients receiving high-frequency rTMS targeting the M1 area demonstrated significant decreases in NRS scores, as well as reductions in opioid consumption, alongside noted improvements in anxiety and depression. This supports the notion that the precentral gyrus is critical not only for distinguishing between CP patients and healthy individuals but also plays a vital role in pain perception, regulation, and emotional processing, marking it as a promising target for future cancer pain interventions. However, it is essential to acknowledge that appropriate screening and management techniques might not benefit all cancer pain patients. Beyond neuroimaging biomarkers, innovative markers, including hematological indicators, genetic predispositions, and composite biomarkers, may play vital roles47. Hence, effectively treating and managing cancer pain continues to pose significant challenges globally.
Our research encounters several limitations: Firstly, the sample size is relatively small, necessitating future studies to corroborate our findings within broader datasets. Secondly, various influential factors exist within the CP cohort, complicating the exclusion of potential confounding variables stemming from pain medication, the cancer pathology itself, anti-tumor treatments, and social influences. Thirdly, we did not undertake a comprehensive examination of how psychological states (e.g., anxiety, depression, suicidal thoughts) and cognitive functions (e.g., attention, executive function) of CP patients impact different brain regions. Alongside the observed changes in brain network connectivity, it is plausible that cancer pain may also induce structural brain changes. Our study did not uncover any correlation between brain activity or FC and pain intensity within CP patients, a finding that may be attributed to the subjective nature of NRS scoring, which can be influenced by patients’ emotional states, education levels, language proficiency, and more48,49. This leads to potential inaccuracies in reflecting the true intensity of the patients’ pain. Furthermore, the limited number of CP patients included in this study primarily experienced mild-to-moderate pain (95.24%), which might constrict the ability to discern between varying pain intensity levels. Future endeavors should focus on a more nuanced classification of CP patients, enhance the assessment of cognitive functions and neuropsychological scales, and delve more deeply into the connections and mechanisms underlying the central nervous system through advanced neuroimaging technologies.
Conclusion
Data Sharing Statement
The datasets and codes generated for this study are available on request to the corresponding author.
Acknowledgments
This study was supported by the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (Grant Number: 2024KY564) and Chinese Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (Grant Number: 2024ZL1291). We express our gratitude to all participating patients.
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Unpacking the Complexity of Cancer Pain and the Brain: A Cheeky Close Look at New Research
Welcome, courageous readers, to another exhilarating adventure into the scintillating world of neuroscience! Buckle up as we delve into a method that sounds so sophisticated, it could probably run for office – the resting-state functional magnetic resonance imaging (rs-fMRI). You might think it’s just fancy tech for figuring out the brains of cancer pain patients, but oh boy, is there more to it than meets the eye – or at least your MRI scans.
Setting the Scene: Why the Fuss about Cancer Pain and rs-fMRI?
Now, let’s be honest. Cancer pain isn’t the most glamorous topic; it’s more like the forgotten cousin who shows up to Thanksgiving, eats all the good pie, and leaves you wondering what on earth just happened. According to studies by Liu et al., the application of rs-fMRI in cancer pain has been, shall we say, a bit restrained. They found interesting patterns, like decreased ALFF in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) among patients with bone metastases from lung cancer, reminding us that even our brains can throw a tantrum in response to pain.
Enter the Study: Participants, Procedures, and MRI Magic
Fasten your seatbelt as we jump into the nitty-gritty of research procedures! Conducted with the utmost ethical finesse (thank you, Research Ethics Committee!), researchers gathered *25* cancer pain (CP) patients and a hilariously matched group of 25 age- and gender-equivalent healthy controls. Eventually, after dodging brain metastases like they were unwanted guests, they ended up with 21 CP patients that could fit through the MRI machine without knocking over a water cooler.
During the MRI scan, participants were advised to shut their eyes and resist the urge to think about their grocery lists. Because we all know nothing distracts from a relaxing MRI experience quite like suddenly remembering your ex’s birthday.
Analyzing the Connections: ALFF, ReHo, and the Big Brain Features
What’s fascinating here is the technical jazz: they analyzed the Amplitude of Low-Frequency Fluctuation (ALFF) and Regional Homogeneity (ReHo) from those tranquil brain images. It’s like finding out that your brain has its own social network, but rather than posting vacation pictures, it’s sharing low-frequency signals of distress – because who doesn’t want to be miserable on a Friday night, right?
Digging Deeper: How Does the CP Brain Compare to the HC Brain?
Upon some statistical sleuthing, the researchers determined CP brains had abysmally lower ALFF mainly in the bilateral inferior temporal gyrus compared to their healthy pals. So, in conclusion, it’s not that cancer pain patients can’t think straight; their brains are just preoccupied looking for lost comfort in some temporal lobes.
The Future of Neuroimaging: What Lies Ahead?
Now, while this study makes significant strides in understanding cancer pain, let’s not toss confetti yet. The sample size is smaller than most of our New Year’s resolutions, and there are plenty of pesky confounding factors lurking in the background like a bad sequel. Painkillers, social influences, and psychological angles were left underexplored, making this a tantalizing appetizer rather than the full buffet of understanding cancer pain.
Conclusion: More Than Just a Blip on the MRI
This groundbreaking yet cheeky dive into the world of cancer pain reveals not just the neural networks of suffering but opens the door to potential future treatments. Imagine finding a pain relief method so effective it silences even the most persistent type of pain; we could be looking at the ultimate MRI after-party.
In summary, the research does an excellent job of suggesting further explorations into hematological indicators, genetic markers, and perhaps the psychological profile of patients experiencing cancer pain. And while the current technology showcases an advance in our understanding, the true magic lies ahead – where further studies can turn pain into a whisper rather than a scream.
That’s a wrap, folks! Until next time, keep your brains intrigued and your pain down to a minimum!
How do brain connectivity patterns differ between individuals suffering from cancer pain and healthy controls, and what implications does this have for pain management strategies?
To the Healthy Brain?
It’s time to compare the brainy battlefields of our cancer pain (CP) warriors and the unsuspecting healthy controls. When the research team crunched the numbers, they found that the CP patients displayed significant changes in their brain connectivity patterns. Specifically, the ALFF analysis revealed a notable decrease in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). These findings imply that the pain process may alter the brain’s regulatory mechanisms, potentially impairing its ability to modulate pain effectively. On the other hand, the healthy controls strutted their stuff with a robust connectivity profile, highlighting how well their brains effectively circulated information. The ability to maintain homeostasis and emotional regulation in healthy individuals stood in stark contrast to the disjointed brain networks observed in CP patients, suggesting that chronic pain is not just an external experience but an internal struggle that reflects the brain’s coping mechanisms. This study doesn’t just provide fascinating insights but also opens the floodgates to a plethora of questions and potential clinical implications. If the altered connectivity correlates with the severity of pain subsided by ongoing cancer treatments, can these brain imaging markers serve as reliable indicators for therapy effectiveness? Furthermore, might therapies that enhance connectivity, like cognitive-behavioral therapy or even mindfulness approaches, reclaim the lost territories in the brain’s pain-processing centers? As we leave the realm of rs-fMRI and step back into reality, it becomes evident that recognizing emotional and cognitive aspects in managing cancer pain is crucial. It’s more than just addressing the physical sensation; it’s unlocking the brain’s adaptations to pain and ensuring that both body and mind harmonize in the healing journey. So, dear readers, as we conclude our brainy escapade through the complexities of cancer pain and its neural underpinnings, let’s remember that research like this is fundamental in redefining how we understand and approach pain management. The brain, it turns out, holds many untold stories, and with continued exploration, we might just be able to find solutions to quell the incessant battles waged within. Until next time, stay curious and keep exploring the wonders of science!What This Means: Implications and Future Directions
Conclusion: Wrapping Up Our Brainy Journey
