Lever positioning manipulation alters real-time brain activity in patients with lumbar disc herniation: An amplitude of low- frequency fluctuation and regional homogeneity study

doi:10.21203/rs.3.rs-2866494/v1

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Lever positioning manipulation alters real-time brain activity in patients with lumbar disc herniation: An amplitude of low- frequency fluctuation and regional homogeneity study

https://doi.org/10.21203/rs.3.rs-2866494/v1

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Introduction

Lumbar disc herniation (LDH) is the preeminent disease of lever positioning manipulation (LPM), a complex disorder involving alterations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has the advantages of non-trauma, zero radiation, and high spatial resolution, which has become an effective means to study brain science in contemporary physical therapy. Furthermore, it can better elucidate the response characteristics of the brain region of LPM intervention in LDH. We utilized two data analysis methods, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) of rs-fMRI, to assess the effects of LPM on real-time brain activity in patients with LDH.

Methods

Patients with LDH (Group 1, n = 21) and age-, gender- and education-matched healthy controls without LDH (Group 2, n = 21) were prospectively enrolled. Brain fMRI was performed for Group 1 at two-time points (TPs): before LPM (TP1) and after one LPM session (TP2). The healthy controls (Group 2) did not receive LPM and underwent only one fMRI scan. Participants in Group 1 completed clinical questionnaires assessing pain and functional disorders using a Visual Analog Scale and the Japanese Orthopaedic Association (JOA), respectively.

Results

Compared to the healthy controls (Group 2), the patients with LDH (Group 1) had significant variation in ALFF and ReHo values in brain activity. After the LPM session (TP2), Group 1 at TP1 also showed significant variation in ALFF and ReHo values in brain activity. In addition, the latter (TP2 vs TP1) showed more significant changes in brain regions than the former (Group 1 vs Group 2). The ALFF values were increased in the Frontal_Mid_R and decreased in the Precentral_L in Group 1 at TP2 compared with TP1. The Reho values were increased in the Frontal_Mid_R and decreased in the Precentral_L in Group 1 at TP2 compared with TP1. The ALFF values were increased in the Precuneus_R and decreased in the Frontal_Mid_Orb_L in Group 1 compared with Group 2. Only three brain areas with significant activity in Group 1 compared with Group 2: Frontal_Mid_Orb_L, Frontal_Sup_Orb_L, and Frontal_Mid_R. ALFF value in the Frontal_Mid_R at TP2 correlated positively with the change rates of JOA scores between TP1 and TP2 (P = 0.04, r = 0.319, R² = 0.102).

Discussion

Patients with LDH showed abnormal brain ALFF and ReHo values, which were altered after LPM. The default mode network, prefrontal cortex, and primary somatosensory cortex regions could predict real-time brain activity for sensory and emotional pain management in patients with LDH after LPM.

Biological sciences/Neuroscience

Biological sciences/Neuroscience/Neural circuit

Health sciences/Health care/Medical imaging/Magnetic resonance imaging

Health sciences/Health care/Therapeutics/Pain management

functional magnetic resonance imaging

lumbar disc herniation

Lever positioning manipulation

amplitude of low-frequency fluctuation

regional homogeneity

Lumbar disc herniation (LDH) is one of the most common diseases in orthopedics. Moreover, it can be associated with back pain, leg pain, and sciatic nerve dysfunction (Ammerman et al., 2019). This disease dramatically affects people's work and quality of life. Furthermore, severe cases can lead to disability (Gautschi et al., 2016). In addition, anxiety and depression may accompany the physical disability of patients with LDH (Engel-Yeger et al., 2018). Research shows that the incidence of LDH is 10–20% (Ramakrishnan et al., 2017). Additionally, the onset age has tended to be younger (Zhang et al., 2019). Therefore, understanding the pathogenesis of LDH and developing effective treatment strategies are critical and urgent.

Currently, a variety of therapeutic interventions have been proposed for the treatment of LDH, inclusive of non-surgical redress and surgical therapy. The survey showed that 2.7% had complications after surgery (Fjeld et al., 2019). Moreover, the 5-year reoperation rate reached 11% (Kim et al., 2019). In contrast, non-surgical treatments are increasingly supported by physicians and patients. Lever positioning manipulation (LPM) is a complementary therapy for LDH, widely used in China (Lijiang et al., 2021, 2008). Lever positioning manipulation is usually administered by a trained physician who applies a controlled force to the patient's lumbar muscles and spine using their hands for pain relief. However, the underlying mechanism of pain relief and the neuropathological regulation of the brain to LPM remains unclear.

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a great insight into fundamentally dynamic characteristics of human brains (Wang et al., 2021; Raimondo et al., 2021). Furthermore, it has been regarded as a new non-invasiveness and high spatial resolution research method for the neurological response of LDH. In recent years, much evidence about it has been arizing. For example, a study demonstrated that patients with LDH had more extensive neuronal activation in the medial ventral prefrontal cortex and sensory-motor cortex than healthy controls (HC) (Huang et al., 2019). Another study demonstrated significant alterations in Kendall's coefficient of concordance (KCC)- and coherence-regional homogeneity (ReHo) of the right cerebellum posterior lobe, brainstem, left medial prefrontal cortex, and bilateral precuneus in LDH patients (Zhou et al., 2019). In addition, another study showed that LDH patients exhibited increased temporal coupling between the thalamus and dorsolateral prefrontal cortex, which further mediates the relationship between chronic pain to depression (Li et al., 2021). However, little research focuses on brain activity alterations in patients with LDH after LPM.

Consequently, this study aimed to explore the real-time brain activity in patients with LDH after LPM. The study used the amplitude of low-frequency fluctuation (ALFF) and ReHo to reveal the differences in regional brain function. Precisely, The ALFF measures the activity of neurons in the brain that are not affected by external factors (Liao et al., 2019); The ReHo measures the consistency of the local functional activities of the brain's various areas (Tian et al., 2012). To our knowledge, this was the first rs-fMRI study based on a data analysis method combining ALFF and ReHo to examine the real-time brain activity in patients with LDH after LPM.

2.1 Participants

Twenty-one right-handed LDH patients and age-, gender- and education-matched healthy controls were recruited from our hospital from September 2021 to November 2022.

The inclusion criteria were the following: patients suffering from low back pain or radicular leg pain or both; persistent pain for at least six months that was nonresponsive to conservative treatments; at least one epidural or facet joint corticosteroid injection in the last past year without pain relief or with an improvement in pain of fewer than two months. Lumbar protrusion level discopathy on MRI corresponds to the level of pain. No interventional pain treatment was applied within the last three months. Furthermore, patients needed a pain score equal to or greater than four on the visual analog scale (VAS).

Exclusion criteria were: patients with motor deficits, systemic infection, coagulation disorders, pregnancy, VAS pain score lower than four, no pre-procedure MRI, and those who did not benefit from follow-up consultations.

We enrolled age-, gender- and education-matched healthy controls with no history of LDH (Group 2) from our hospital. The eligibility criteria were as follows:

(1) Right-handed;

(2) 18–65 years of age;

(3) No history of LDH;

(4) No pharmacotherapy, physical therapy, or LPM for pain one month prior to enrollment;

(5) Able to understand and sign the written informed consent.

The Institutional Review Board approved this research protocol (No.ZSLL-KY-2022-049-01) at The Third Affiliated Hospital of Zhejiang Chinese Medical University, P. R. China. This protocol was registered on ClinicalTrials.gov (NCT 05613179). Furthermore, it was performed following the Declaration of Helsinki. Written informed consent was obtained from all participants.

2.2 Procedure

Brain fMRI scans were obtained for patients with LDH (Group 1) at two time points (TMs): before LPM (time point 1, TP1) and within one hour after the first LPM session (time point 2, TP2).

Patients in Group 1 were also asked to complete clinical questionnaires prior to the fMRI scan at two-time points. Specifically, they were asked to score the severity of their LDH in terms of VAS (0 = no pain, 10 = strongest imaginable pain) and to complete the JOA forms. Differences in VAS and JOA scores between the two-time points were calculated.

Only one brain fMRI scan was obtained for the healthy controls (Group 2), and Group 2 participants did not complete any clinical questionnaires.

2.2.1 Lever positioning manipulation (LPM)

All LPM sessions were performed by the same rehabilitation specialist (LLJ), who has 32 years of experience. The specific lever positioning manipulation is as follows: the patient lies prone at the mouth-open position with muscles relaxed, waist exposed, knees and hips bent, and ankle joints crossed. The practitioner uses the right elbow olecranon to point at the lumbar vertebrae. Subsequently, they hold the patient's two ankle joints in both hands to cause the lumbar vertebra to make hyperextension and flexion movements through the arms and pull the patient backward and upwards hard. When pulling upwards to the trigger point, the practitioner pulls with a lever quickly with a technique and then senses a clatter or looseness at the positioning point. When the practitioner pulls with lever manipulation, the patient is asked to exhale and then inhale at the end of the manipulation (Fig. 1). Each LPM session lasted about 30 min. There were no complications from the LPM sessions in our study cohort.

2.2.2 Brain fMRI Scan

The MRIs were conducted using a GE Discovery 3.0-T MR750w scanner (General Electric, America) with a twenty-channel head coil. All the participants were instructed to reflex and lie with their eyes closed but not fall asleep. In addition, the functional images were obtained using an echo-planar imaging sequence with the following parameters: 36 slices, repetition time (TR) = 2000ms, echo time (TE) = 40ms, 3.0-mm thickness, no gap, voxel size = 3.75 mm×3.75 mm×4.0 mm, flip angle = 90°, the field of view (FOV) = 240 mm×240 mm, data matrix = 64 × 64, and 40 scan layers. The functional sequence took 8 min. Moreover, structural images were obtained using a three-dimensional turbo fast echo T₁WI sequence with the following parameters: 176 slices, TR/TE = 7.90/3.06 ms, 1.0-mm thickness, gap = 0 mm, FOV = 256 mm × 256 mm, acquisition matrix = 256 × 256, and fluorescein angiography = 10°. The structural sequence took 6 min.

2.2.3 Brain fMRI Processing

The original images were input to the offline workstation. The package for statistical parametric mapping software (SPM 12, http://www.fil.ion.ucl.ac.uk/spm/), which is run using MATLAB 2013 b, and a software package based on SPM 12 and DPARSF of RESTplusV1.2 were used.

Main steps: The first 10 images were removed to exclude the effects of the time for magnetic fields to the steady state and the volunteers' adaption to the environment.

Time correction: Aimed at reducing the differences of the moment to get each picture.

Head movement correction: This step aims to reduce the effect of signals due to the noise generated by the movement of the head. Data corresponding to the three-dimensional translation of head moving > 1 mm and three-dimensional rotation > 1° were removed. Translational and rotational head motion did not differ significantly between groups or across time points for Group 1.

Spatial normalization: The functional sequence of images of all volunteers was spatially normalized to the Montreal Neurological Institute (MNI) template to locate the active areas of the brain.

Low-pass filtering: The obtained signal was subjected to low-pass filtering by 0.01–0.08 Hz to remove the interference generated by high- and low-frequency signals.

Finally, the obtained frequency wave signals were analyzed using ALFF and ReHo.

2.2.4 Calculation of ALFF and ReHo

Functional images without filters were transformed into the frequency domain to obtain the power spectrum using a fast Fourier transform. The square root of the power spectrum was then averaged across 0.01–0.08 Hz at each voxel. Subsequently, the average values were recorded as the ALFF. Following this, the ALFF of each voxel was divided by the global mean ALFF value to obtain the standardized ALFF for subsequent statistical analysis. Additionally, data without smooth was used to calculate ReHo through KCC to measure the synchronization of the time series of a given voxel with its 26 nearest neighboring voxels. Moreover, individual ReHo maps were divided by the global mean ReHo value for standardization and then smoothed with a Gaussian kernel of 6-mm full width at half maximum.

2.2.5 Image viewing

Brain images that were statistically significant after the above processing was superimposed on standard brain images to obtain images that displayed anatomical and statistical information, including image information for the sagittal, coronal, horizontal position, or 3-Dimensional mode images. Figure plugins such as Slice Viewer and Xjview 95 were used to read the coordinates of the activated brain areas, the anatomical location, and the T values in the MNI template.

2.3 Statistical Methods

The Statistical Package for the Social Sciences 26.0 (IBM SPSS Statistics, Armonk, NY, USA) software was used for statistical analyses. Furthermore, the significance level was set at P < 0.05. We performed our initial statistical analysis using the Kruskal–Wallis H-test to assess differences in VAS scores, JOA scores, age, gender, and education. Once a statistically significant difference was detected, we proceeded with the Mann–Whitney test to determine which pairs of datasets differed significantly. Moreover, two groups (Group 1 vs Group 2) were compared using an two sample t-test. Additionally, two-time points (TP1 vs TP2) for the same group were compared using paired t-test for the normally distributed continuous variables.

Statistical comparisons of the ALFF and ReHo values were conducted through the SPM 12 software. Age, gender, and education were controlled as covariates, and the significance threshold was set at voxel-level q < 0.05 (false discovery rate corrected) with a 50-voxel extension. Furthermore, partial correlation analysis was performed to estimate the associations between functional impairments in the brain and the clinical characteristics (P < 0.05) of LDH, controlling for age, gender, and education.

3.1 Clinical Data

Patients with LDH (Group 1, n = 21) and age-, gender- and education-matched healthy controls without LDH (Group 2, n = 21) were prospectively enrolled. There were no significant differences in age (LDH: 41.28 ± 14.68 vs HC: 40.80 ± 13.72 years; P = 0.882), gender (M/F: LDH: 11/10 vs HC: 9/12, χ² test, P = 0.826) or education (LDH: 13.43 ± 4.28 vs HC: 13.28 ± 4.40; P = 0.878) between the patients and HCs. After one LPM treatment (TP1 and TP2), the VAS scores in Group 1 were significantly lower than that before LPM (TP1) (P < 0.001). There were also significant differences in JOA scores between different time points (TP1 and TP2). Specifically, the JOA score after one LPM was significantly higher than that before LPM (TP1) (P < 0.001) (Table 1).

Table 1

Participant information and clinical questionnaire scores
Group	Gender (male\female)	Age (year)	Education (year)	VAS scores	JOA scores
LDH-TP1	11/10	41.28 ± 14.68	13.43 ± 4.28	5.63 ± 1.021	12.81 ± 1.569
LDH-TP2	11/10	41.28 ± 14.68	13.43 ± 4.28	2.91 ± 0.851	17.95 ± 1.499
HC	9/12	40.80 ± 13.72	13.28 ± 4.40	1.86,0.918	18.24,1.841
t/χ²	0.382	0.022	0.024	12.555	10.283
P-value	P = 0.537^a	P = 0.882^b	P = 0.878^b	P ＜0.001^c	P ＜0.001^c
^a χ² t-test;
^b Two sample t-test between two groups (LDH vs HC);
^c Paired t-test between two-time points (TP1 vs TP2) for the same group (patients with LDH).
LDH, Lumbar disc herniation;
HC, health control;
VAS, Visual Analogue Scale;
JOA, Japanese Orthopaedic Association.

3.2 Brain fMRI Data

To examine the brain areas that differed significantly between LDH patients before and after the LPM intervention (TP1 vs TP2), we employed the paired t-test. Table 2 and Fig. 2 show brain areas with significant activity in ALFF values. Plentiful brain areas were significantly activated. The Frontal_Mid_R, which has 221 voxels and a peak of 5.02, is the most significantly enhanced. Furthermore, the Precentral_L, which has 231 voxels and a peak of -4.3711, is the most significantly decreased. Table 3 and Fig. 3 show brain areas with significant activity in ReHo values. Numerous brain areas were significantly activated. The Precentral_L has 260 voxels and a peak of -3.7401, the most significantly decreased. Furthermore, the Frontal_Mid_R has 312 voxels and a peak of 3.8558, the most significantly enhanced.

Table 2

Paired t-test Results of the ALFF Values Between Group 1 at TP2 and Group 1 at TP1
Brain regions (AAL)	Cluster size (voxels)	MNI Coordinates			Peak t-scores
		x	y	z
Frontal_Mid_R	221	-18	63	18	5.02
Frontal_Sup_Medial_R	199	-18	63	18	5.02
Frontal_Mid_L	175	-18	63	18	5.02
Cingulum_Ant_R	140	-18	63	18	5.02
Frontal_Inf_Tri_L	131	-18	63	18	5.02
Frontal_Inf_Tri_R	120	-18	63	18	5.02
Frontal_Inf_Orb_R	117	-18	63	18	5.02
Frontal_Sup_Medial_L	101	-18	63	18	5.02
Putamen_R	70	12	-27	6	4.1552
Thalamus_R	56	12	-27	6	4.1552
Precuneus_R	197	-6	-87	42	4.4355
Precuneus_L	178	-6	-87	42	4.4355
Cuneus_L	60	-6	-87	42	4.4355
Precentral_L	231	-12	-12	63	-4.3711
Supp_Motor_Area_L	98	-12	-12	63	-4.3711
Note: p < 0.05, FDR, correction AAL = Anatomical Automatic Labeling; MNI = Montreal Neurological Institute.

Table 3

Paired t-test Results of the ReHo Values Between Group 1 at TP2 and Group 1 at TP1
Brain regions (AAL)	Cluster size (voxels)	MNI Coordinates			Peak t-scores
		x	y	z
Temporal_Pole_Sup_L	132	-42	6	-27	5.6467
Temporal_Mid_L	53	-42	6	-27	5.6467
Temporal_Sup_L	53	-42	6	-27	5.6467
Frontal_Mid_R	312	33	0	-21	3.8558
Frontal_Sup_Medial_L	152	33	0	-21	3.8558
Frontal_Inf_Orb_R	146	33	0	-21	3.8558
Frontal_Sup_Medial_R	132	33	0	-21	3.8558
Frontal_Mid_L	117	33	0	-21	3.8558
Temporal_Sup_R	140	54	-48	12	3.1461
Temporal_Mid_R	82	54	-48	12	3.1461
Rolandic_Oper_R	79	69	-3	12	3.315
Hippocampus_L	52	-21	-54	27	3.0377
Temporal_Inf_R	125	48	-3	-45	-4.6446
Fusiform_R	74	48	-3	-45	-4.6446
Precentral_L	260	-39	-27	54	-3.7401
Supp_Motor_Area_L	126	-39	-27	54	-3.7401
Postcentral_L	124	-39	-27	54	-3.7401
Precentral_R	75	24	-51	54	-3.3506
Temporal_Inf_L	96	-36	6	-42	-3.7902
Note: p < 0.05, FDR correction; AAL = Anatomical Automatic Labeling; MNI = Montreal Neurological Institute.

Since the healthy controls received just one brain fMRI scan, no longitudinal comparisons were evaluated for Group 2. To examine the brain areas that differed significantly between LDH patients and HCs (Group 1 vs Group 2), we employed the two-sample t-test. Table 4 and Fig. 4 show brain areas with significant activity in ALFF values. The Precuneus_R has 136 voxels and a peak of 3.1207, which is the most significantly enhanced. Correspondingly, the Frontal_Mid_Orb_L has 155 voxels and a peak of -3.345, the most significantly decreased. Table 5 and Fig. 5 show brain areas with significant activity in ReHo values. There were only three brain areas with significant activity: Frontal_Mid_Orb_L, Frontal_Sup_Orb_L, and Frontal_Mid_R.

Table 4

Twosample t-test Results of the ALFF Values Between Group 1 and Group 2
Brain regions (AAL)	Cluster size (voxels)	MNI Coordinates			Peak t-scores
		x	y	z
Rolandic_Oper_L	57	-60	-3	9	-3.6496
Frontal_Mid_Orb_L	155	-21	54	-6	-3.345
Frontal_Sup_Orb_L	55	-21	54	-6	-3.345
Precuneus_R	136	-18	63	18	3.1207
Precuneus_L	110	-18	63	18	3.1207
Angular_R	59	-18	63	18	3.1207
Parietal_Inf_R	57	-18	63	18	3.1207
Note: p < 0.05, FDR, correction AAL = Anatomical Automatic Labeling; MNI = Montreal Neurological Institute.

Table 5

Twosample t-test Results of the ReHo Values Between Group 1 and Group 2
Brain regions (AAL)	Cluster size (voxels)	MNI Coordinates			Peak t-scores
		x	y	z
Frontal_Mid_Orb_L	84	-27	48	-6	-2.4614
Frontal_Sup_Orb_L	61	-27	48	-6	-2.4614
Frontal_Mid_R	66	36	24	48	2.3695
Note: p < 0.05, FDR correction; AAL = Anatomical Automatic Labeling; MNI = Montreal Neurological Institute.

Significant correlations of brain activity with the VAS and JOA scores change rates for Group 1 (TP1 vs TP2) were presented in Fig. 6 and Fig. 7. The ALFF value in the Frontal_Mid_R at TP2 correlated positively with the change rates of JOA scores between TP1 and TP2 (P = 0.04, r = 0.319, R² = 0.102). However, the other areas of brain activity and the clinical variables did not significantly correlate.

Our study showed that LPM achieved good clinical efficacy in treating LDH, according to the VAS and JOA scores. Clinically, the scales most commonly used to evaluate this treatment are VAS, JOA, Oswestry disability index (ODI), Self-Rating Anxiety Scale (SAS), and Self-rating depression scale (SDS), amongst others. Among them, VAS and JOA are most commonly used to evaluate the efficacy of LDH treatment, ODI is often used to evaluate before and after surgery, and SAS and SDS are often used to evaluate the emotional and mental disorders associated with this disease. Since LPM is a non-surgical treatment, the present study did not explore the factors of mental and emotional disorders. Therefore, VAS and JOA were used as the main clinical indicators to evaluate this treatment in this study. In addition, the goal of treatment for LDH is to correct the herniated disc structurally, relieve the symptoms of pain functionally, and improve the state of anxiety psychologically. Therefore, the gold standard for treating LDH is also judged around the above efficacy. Although, medical institutions often recommend surgery and medication. However, its negative effects are inevitable for doctors and patients to worry about. In contrast, LPM, a green and safe therapy of non-invasive non-drug for LDH, is more likely to be widely recognized and meets the gold standard of therapeutic approaches (Dhatt et al., 2011).

This study explored the effect of LPM on brain function in patients with LDH through fMRI technology and obtained significant results. As MRI is a sensitive imaging method, different intervention methods (Toffoletto et al., 2014), pathological states (Hauzeur et al., 1992), and measurement indicators (Takahashi and Grove, 2019) can activate different brain regions. For example, one study showed that conventional tuina therapy could activate the ALFF value of the right lingual gyri in LDH patients (Wen et al., 2022). Another study compared the brain regions of LDH patients with balance acupuncture and body acupuncture. Moreover, they found that balance acupuncture could better regulate the function of the endogenous pain regulation network, especially in the bilateral amygdala, while the brain regulation of body acupuncture was not significant (Ye and Liu, 2012). Based on the above, we believe that the significant activation of brain areas in patients with LDH is likely related to the effectiveness of treatment methods and timeliness. Lever positioning manipulation treatment of LDH is effective and highly accepted by patients. Even a single immediate treatment can comfort patients significantly (Lijiang et al., 2012). Therefore, this study focused on the real-time brain activity in patients with LDH after LPM.

This study demonstrated that the ALFF values of patients with LDH were most significantly enhanced in the Precuneus_R but most significantly decreased in the Frontal_Mid_Orb_L compared to healthy controls. The ReHo values of patients with LDH were increased in the Frontal_Mid_R. However, the Frontal_Mid_Orb_L and Frontal_Sup_Orb_L decreased when compared with healthy controls. After the one-LPM session, the degree of pain was relieved. Additionally, large brain areas were significantly activated in ALFF and ReHo values. Frontal_Mid_R is the most significantly enhanced brain area, and Precentral_L is the most significantly decreased brain area.

The Precuneus_R is part of the default mode network (DMN). Furthermore, the Frontal_Mid_Orb_L, Frontal_Mid_R, Frontal_Mid_Orb_L, and Frontal_Sup_Orb_L are part of the prefrontal cortex (PFC). Moreover, the Precentral_L is part of the primary somatosensory cortex (S1). These interconnected brain networks show that activity in the DMN, PFC, and S1 may reflect responses to pain management with LPM in patients with LDH.

The DMN, a network of brain regions active during sleep, is suppressed when specific tasks are involved (Buckner et al., 2008). Typical pain cognitive function depends on maintaining a dynamic equilibrium between the DMN and other brain systems (Uddin et al., 2009). A study by Tan et al. suggests the potential utility of the DMN as a neuroimaging biomarker for pain management in patients with LDH (Tan et al., 2020). Baliki et al. suggest that disruptions of the DMN may underlie the cognitive and behavioral impairments accompanying chronic pain (Baliki et al., 2008). Zhou et al. found that patients with discogenic low back pain and leg discomfort had decreased ALFF values in DMN areas compared to healthy controls (Zhou et al., 2018). In our study, patients with LDH showed more activity than healthy controls in the Precuneus_R. This brain area is one essential part of the DMN. Our results support the notion that in patients with LDH, the DMN contributes to the brain's mechanisms for pain regulation.

The PFC is critical for controlling emotional responses and integrating emotional information (Cabeza and Nyberg, 2000; Miller, 2000; Salzman and Fusi, 2010). When the PFC is activated, the pain signal processing rate is accelerated, thus reducing the effects of pain (Lorenz et al., 2003; Schweinhardt et al., 2009). Ivo et al.'s voxel-based morphometry study revealed significant reductions in grey matter density in the dorsolateral prefrontal cortex (dlPFC), a region connected to the processing and modulation of pain (Ivo et al., 2013). Another study by Čeko et al. found that patients with LDH showed structural changes in white matter in the dlPFC compared with healthy persons (Čeko et al., 2015). In addition, Baliki et al. found that increased frequency fluctuations within the medial PFC were temporally synchronous with spontaneous pain changes in patients during a pain-rating task (Baliki et al., 2011). According to our analysis, patients with LDH showed more significant activity in the Frontal_Mid_Orb_L, Frontal_Mid_R, Frontal_Mid_Orb_L, and Frontal_Sup_Orb_L after one LPM session. These brain areas are part of the PFC. Therefore, our findings are consistent with the idea that the PFC is a neural correlate of LDH and that it plays a role in the analgesic effects of LPM and the brain's pain-emotion-management mechanisms in people with LDH following LPM.

The S1 is one of the important centers responsible for processing exteroceptive body information (Farb et al., 2013). A study by Li et al. reported that patients with LDH showed increased gray matter volume at the bilateral S1s (Li et al., 2018). Furthermore, Wasan et al. discovered that the clinically significant exacerbation of persistent LDH was connected to significant regional blood flow increases inside S1, which is known to activate in response to experimental pain (Wasan et al., 2011). However, their study focused on the brain structure and cerebral blood flow factors of patients with LDH. In contrast, ours focused on the functional brain network of an analgesic factor after an LPM session. In our study, patients with LDH showed more significant activity in the Precentral_L after one LPM session. This brain area is one essential part of the S1. So we infer that the immediate effects of LPM are mainly embodied in the changes of activity in the cognitive and central analgesia network.

There were some limitations to this study. First, our sample size was small and heterogeneous, which hindered our ability to detect minute changes in brain activity. For example, only the ALFF value in the Frontal_Mid_R at TP2 correlated positively with the change rates of JOA scores between TP1 and TP2. The other areas of brain activity and the clinical variables did not significantly correlate, which was unexpected. The small sample size may have contributed to the lack of notable longitudinal within-group changes. It might also result from our rigorous data analysis methodology with multiple comparison corrections. Second, our trial enrolled patients as walk-ins from outpatient pain clinics. Additionally, they often experienced less severe low back pain than patients in an inpatient environment, who might have more incapacitating pain. Third, the LDH group had no long-term follow-up after LPM. Therefore, there was no information on how long the pain was relieved after LPM. Last, we only used ALFF and ReHo as outcome indicators. Although these two indicators can analyze the brain activity in patients with LDH after LPM from the perspective of the activity of neurons in the brain that are not affected by external factors and the consistency of the local functional activities of the brain's various regions, further research needs to be conducted from the perspective of functional connectivity and functional brain networks.

In the future, an additional follow-up assessment, such as a six-month interval assessment, should be obtained to evaluate changes in the brain responses identified in this study. To summarize, future research is required to increase the sample size, regulate the inclusion and exclusion standards, enhance the experimental design plan, and amplify in-depth research indicators.

In conclusion, our study identified significant ALFF and ReHo values alterations in brain activity in the DMN, PFC, and S1 regions of patients with LDH after LPM treatment. These results suggest that DMN, PFC, and S1 regions may be potential biomarkers for assessing real-time brain responses for sensory and emotional pain management.

lumbar disc herniation (LDH)

lever positioning manipulation (LPM)

resting-state functional magnetic resonance imaging (rs-fMRI)

amplitude of low-frequency fluctuation (ALFF)

regional homogeneity (ReHo)

time points (TP)

Japanese Orthopaedic Association (JOA)

Kendall's coefficient of concordance (KCC)

primary somatosensory cortex (S1)

visual analog scale (VAS)

default mode network (DMN)

prefrontal cortex (PFC)

Oswestry disability index (ODI)

self-rating anxiety scale (SAS)

self-rating depression scale (SDS)

dorsolateral prefrontal cortex (dlPFC)

healthy controls (HC)

false discovery rate (FDR)

field of view (FOV)

repetition time (TR)

echo time (TE)

Conflict of Interest

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.

Author Contributions

ZXC, LZZ, and LLJ conceived and designed the study. ZXC, HYB, WS, TY, and HHJ drafted and revised the manuscript. All authors participated in the collection and analysis of the data.

Funding

This study was supported by the General Program of National Natural Science Foundation of China (Grant number:82274672), the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (Grant No. 2022C03123).

Acknowledgments

The authors thank Min Lin, Caiyun Mou, Lihai Zhai, and Jiashan Chen for their help in the consultation of this neuroimaging trial.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

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No competing interests reported.

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Lever positioning manipulation alters real-time brain activity in patients with lumbar disc herniation: An amplitude of low- frequency fluctuation and regional homogeneity study

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Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 Participants

2.2 Procedure

2.2.1 Lever positioning manipulation (LPM)

2.2.2 Brain fMRI Scan

2.2.3 Brain fMRI Processing

2.2.4 Calculation of ALFF and ReHo

2.2.5 Image viewing

2.3 Statistical Methods

3 Results

3.1 Clinical Data

3.2 Brain fMRI Data

4 Discussion

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Conflict of Interest

Author Contributions

Funding

Acknowledgments

Data Availability Statement

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