多发性硬化症患者在接受大剂量免疫抑制治疗和自体造血干细胞移植前后的静息态功能性核磁共振成像研究

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BACKGROUND

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by spatially and temporally distributed multifocal inflammatory demyelinating lesions in the central nervous system (CNS) and usually affects people of working age [1].

In MS, the blood–brain barrier is damaged, multifocal inflammation and reactive gliosis develop, and the myelin sheath of axons undergoes degrees of destruction [1].

Clinically, MS is manifested by reduced motor activity (e.g., unsteady gait, dysmetria, and intention tremor), reduced visual acuity to complete blindness, ophthalmoplegia, nystagmus, hearing loss, monoparesis and hemiparesis, sensory disturbances, and cognitive dysfunction (e. g., reduced memory and attention and chronic fatigue and tiredness), leading to persistent loss of work capacity and disability of the patient [2].

Patients gradually lose their ability to work, function, and care for themselves as MS progresses. The Expanded Disability Status Scale (EDSS) is used to evaluate the patient’s condition. Additionally, it can be employed in classifying the disability level of patients from 0 (no disability) to 10 (MS as the cause of death) [3].

Currently, the 2021 McDonald criteria are used to diagnose MS [2, 3]. They are based on two characteristics: spatial distribution (presence of two or more clinical lesions and distribution of lesions in two or more CNS regions according to magnetic resonance imaging (MRI)) and temporal distribution (clinical exacerbations involving a new CNS region and new MS lesions according to MRI) [2]. Moreover, MS diagnosis includes cerebrospinal fluid evaluation and oligoclonal immunoglobulin G (IgG) testing. The diagnosis is confirmed by MRI.

Recently, special attention has been paid to assessing changes in functional connectivity between brain structures and morphological changes in these structures, associated with cognitive impairment, as the state of brain matter is crucial in predicting MS progression. This requires developing biomarkers based on advanced neuroimaging techniques such as functional magnetic resonance imaging (fMRI), which provides information about the state of brain matter and predict disease progression.

fMRI is a neuroimaging technique for examining changes in functional connectivity to diagnose various neurological disorders based on changes in the blood oxygenation level-dependent (BOLD) signal, which in turn is affected by changes in neuronal activity in a specific brain region [1].

There are 2 types of fMRI, resting-state fMRI and dynamic fMRI [4]. Resting-state fMRI is used to investigate functional brain connectivity, whereas dynamic fMRI measures brain activation in response to specific tasks performed by patients during the examination. The tasks are divided into several categories. The first category includes sensorial and active motor tasks, such as flexion and extension of four fingers of the dominant hand, flexion and extension of the foot, manipulation of objects used daily, and opposition of the index finger to the thumb of the dominant hand. The second category involves active cognitive tasks, such as the Paced Auditory Serial Addition Test (PASAT) and Paced Visual Serial Addition Test (PVSAT), and immediate and delayed memory tasks [5–9].

Functional brain connectivity changes visualized by resting-state fMRI can be used as biomarkers of cognitive impairment, as this criterion reflects the coherence of biological neural networks.

Networks are classified as follows: default mode network (DMN), salience detection network (SDN), and central executive network (CEN) [3, 10].

Each network has its topography and scope. The DMN, which is less active during performing complex tasks requiring increased attention, involves several anatomical regions, including the ventromedial prefrontal, dorsomedial prefrontal, lateral medial, and posterior cingulate cortices [11]. The SDN acts as a filter for the most critical stimuli and provides the ability to focus on a specific intellectual task. It is located in the anterior insula and dorsal anterior cingulate gyrus. The CEN integrates the dorsolateral prefrontal cortex with the posterior parietal cortex and processes incoming information, maintaining attention, manipulating memory, and performing tasks requiring cognitive effort.

Several studies have reported that changes in the functional connectivity of the brain’s neural networks indicate disease progression, treatment efficacy, and neurorehabilitation [3, 12, 13]. In the future, assessment of functional brain connectivity may be beneficial in developing personalized treatment and rehabilitation approaches according to functional impairment severity [12].

The current approach to MS treatment includes three components: attack therapy, disease-modifying drugs (DMTs), and symptomatic therapy; however, many studies focused on finding new treatment options for MS patients [2]. High-dose immunosuppressive therapy with autologous hematopoietic cell transplantation (HDIT/AHSCT) plays a role in achieving stable long-term remissions [14, 15].

The treatment involves several steps: mobilization and collection of hematopoietic stem cells (HSCs) from the patient’s bone marrow, cryopreservation and storage of the HSC graft, high-dose immunosuppression (conditioning regimen), and re-infusion of the thawed HSC graft 48 hours after the last dose of the cytostatic agent [16].

This study aimed to determine changes in functional connectivity of neural networks in MS patients before and after HDIT/AHSCT using resting-state fMRI findings.

MATERIALS AND METHODS

Resting-state fMRI findings in MS patients before and after HDIT/AHSCT were evaluated. The study included 25 patients with confirmed MS diagnosis. Each patient underwent comprehensive MRI at two time points (pre- and post-HDIT/AHSCT) 12 months apart, including structural MRI to rule out abnormal brain lesions (other than MS lesions) and resting-state fMRI to assess functional brain connectivity.

A 3.0 Tesla tomograph was used for MRI. Patients were instructed to lie open-eyed (not asleep) and not to fixate their gaze during scanning. A T1-weighted gradient-echo MP-RAGE pulse sequence was utilized to align fMRI images with anatomical brain structures (Table 1).

 

Table 1. MRI protocol

Таблица 1. Протокол МРТ-исследования

No.	MRI sequence	Scan time	Characteristics of sequences
1	t2_tse_tra_320_p2	2 min 30 sec	FOV 220 × 220 mm, slice thickness 4.0 mm, TR 6,000 ms, TE 93 ms, matrix 320 × 320, number of slices 27
2	t2_tirm_tra_dark-fluid	4 min 30 sec	FOV 199 × 220 mm, slice thickness 4.0 mm, TR 9,000 ms, TE 93 ms, matrix 256 × 232, number of slices 27
3	Т 1 ВИ (MPRAGE)	9 min	FOV 240 × 256 mm, slice thickness 1.2 mm, TR 2,300 ms, TE 3 ms, matrix 256 × 240, number of slices 160
4	gre_field_mapping	1 min 30 sec	FOV 192 × 192 mm, slice thickness 1.2 mm, TR 400 ms, TE 7.4 ms, matrix 64 × 64, number of slices 36
5	ep2_120_bold_Rest	6 min	FOV 192 × 192 mm, slice thickness 4.5 mm, TR 3,000 ms, TE 30 ms, matrix 64 × 64, number of slices 36

 

Furthermore, a clinical neurological examination using a classic method generally accepted in neurology was performed, including an objective assessment of the neurological status and evaluation of the patient using EDSS.

The inclusion criteria for HDIT/AHSCT were as follows:

1. Age 18–65 years and confirmed MS diagnosis (EDSS1.0–6.5)
2. Confirmed MS progression despite standard therapy; EDSS worsening of ≥1 from a baseline of <5, and EDSS worsening of ≥0.5 from a baseline of >5
3. New (including Gd+) MS lesions according to MRI findings
4. No serious comorbidities
5. No treatment with interferons and immunosuppressants within the last 3 months.

Based on HDIT/AHSCT findings, patients were divided into two groups: Group 1 (n = 18; favorable outcome group), patients with complete remission within one year, and Group 2 (n = 7, unfavorable outcome group), patients with at least one attack within one year of treatment.

The functional connectivity of the neural networks was evaluated using the CONN toolbox v20a based on SPM 12. The groups were compared to identify individual areas of altered functional connectivity of resting-state neural networks.

FINDINGS

When the resting-state fMRI findings were compared between the two groups, changes in functional activity were found in several brain regions believed to be responsible for the clinical differences between the study groups.

Changes were found in the following neural networks: DMN, sensorimotor neural network, dorsal attention network, frontoparietal network, and anterior and posterior cerebellar neural networks (Table 2).

 

Table 2. Clusters of the default mode network of the brain

Таблица 2. Кластеры сети пассивного режима работы мозга

Cluster No.	Position (x, y, z)	Size of Cluster	FWR	FDR	p-unc	peak p-FWE	peak p-unc	Location of cluster
1	+04, +60, +24	344	0.000000	0.000000	0.000000	0.000387	0.000000	Right frontal lobe, right superior frontal gyrus, left superior frontal gyrus, left frontal lobe, right paracingulate gyrus
2	–20, –70, +00	121	0.002707	0.002514	0.000055	0.477232	0.000003	Left lingual gyrus, left intracalcarine cortex, left occipital fusiform gyrus
3	+18, –50, –42	112	0.004425	0.002743	0.000090	0.123083	0.000001	Right cerebellar hemisphere
Note. FWR — Family-Wise Error Rate, p-value at the family-wise error rate; FDR — False Discovery Rate, p-value at the false discovery rate; p-unc — uncorrected p-value; peak p-FWE — peak p-value at the family-wise error rate; peak p-unc — peak uncorrected p-value

 

DMN Evaluation

DMN was the first resting-state neural network to be compared. This is the brain’s “true” resting-state network of because it is evaluated in the absence of cognitive and motor paradigms in the patient. Several studies showed that impaired functional connectivity in this network indicate the presence of cognitive and affective disorders [7, 17]. Moreover, it was found that the process of patient disability is correlated with the disruption of the main resting neural network.

Comparison groups for changes in functional connectivity for this network showed an increase in clusters in the prefrontal region in group 1 (the successful transplant group). This shows the restoration of connections in this neural network caused by the pool of mirror/silent neurons in the prefrontal region.

Increased functional connectivity of the superior frontal gyri bilaterally (Fig. 1) and diffusely in the frontal lobes leads to changes in the clinical picture with decreased severity of affective disorders in patients and improved Symbolic Digit Modalities Test (SDMT) score.

 

Fig. 1. Cluster N 1 of the default mode network

Рис. 1. Кластер № 1 сети пассивного режима

 

The cluster (Fig. 2) showed increased activity in the lingual gyrus. These changes also improve the clinical testing results and, most importantly, the patients’ quality of life. The increase in the area of the detected cluster suggests a high potential for restoring the brain’s main resting-state neural network, as some of the cells in this area are associated with the medial prefrontal cortex. These changes indicate the cause of the positive change in the patients’ mood, level of cognitive capabilities, and some motor functions.

 

Fig. 2. Cluster N 2 of the default mode network

Рис. 2. Кластер № 2 сети пассивного режима

 

As for the functional affiliation of the detected clusters, then sections 2 and 3 of the cluster (Fig. 2, 3), associated with the DMN, are located in areas close to the structures involved in the formation of this neural network, indicating the restoration of functional connectivity of the neural network itself. Based on this phenomenon, we can potentially expect to see improved memory test scores, attention, psychological components, reduced severity of motor dysfunction due to improved action planning, and increased quality of life.

 

Fig. 3. Cluster N 3 of the default mode network

Рис. 3. Кластер № 3 сети пассивного режима

 

Our finding of increased functional connectivity of this neural network in patients who were in remission for more than one year after therapy suggests a reparative potential, possibly based on neuroplasticity or neurogenesis.

Evaluation of the sensorimotor neural network

A sensorimotor neural network (SNN) is a resting-state neural network that is the main network when performing any motor action (Table 3).

 

Table 3. Clusters of the sensorimotor neural network

Таблица 3. Кластеры сенсомоторной нейросети

Cluster No.	Position (x, y, z)	Size of Cluster	FWR	FDR	p-unc	peak p-FWE	peak p-unc	Location of cluster
1	+06, +58, +22	371	0.000000	0.000000	0.000000	0.006336	0.000000	Right frontal lobe, left frontal lobe, left superior frontal gyrus, right superior frontal gyrus, left paracingulate gyrus, right paracingulate gyrus
2	+36, +46, +14	190	0.000100	0.000069	0.000002	0.974484	0.000027	Right frontal lobe, right middle frontal gyrus
3	–50, +08, –42	141	0.001061	0.000382	0.000022	0.840376	0.000011	Left temporal lobe
4	+48, –46, +12	140	0.001117	0.000382	0.000023	0.776472	0.000008	Right angular gyrus, right middle temporal gyrus, right temporo-occipital region, right posterior part of supramarginal gyrus
Note. FWR — Family-Wise Error Rate, p-value at the family-wise error rate; FDR — False Discovery Rate, p-value at the false discovery rate; p-unc — uncorrected p-value; peak p-FWE — peak p-value at the family-wise error rate; peak p-unc — peak uncorrected p-value

 

When evaluating clusters 1 and 2 of the sensorimotor neural network, the increased functional connectivity in the frontal lobe area was noted (Fig. 4, 5).

 

Fig. 4. Cluster N 1 of the sensorimotor neural network

Рис. 4. Кластер № 1 сенсомоторной нейросети

 

Fig. 5. Cluster N 2 of the sensorimotor neural network

Fig. 5. Cluster N 2 of the sensorimotor neural network

 

Increased functional connectivity was found in the left temporal lobe (Fig. 6). Note that the change in functional connectivity occurs in the gray matter and the area of increased activity is quite large.

 

Fig. 6. Cluster N 3 of the sensorimotor neural network

Рис. 6. Кластер № 3 сенсомоторной нейросети

 

Cluster 4 (Fig. 7) showed increased functional connectivity in the angular gyrus. The angular gyrus is a brain region located primarily in the anterolateral region of the parietal lobe. Its role is associated with the transmission of visual information to Wernicke’s area for the acquisition of written language.

 

Fig. 7. Cluster N 4 of the sensorimotor neural network

Рис. 7. Кластер № 4 сенсомоторной нейросети

 

The SNN also showed a positive increase in functional connectivity in the areas of the SNN’s structures as well as in the left frontal and temporal lobes (all patients were right-handed). This suggests that the patients have increased control over their own movements and that the areas responsible for motor skill acquisition are being restored.

DISCUSSION

MS is a chronic and potentially disabling disease that requires early diagnosis and appropriate treatment.

The current standard of care for MS patients is corticosteroids and DMTs. However, these agents do not provide a complete stable remission, so researchers are looking for new treatment methods, and HDIT/AHSCT is one of these new treatment options. The literature shows that HDIT/AHSCT reduces the annual attack rate, which is consistent with our data (more than 70% of patients achieved complete remission within 1 year).

fMRI is a potential technique for evaluating treatment and rehabilitation. In the present study, resting-state fMRI was used to assess the functional connectivity of the DMN and SNN. The neural networks described above showed the presence of clusters of increased functional connectivity in clinically significant regions of the brain in patients with a successful outcome of HDIT/AHSCT (e.g., in the frontal lobes, angular and lingual gyri). This correlated with improved SDMT scores and reduced severity of affective disorders. However, further magnetic resonance imaging with resting-state fMRI and neurological monitoring over time are warranted to compare MRI with clinical and neurological findings, and a larger patient population should be evaluated.

CONCLUSION

Resting-state fMRI allows quantitative and objective detection of changes in the functional connectivity of neural networks in patients with successful outcome of high-dose immunosuppressive therapy with autologous HSC transplantation.

Further evaluation of the functional connectivity of neural networks in this patient population may enable identifying functional markers that could predict the HDIT/AHSCT outcome. This requires targeted exploration of the brain’s neural networks using resting-state fMRI and dynamic MRI.

作者简介

Elena A. Potemkina

Almazov Research Center of the Ministry of Health of the Russian Federation

Email: lenagorbunova-124@yandex.ru
ORCID iD: 0000-0003-3987-9916
Scopus 作者 ID: 57217020760
Researcher ID: ABF-8381-2021
俄罗斯联邦, Saint Petersburg

Artem G. Trufanov

Military Medical Academy; Pavlov First Saint Petersburg State Medical University

Email: trufanovart@gmail.com
ORCID iD: 0000-0003-2905-9287

MD, Dr. Sci. (Medicine), Professor at the Department

俄罗斯联邦, Saint Petersburg; Saint Petersburg

Alexander Yu. Efimtsev

Almazov Research Center of the Ministry of Health of the Russian Federation

Email: atralf@mail.ru
ORCID iD: 0000-0003-2249-1405
SPIN 代码: 3459-2168
Scopus 作者 ID: 56807130100
Researcher ID: L-1124-2015

MD, Dr. Sci. (Medicine), Associate Professor at the Department

俄罗斯联邦, Saint Petersburg

Aleksey Yu. Polushin

Pavlov First Saint Petersburg State Medical University

Email: alexpolushin@yandex.ru
ORCID iD: 0000-0001-8699-2482

MD, Cand. Sci. (Medicine)

俄罗斯联邦, Saint Petersburg

Victoria V. Volgina

Almazov Research Center of the Ministry of Health of the Russian Federation

Email: volginaviktoria1@gmail.com
ORCID iD: 0009-0003-1517-8709
俄罗斯联邦, Saint Petersburg

Yana A. Filin

Almazov Research Center of the Ministry of Health of the Russian Federation

编辑信件的主要联系方式.
Email: filin_yana@mail.ru
ORCID iD: 0009-0009-0778-6396
俄罗斯联邦, Saint Petersburg

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