Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study

The study aimed at mapping (i) the distributed electroencephalographic (EEG) sources specific for mild Alzheimer's disease (AD) compared to vascular dementia (VaD) or normal elderly people (Nold) and (ii) the distributed EEG sources sensitive to the mild AD at different stages of severity. Resting EEG (10-20 electrode montage) was recorded from 48 mild AD, 20 VaD, and 38 Nold subjects. Both AD and VaD patients had 24-17 of mini mental state examination (MMSE). EEG rhythms were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical EEG sources were modeled by low resolution brain electromagnetic tomography (LORETA). Regarding issue i, there was a decline of central, parietal, temporal, and limbic alpha 1 (low alpha) sources specific for mild AD group with respect to Nold and VaD groups. Furthermore, occipital alpha 1 sources showed a strong decline in mild AD compared to VaD group. Finally, distributed theta sources were largely abnormal in VaD but not in mild AD group. Regarding issue ii, there was a lower power of occipital alpha 1 sources in mild AD subgroup having more severe disease. Compared to previous field studies, this was the first investigation that illustrated the power spectrum profiles at the level of cortical (macroregions) EEG sources in mild AD patients having different severity of the disease with respect to VaD and normal subjects. Future studies should evaluate the clinical usefulness of this approach in early differential diagnosis, disease staging, and therapy monitoring.     

Donepezil effects on sources of cortical rhythms in mild Alzheimer's disease: Responders vs. Non-Responders

Acetylcholinesterase inhibitors (AChEI) such as donepezil act in mild Alzheimer's disease (AD) by increasing cholinergic tone. Differences in the clinical response in patients who do or do not benefit from therapy may be due to different functional features of the central neural systems. We tested this hypothesis using cortical electroencephalographic (EEG) rhythmicity. Resting eyes-closed EEG data were recorded in 58 mild AD patients (Mini Mental State Examination [MMSE] range 17-24) before and approximately 1 year after standard donepezil treatment. Based on changes of MMSE scores between baseline and follow-up, 28 patients were classified as "Responders" (MMSEvar >or=0) and 30 patients as "Non-Responders" (MMSEvar <0). EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical EEG sources were studied with low-resolution brain electromagnetic tomography (LORETA). Before treatment, posterior sources of delta, alpha 1 and alpha 2 frequencies were greater in amplitude in Non-Responders. After treatment, a lesser magnitude reduction of occipital and temporal alpha 1 sources characterized Responders. These results suggest that Responders and Non-Responders had different EEG cortical rhythms. Donepezil could act by reactivating existing yet functionally silent cortical synapses in Responders, restoring temporal and occipital alpha rhythms.     

Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.     

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

Grouping of MEG gamma oscillations by EEG sleep spindles

Studies have revealed an association between EEG sleep spindles and processing of memories during sleep. Here we investigated whether there is a temporal relation between sleep spindles and MEG oscillatory activity in the gamma frequency band (> 30 Hz) which is considered to reflect local cortical processing of memory representations. MEG and simultaneous EEG (at Cz) were obtained in subjects during sleep together with standard polysomnography. As expected EEG spindles were correlated with power increases in MEG spindle (12.5-15.5 Hz) power mainly over prefrontal and occipital cortical areas. During EEG spindles we revealed both transient significant increases and decreases in MEG power, with decreases occurring significantly more often than increases. The modulations in gamma power occurred mainly at sites of increased MEG spindle power, and more often during peaks than troughs within the EEG spindle cycle. Cross-frequency coherence analyses confirmed a strong phase-coupling of gamma band activity with the spindle rhythm. The findings are consistent with the idea that spindles provide a fine-tuned temporal frame for integrated cortical memory processing during sleep.     

3.0T高分辨率磁共振血管壁成像及伪连续式动脉自旋标记技术在单侧烟雾病中的应用

目的 探讨3.0T高分辨率磁共振(HR-MRI)血管壁成像及伪连续式动脉自旋标记(3D-pCASL)技术诊断及评估单侧烟雾病(MMD)的应用价值。方法 对经DSA确诊的17例单侧MMD患者行3D-TOF MRA、HR-MRI及3D-pCASL检查。分析单侧MMD患者大脑中动脉(MCA)狭窄段管壁外缘面积,狭窄类型(偏心或向心性),管壁信号强度(均匀或不均匀)及狭窄段周围血管,评估单侧MMD脑血流灌注情况。结果 单侧MMD患者受累侧MCA狭窄段管壁外缘面积为(2.17±0.39)mm²,镜像侧为(6.11±0.37)mm²,二者差异有统计学意义(t=31.52,P<0.001)。17例单侧MMD患者均表现为单一受累MCA管腔均匀缩小或闭塞,管壁信号强度一致,狭窄段周围出现2个以上流空血管影,累及同侧颈内动脉(ICA)末端。受累侧MCA供血区(额颞叶)CBF值为(24.76±10.86)ml/min/100 g,低于镜像侧的(59.84±9.72)ml/min/100 g,差异有统计学意义(t=7.89,P<0.001),受累侧枕叶CBF值为(47.59±7.26)ml/min/100 g,镜像侧为(48.30±9.06)ml/min/100 g,二者差异无统计学意义(t=0.70,P=0.49)。结论 单侧MMD患者的HR-MRI表现具有一定的特征性,可提示诊断。3D-pCASL技术能反映单侧MMD患者脑血流灌注的基本情况,可用于临床评估与长期随访。     

Identifying reliable independent components via split-half comparisons

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.     

Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.     

Spatial and temporal EEG dynamics of motion sickness

This study investigates motion-sickness-related brain responses using a VR-based driving simulator on a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations to induce motion sickness in a manner that is close to that in daily life. Subjects' brain dynamics associated with motion sickness were measured using a 32-channel EEG system. Their degree of motion sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop behavioral references to the recorded EEG changes. The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes. The decomposition enables the brain dynamics that are induced by the motion of the platform and motion sickness to be disassociated. Five MS-related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta and delta bands; the occipital midline components exhibited a broadband power increase. Further, time series cross-correlation analysis was employed to evaluate relationships between the spectral changes associated with different brain processes and the degree of motion sickness. According to our results, it is suggested both visual and vestibular stimulations should be used to induce motion sickness in brain dynamic studies.     

脑磁图在癫(癎)诊断和治疗中的应用

目的应用脑磁图(MEG)技术对致灶进行定位,比较其与头皮脑电图(EEG)在神经影像学方法的应用价值,探讨MEG技术对癫灶定位的应用前景。方法对113例癫患者进行手术治疗,术前均通过临床症候学、头皮EEG、MRI、MEG检查,进行MEG与其他检查方法和临床症候学在定侧、定叶诊断的对比研究。手术在皮层EEG及脑深部EEG监测下进行,手术治疗结果以Engel疗效分级评价。所有手术标本常规行光镜检查。结果113例患者中MEG定位局限于单个叶的为91例,头皮EEG仅为30例。术前致灶定位依据多种检查结果和临床症候学综合定位。MEG与MRI、临床症候学在定侧诊断准确性方面比较差异有显著性。MEG与MRI在定叶诊断准确性方面的比较差异有显著性。36例术前头皮EEG表现为双侧或全导癫波的癫患者,其中有34例MEG表现为单侧癫波,具有定侧诊断的意义。结论头皮EEG、MRI、正电子发射计算机断层扫描(PET)、临床症候学均不足以做出独立精确的致灶定位诊断,综合比较MEG比上述方法定侧和定叶的准确性高。MEG空间分辨率、时间分辨率高,有助于区分致灶和镜灶。     

Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements

Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the “idling state” of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in a mixed block and event-related design. In this study, 13 subjects performed tasks of right-hand, right-foot or left-hand movement and motor imagery when EEG and fMRI data were separately collected. Task-induced increase of BOLD signal and decrease of EEG amplitudes in alpha and beta bands were shown to be co-localized at the somatotopic sensorimotor cortex. At the corresponding regions, the reciprocal changes of the two signals co-varied in the magnitudes across imagination and movement conditions. The spatial correspondence and negative covariation between the two measurements were further shown to exist at somatotopic brain regions associated with different body parts. These results suggest an inverse functional coupling relationship between task-induced changes of BOLD and low-frequency EEG signals.     

Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy

We used simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) and EEG-near infrared spectroscopy (NIRS) to investigate whether changes of the posterior EEG alpha rhythm are correlated with changes in local cerebral blood oxygenation. Cross-correlation analysis of slowly fluctuating, spontaneous rhythms in the EEG and the fMRI signal revealed an inverse relationship between alpha activity and the fMRI-blood oxygen level dependent signal in the occipital cortex. The NIRS-EEG measurements demonstrated a positive cross-correlation in occipital cortex between alpha activity and concentration changes of deoxygenated hemoglobin, which peaked at a relative shift of about 8 s. Our data suggest that alpha activity in the occipital cortex is associated with metabolic deactivation. Mapping of spontaneously synchronizing distributed neuronal networks is thus shown to be feasible.     

Mu rhythm: State of the art with special focus on cerebral palsy

Various specific early rehabilitation strategies are proposed to decrease functional disabilities in patients with cerebral palsy (CP). These strategies are thought to favour the mechanisms of brain plasticity that take place after brain injury. However, the level of evidence is low. Markers of brain plasticity would favour validation of these rehabilitation programs. In this paper, we consider the study of mu rhythm for this goal by describing the characteristics of mu rhythm in adults and children with typical development, then review the current literature on mu rhythm in CP. Mu rhythm is composed of brain oscillations recorded by electroencephalography (EEG) or magnetoencephalography (MEG) over the sensorimotor areas. The oscillations are characterized by their frequency, topography and modulation. Frequency ranges within the alpha band (∼10 Hz, mu alpha) or beta band (∼20 Hz, mu beta). Source location analyses suggest that mu alpha reflects somatosensory functions, whereas mu beta reflects motor functions. Event-related desynchronisation (ERD) followed by event-related (re-)synchronisation (ERS) of mu rhythm occur in association with a movement or somatosensory input. Even if the functional role of the different mu rhythm components remains incompletely understood, their maturational trajectory is well described. Increasing age from infancy to adolescence is associated with increasing ERD as well as increasing ERS. A few studies characterised mu rhythm in adolescents with spastic CP and showed atypical patterns of modulation in most of them. The most frequent findings in patients with unilateral CP are decreased ERD and decreased ERS over the central electrodes, but atypical topography may also be found. The patterns of modulations are more variable in bilateral CP. Data in infants and young children with CP are lacking and studies did not address the questions of intra-individual reliability of mu rhythm modulations in patients with CP nor their modification after motor learning. Better characterization of mu rhythm in CP, especially in infants and young children, is warranted before considering this rhythm as a potential neurophysiological marker of brain plasticity.     

彩色多普勒超声在外周、腹腔动脉支架植入术后监测中的价值

目的探讨彩色多普勒超声在颈动脉、椎动脉、锁骨下动脉、无名动脉、肾动脉、髂动脉支架植入术后监测中的价值。方法以高、低频探头观察31例因外周、腹腔动脉狭窄性病变、动静脉瘘行动脉裸支架或覆膜支架植入患者（植入37个支架）的支架有无术后再狭窄或闭塞。结果在二维声像图上不易分辨裸支架和覆膜支架。彩色多普勒超声可发现双侧肾动脉起始段支架植入、锁骨下动脉起始部支架植入术后再狭窄和椎动脉起始部植入支架阻塞各1例，颈动脉支架植入术后狭窄3例，支架植入术后再狭窄率为16．2％（6／37），支架植入术后阻塞率为2．7％（1／37）。结论彩色多普勒超声可作为颈动脉、椎动脉、锁骨下动脉、无名动脉、肾动脉、髂动脉支架植入术后监测的首选影像学手段。     

彩色多普勒超声评价颈内-外动脉侧支循环对颈总动脉血流动力学的影响

目的探讨颈内动脉（ICA）重度狭窄或闭塞时颈内-外动脉侧支循环（IEACC）是否开放对患侧颈总动脉（CCA）血流动力学的影响。方法选择临床及影像资料完整，单侧ICA颅外段狭窄70％～99％（74例）、闭塞（86例），且健侧ICA及双侧CCA正常或狭窄程度〈50％的患者。按IEACC是否开放将患者分为ICA狭窄70％-99％IEACC开放组（42例）和IEACC未开放组（32例），闭塞IEACC开放组（43例）和IEACC未开放组（43例）。采用彩色多普勒超声测量CCA和颈外动脉（ECA）的收缩期峰值流速（PSV）、舒张末期流速（EDV）、平均流速（MV）、阻力指数（RI），计算CCA的血流量（BFV）。分别比较ICA狭窄70％-99％及闭塞时，IEACC是否开放对患侧CCA血流动力学的影响，分析ECA在IEACC开放后患侧、健侧血流动力学的变化特点。结果（1）ICA狭窄70％-99％组与闭塞组IEACC开放者患侧CCA的EDV、BFV高于IEACC未开放者，EDV分别为（15±5）cm／s、（12±5）cm／s（P=0．010）与（14±6）cm／s、（10±6）cm／s（P=0．002），BFV分别为（676±271）ml／min、（557±188）ml／min（P=0．036）与（606±218）ml／min、（488±180）ml／min（P=0．035），而PSV、MV、RI差异均无统计学意义；（2）ICA狭窄70％～99％组与闭塞组IEACC开放者比较，患侧CCA的PSV、EDV、MV、RI、BFV差异均无统计学意义；IEACC未开放患者比较，70％～99％组的EDV及BFV高于闭塞组，分别为（12±5）cm／s、（10±6）cm／s（P=0．014）与（557±188）ml／min、（488±180）ml／min（P=0．047）；（3）ICA狭窄70％～99％组及闭塞组IEACC开放者患侧ECA的PSV、EDV、MV高于健侧，分别为（124±35）cm／s、（107±22）cm／s（P〈0．001），（34±12）cm／s、（22±8）cm／s（P〈0．001）与（64±18）cm／s、（50±11）cm／s（P〈0．001）；RI值患侧低于健侧，分别为0．72±0．07、0．79±0．07（P〈0．001）；IEACC未开放?     

Cortical dynamics of human scalp EEG origins in a visually guided motor execution

The EEG mu rhythm is often used as an index of activation in the sensorimotor cortex. However, the blur caused by volume conduction makes it difficult to identify the exact origin of the EEG rhythm in the brain using only the human scalp EEG. In this study, simultaneous fMRI and EEG measurements were performed during a visually guided motor execution task in order to investigate whether the mu rhythm in the scalp EEG is an indication of the activity in the sensorimotor cortex. In addition, a new method was proposed for reconstruction of the cortical EEG activity through the fusion of fMRI and EEG data. A suppression of mu rhythm appeared around the lateral central electrode sites, just above the sensorimotor cortex, in association with the activity in that region. During a visually guided motor execution task, the alpha rhythms at the occipital electrode sites and the alpha rhythm at the central electrode sites also showed a correlation with the fMRI signal in the occipital and the supplementary motor cortices, respectively. This method allows the investigation of the scalp EEG origin with the spatial precision of fMRI, while retaining dynamic properties of the cortex with the temporal precision of EEG.     

Localization of abnormal discharges causing insular epilepsy by magnetoencephalography

The insula, one of the five cerebral lobes of the brain, is located deep within the brain and lies mainly beneath the temporal lobe. Insular epilepsy can be easily confused and misdiagnosed as temporal lobe epilepsy (TLE) because of the similar clinical symptoms and scalp electroencephalography (EEG) findings due to the insula location and neuronal connections with the temporal lobe. Magnetoencephalography (MEG) has higher sensitivity and spatial resolution than scalp EEG, and thus can often identify epileptic discharges not revealed by scalp EEG. Simultaneous scalp EEG and MEG were performed to detect and localize epileptic discharges in two patients known to have insular epilepsy associated with cavernous angioma in the insula. Epileptic discharges were detected as abnormal spikes in the EEG and MEG findings. In Patient 1, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe, similar to TLE. In contrast, the sources of all MEG spikes detected only by MEG (M-spikes) were adjacent to the insular lesion. In Patient 2, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe. These findings indicate that MEG allows us to detect insular activity that is undetectable by scalp EEG. In conclusion, simultaneous EEG and MEG are helpful for detecting spikes and obtaining additional information about the epileptic origin and propagation in patients with insular epilepsy.     

MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus

The 170-ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously during the presentation of unfamiliar faces. An Independent Component Analysis (ICA) was applied to the data prior to localization. N170 and M170 were then modelled with a pair of dipoles in a four-shell ellipse (EEG)/homogeneous sphere (MEG) arranged symmetrically across midline. The dipole locations were projected onto the individual structural MR brain images. Dipoles were localized in fusiform gyri in ten out of eleven individuals for EEG and in seven out of eleven for MEG. N170 and M170 were co-localized in the fusiform gyrus in six individuals. The ICA shifted some of the single-subject dipoles up from cerebellum to fusiform gyrus mainly due to the removal of cardiac activity. The group mean dipole locations were also found in posterior fusiform gyri, and did not differ significantly between EEG and MEG. The result was replicated in a repeated measurement 3 months later.     

单侧和双侧颈内动脉狭窄或闭塞侧支循环途径的研究

目的研究单侧和双侧颈内动脉狭窄或闭塞的侧支循环方式。方法经DSA、MRA确诊的颈内动脉狭窄或闭塞者95例,分为单侧和双侧病变组。分析DSA、MRA结果,对比两组病例侧支循环方式。结果DSA和MRA上,单侧病变组前交通动脉出现率明显高于双侧病变组(P<0.05),后交通动脉出现率在两组间无统计学差异(P>0.05)。DSA上,眼动脉出现率在两组间无统计学差异;双侧病变组眼动脉增粗率(57.9%)高于单侧组(32%)。双侧病变组软脑膜吻合支出现率明显高于单侧组(P<0.01)。结论单侧和双侧颈内动脉狭窄或闭塞的侧支方式不同,单侧病变主要通过前交通动脉、同侧后交通动脉等代偿,双侧病变主要侧支是后交通动脉、眼动脉和软脑膜吻合支。