- Email: [email protected]
Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study
brain research 1546 (2014) 27–33
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study Yu Leia, Yanjiang Lia, Wei Nia, Hanqiang Jianga, Zhong Yangb, Qihao Guoc, Yuxiang Gua,n, Ying Maoa a Huashan Hospital of Fudan University, Department of Neurosurgery, 12# Wulumuqi Zhong Road, Shanghai 200040, China b Huashan Hospital of Fudan University, Department of Radiology, 12# Wulumuqi Zhong Road, Shanghai 200040, China c Huashan Hospital of Fudan University, Department of Neurology, 12# Wulumuqi Zhong Road, Shanghai 200040, China
art i cle i nfo
ab st rac t
Article history:
Adult patients with moyamoya disease (MMD) are reported to suffer from vascular cognitive
Accepted 19 December 2013
impairment (VCI), including considerable impairment of executive function/attention. The
Available online 28 December 2013
spatial pattern of functional brain activity in adult MMD patients with VCI has not been
Keywords:
studied before and can be measured by examining the amplitude of low-frequency
Amplitude of low-frequency fluctuation
fluctuations (ALFF) of blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) during rest. Twenty-three adult patients with MMD were recruited to participate
Blood oxygen level-dependent
in this study, including 11 with VCI and 12 without VCI (NonVCI), as well as 22 healthy young
functional magnetic resonance
adults (normal control, NC). Widespread differences in ALFF were observed between the VCI/
imaging
NonVCI and NC groups in such regions as the frontal, parietal and temporal gyri, with parts
Intrinsic brain activity
of the frontal gyrus, such as the anterior cingulate cortex (ACC) and the right supplemental
Moyamoya disease
motor area (SMA), showing significant differences in ALFF. It is worth to note that regions
Vascular cognitive impairment
such as the parietal gyrus, the right superior frontal gyrus (SFG), the right superior temporal gyrus (STG) and the left caudate nucleus (CN) exhibited significant changes in ALFF during the progressive cognitive decline of MMD. Taken together, our results demonstrate that MMD exhibits a specific intrinsic pattern of ALFF and that this pattern changes with the progression of cognitive decline, providing insight into the pathophysiological nature of this disease. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Moyamoya disease (MMD) is a chronic cerebrovascular disease of uncertain cause. MMD is characterized by the progressive stenosis of the arteries of the circle of Willis, which results in the formation of a collateral network of capillaries, producing the appearance of a “puff of smoke” at the base of the brain on n
angiography (Suzuki and Kodama, 1983). Adult patients with MMD frequently suffer from vascular cognitive impairment (VCI), including considerable impairment in executive function/attention, and with the other three cognitive domains of memory, language and visuospatial functions being impaired to varying degrees (Calviere et al., 2012; Karzmark et al., 2012; Su et al., 2013; Weinberg et al., 2011). The cognitive domains impaired have
Corresponding author. Fax: þ86 21 62489191. E-mail addresses: [email protected] (Y. Lei), [email protected] (Y. Gu).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.12.022
28
brain research 1546 (2014) 27–33
been demonstrated to be different from those impaired in Alzheimer0 s disease (AD) and mild cognitive impairment (MCI) (Hachinski et al., 2006). The amplitude of low-frequency fluctuations (ALFF) (o0.1 Hz) in blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) during rest arises primarily from spontaneous fluctuations of brain physiology and neuronal activity, which provides insight into the intrinsic functional architecture of the brain (Fox and Raichle, 2007). ALFF analysis has been proven to be useful to detect spatial patterns in the brain activity of MCI and AD patients in several fMRI studies (Wang et al., 2011; Xi et al., 2012). The basic pathophysiological nature of VCI in adult patients with MMD is different from that in patients with AD and MCI, resulting in differences in ALFF. To the best of our knowledge, no previously published study has focused on the functional patterns of VCI in adult patients with MMD. This study investigates the spatial pattern of intrinsic brain activity in adult patients with MMD.
than that of the NC group (Po0.05). According to the published clinical criteria for the diagnosis of VCI (Gorelick et al., 2011; Hachinski et al., 2006), 11 patients (47.8%) in the case group were diagnosed with cognitive impairment (VCI subgroup), while the other 12 (52.2%) exhibited normal cognitive function (NonVCI subgroup).
2.2.
ALFF patterns of each group
The ALFF patterns of adult patients with MMD (VCI and NonVCI subgroups) and healthy young adult subjects are presented in Fig. 1. Regions of the frontal gyrus such as the prefrontal gyrus (PFG), anterior cingulate cortex (ACC) and right supplemental motor area (SMA); regions of the parietal gyrus such as the precuneus (PCu) and the inferior parietal lobe (IPL); regions of the temporal gyrus such as the amygdala nucleus (AN), hippocampus (Hip) and parahippocampal gyrus (PHG); and subcortical regions such as the caudate nucleus (CN), thalamus (Tha) and left insular gyrus (IG) showed significant differences in ALFF within each group.
2.
Results
2.3.
2.1.
Demographics and cognitive testing
Differences in ALFF between the VCI and NC groups are highlighted in Fig. 2. Significant decreases in ALFF in the VCI subgroup were found in the parietal gyrus, including the bilateral inferior parietal lobe (IPL), the superior parietal lobe (SPL) and the PCu. Significant increases in ALFF were observed
The case and NC groups did not differ significantly in age, education level, or gender distribution (Table 1). As expected, the MMSE score of the case group was significantly lower
ALFF patterns between groups
Table 1 – Demographic characteristics and cognitive testing of the two groups. Index
NC group (n ¼22)
Case group Total (n ¼23)
VCI subgroup (n ¼11)
t/z/X2 value (P value)a
NonVCI subgroup (n ¼12)
Age (years)
40.279.5
40.2711.2
40.378.1
40.277.2
0.011(0.991)
Male (%)
11(47.8)
4(36.4)
7(58.3)
10(45.5)
0.025(0.873)
Education (years) MMSE
8.375.0
6.175.0
10.374.1
9.474.2
0.611(0.545)
24.375.5
19.674.3
28.571.5
29.071.2
2.722(0.007)
MMSE ¼Mini-mental state examination. a Comparison between the case and NC groups.
Fig. 1 – ALFF patterns of VCI subgroup, NonVCI subgroup and healthy young adult subjects. Frontal gyrus locations such as the prefrontal gyrus (PFG), anterior cingulate cortex (ACC) and right supplemental motor area (SMA), parietal gyrus locations such as the precuneus (PCu) and inferior parietal lobe (IPL), temporal gyrus regions such as the amygdala nucleus (AN), hippocampus (Hip) and parahippocampal (PHG), and some subcortical regions such as the caudate nucleus (CN), thalamus (Tha) and left insular gyrus (IG) exhibited significant ALFF differences within each group. The statistical threshold was set at Po0.001 with cluster size4216 mm3 (for more details, readers are referred to the corresponding author).
brain research 1546 (2014) 27–33
29
Fig. 2 – ALFF differences between the VCI and NC groups. Significant decreases in ALFF were observed in the VCI subgroup in regions of the parietal gyrus including the IPL, SPL and PCu. Significant increases in ALFF were observed in regions of the frontal gyrus, including the bilateral ACC, MSFG, orbitofrontal gyrus (OFG), right MFG and right SMA, in parts of the temporal gyrus such as the bilateral FG, Hip, PHG and left AN, and in subcortical regions such as the bilateral CN, Tha and left IG. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
in the frontal gyrus, including the bilateral ACC, the medial superior frontal gyrus (MSFG), the orbitofrontal gyrus (OFG), the right middle frontal gyrus (MFG) and right SMA, in parts of the temporal gyrus such as the bilateral fusiform gyrus (FG), Hip, PHG and left AN, and in subcortical regions such as the bilateral CN, Tha and left IG. No significant differences were observed between the two groups in the occipital gyrus (Table 2). Differences in ALFF between the NonVCI and NC groups are presented in Fig. 3. Interestingly, no significant differences were observed between the two groups in the parietal gyrus. The changes in the ALFF of the right SMA, right SFG and left CN were significantly different from those between the VCI and NC groups as described above. Significant increases in the ALFF of the NonVCI subgroup were observed in the frontal gyrus, including the right MFG, left ACC, left MSFG and bilateral OFG, and in the temporal gyrus, including the left AN, right FG, bilateral Hip and bilateral PHG. No significant differences were observed between the two groups in the occipital gyrus (Table 3). Differences in ALFF between the VCI and NonVCI subgroups are presented in Fig. 4. Compared with the NonVCI subgroup, the right STG of the VCI subgroup exhibited a significant decrease in ALFF. Significant increases were observed in the ALFF of the VCI subgroup in frontal gyrus locations such as the right SMA, right SFG and right ACC, in the temporal gyrus such as the left Hip and left PHG, and in the CN and left FG. No significant differences were observed between the two groups in the occipital gyrus (Table 4).
3.
Discussion
Using ALFF analysis based on the BOLD fMRI at rest, we observed the abnormal intrinsic functional activity of adult
patients with MMD. The results demonstrated changes in the ALFF patterns of many brain regions occurring together with the progressive cognitive decline caused by MMD. The frontal gyrus, notably the PFG, ACC and right SMA, exhibited the most significant difference in ALFF. Significant differences were also observed in the PCu, IPL, AN, Hip, PHG, CN, Tha, right STG and left IG. MMD is a chronic cerebrovascular occlusive disease which leads to transient ischemic attacks, cerebral infarction or intracranial hemorrhage as the disease progresses. Previous studies of adult MMD revealed that VCI is the consequence of ischemic damage to dynamic factors such as cerebral hypoperfusion, rather than to cerebral gray matter (Festa et al., 2010; Karzmark et al., 2012). Executive function/attention has been proven to be the main cognitive domain that deteriorates in VCI (Gorelick et al., 2011). Falkenberg et al. demonstrated that attention was significantly correlated with ACC, right SMA, right dorsolateral PFG, OFG, IPL and right PCu by analyzing the results of magnetic resonance spectroscopy (MRS) combined with resting-state BOLD fMRI (Falkenberg et al., 2012). Compared with the healthy young subjects in our study, patients with VCI exhibited decreases in ALFF in parts of the parietal gyrus including right IPL, left SPL and PCu, indicating functional deficiency, together with increases in ALFF in the frontal gyrus of ACC, right SMA, OFG, MSFG and right MFG, indicating functional compensation. VCI is a long-term complication for adult patients with MMD, indicating that the NonVCI group is at an earlier point in the progression of cognitive decline (Weinberg et al., 2011; Calviere et al., 2012; Karzmark et al., 2012; Su et al., 2013). Brain regions such as the right IPL, left SPL, PCu, right SMA, right SFG, right STG and left CN showed significant changes in ALFF following the deterioration of MMD, with either
30
brain research 1546 (2014) 27–33
Table 2 – Regions showing differences in ALFF between the VCI and NC groups. Vol (mm3)
Brain regions
MNI coordinates (mm) x
Frontal gyrus
Temporal gyrus
Parietal gyrus
Subcortical regions littelt
Left ACC Right MSFG Right SFGorb Right ACC Right SFG Right SMA Left IFGorb Right MFGorb Left AN Left FG Left Hip Right PHG Right IPL Left SPL Left PCu Left CN Left IG Right Tha
y
Maximum Z z
918 1440 2169 1431 3393
6 12 16 6 16
16 38 24 12 36
22 38 22 24 32
5.158 4.593 4.494 4.483 4.203
2277 1305 216 882 2772 2412 3177 1809 738 6309 891 630 639
14 26 30 20 30 20 22 42 30 0 6 26 6
24 24 54 4 0 16 4 54 60 66 6 12 16
48 22 4 18 46 16 28 50 44 54 12 18 2
3.738 3.667 3.194 5.394 4.911 4.711 4.096 5.121 3.920 3.790 5.352 4.954 4.360
x, y, z ¼ coordinates of primary peak locations in the MNI space; Z ¼ statistical value of peak voxel showing differences in ALFF between the VCI and NC groups (positive values indicate VCI4NC and vice versa, Po0.05). ACC ¼ anterior cingulate cortex; MSFG ¼ medial superior frontal gyrus; SFGorb ¼ superior frontal gyrus, orbital; SFG ¼superior frontal gyrus; SMA ¼supplemental motor area; IFGorb ¼ inferior frontal gyrus, orbital; MFGorb ¼middle frontal gyrus, orbital; AN¼ amygdala nucleus; FG ¼fusiform gyrus; Hip ¼ hippocampus; PHG ¼parahippocampal gyrus; IPL ¼ inferior parietal lobe; SPL¼ superior parietal lobe; PCu ¼ precuneus; CN ¼caudate nucleus; IG ¼insular gyrus; Tha ¼ thalamus.
Fig. 3 – ALFF differences between the NonVCI and NC groups. Significant decreases in ALFF were observed in the NonVCI subgroup in the right SMA, right SFG and left CN. Significant increases in ALFF were observed in the frontal gyrus, including in the right MFG, left ACC, left MSFG and bilateral OFG, and in regions of the temporal gyrus such as the left AN, right FG, bilateral Hip and bilateral PHG. Little difference was observed between the two groups in the parietal gyrus. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
negative or positive changes, indicating either functional deficiency or compensation. It has been revealed that the right IPL played a crucial role so as to maintain attentive control and to respond to salient new information (Adler
et al., 2001; Gur et al., 2007). Also, the PCu is a major association area that connects with other brain regions such as the IPL, SPL, SMA and ACC to subserve a variety of behavioral functions (Cavanna and Trimble, 2006).
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brain research 1546 (2014) 27–33
Table 3 – Regions showing differences in ALFF between the NonVCI and NC groups. Vol (mm3)
Brain regions
Frontal gyrus
Temporal gyrus
Subcortical regions
Right MFG Left ACC Left MSFG Left IFGorb Left SFGorb Right SMA Right SFG Left AN Right Hip Right PHG Right FG Left CN
243 567 783 1062 486 2943 1566 522 1116 1233 1152 180
MNI coordinates (mm)
Maximum Z
x
y
z
36 6 6 32 12 4 24 20 22 20 32 14
38 18 32 24 18 12 2 4 6 6 12 10
14 22 50 18 22 62 66 16 22 22 46 24
6.034 4.592 4.117 3.674 3.079 4.296 4.082 4.447 4.391 4.391 3.673 3.548
x, y, z ¼coordinates of primary peak locations in the MNI space; Z¼ statistical value of peak voxel showing differences in ALFF between the VCI and NC groups (positive values indicate NonVCI4NC and vice versa, Po0.05). MFG ¼middle frontal gyrus; SFGorb ¼superior frontal gyrus, orbital.
Fig. 4 – ALFF differences between the VCI and NonVCI subgroups. A significant decrease in ALFF was observed in the VCI subgroup in the right STG. Significant increases in ALFF were observed in the frontal gyrus, including the right SMA, right SFG and right ACC, in regions of the temporal gyrus such as the left Hip and left PHG, and in the left FG and bilateral CN. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
It has been demonstrated that an inherent activity of brain regions in the resting state is spatially organized as a network with specific coherent patterns (Jafri et al., 2008). In our study, the regions showing ALFF differences belong to different restingstate networks (RSNs) such as the dorsal attention network (DAN), central-executive network (CEN), self-referential network (SRN) and default mode network (DMN) (Li et al., 2013). These RSNs contribute fully or partially to cognitive domains of executive function/attention. These signal changes following the deterioration of MMD in our study have not been described before. It may indicate strengthening or weakening of functional connectivity within the RSNs, thus resulting in neurocognition
decline. Further investigation is needed to understand these findings in detail. In addition, regions of the temporal gyrus such as the Hip, PHG, FG and AN revealed significant increases in ALFF in VCI subjects compared with NonVCI and NC subjects. The results demonstrated that the episodic memory of VCI patients was also affected but was mainly functionally compensated, which was supported by previous studies (Karzmark et al., 2012; Xi et al., 2012). Two additional issues require further investigation. First, we determined that MMD patients without cognitive impairment (NonVCI) also revealed significant changes in ALFF in
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brain research 1546 (2014) 27–33
Table 4 – Regions showing differences in ALFF between the VCI and NonVCI subgroups. Vol (mm3)
Brain regions
Frontal gyrus
Temporal gyrus Parietal gyrus Subcortical regions
Right SMA Right SFG Right ACC Left Hip Left PHG Right STG Left FG Left CN
MNI coordinates (mm)
4257 927 54 279 693 1242 711 216
Maximum Z
x
y
z
12 16 8 36 30 58 36 6
6 6 6 34 40 6 16 2
56 60 28 10 10 10 34 12
5.6385 3.942 3.1203 5.2225 3.1372 3.8511 3.6145 3.2883
x, y, z ¼coordinates of primary peak locations in the MNI space; Z ¼statistical value of peak voxel showing ALFF differences between VCI and NonVCI subgroups (Positive values indicate VCI4 NonVCI and vice versa, Po0.05). STG ¼ superior temporal gyrus.
comparison with healthy young subjects. As cognitive impairment is a continuous process in adult patients with MMD, the brain regions with changes in ALFF may help to understand the intrinsic activity dynamics and the underlying neurophysiological mechanism of the disease. In addition, several regions, such as the ACC, right SMA and PCu, exhibited significant changes in ALFF in our study, indicating that they could serve as markers to differentiate VCI/NonVCI patients from the normal control. Taken together, our results demonstrate that MMD patients exhibit a specific pattern of ALFF and that this pattern changes following cognitive impairment, thereby providing insight into the pathophysiological nature of this disease.
4.
Experimental procedures
4.1.
Participants
4.2.
A battery of neuropsychological tests was administered to participants by a trained rater who was unaware of the study aim or the patient diagnosis. The tests covered global cognition, executive, memory, language, and visuospatial functions, including the Mini-mental state examination (MMSE), the Trail Making Test (TMT), the Auditory Verbal Learning Test (AVLT), the verbal fluency test (VFT), the Rey–Osterrieth complex figure test (CFT), etc. The MMSE was adopted as a screening test for cognitive impairment. We adopted the practical diagnostic criteria for VCI provided by the American Heart Association/American Stroke Association. The criteria are based on two factors: presence of cognitive impairment demonstrated by neuropsychological tests and presence of vascular disease demonstrated by neuroimaging. And the two factors should be correlated (Gorelick et al., 2011).
4.3. Twenty-three adult patients with MMD (the case group) were recruited consecutively from the Department of Neurosurgery, Huashan Hospital, from January to June 2013. Inclusion criteria were as follows: (1) right-handed Chinese people aged over 18 years; (2) no evidence of recent or remote infarct in the cerebral cortical, basal ganglia, brainstem or cerebellum; (3) no evidence of recent or remote intracerebral hemorrhage; (4) diagnosis confirmed by digital subtraction angiography (DSA); (5) no surgical intervention before recruitment; (6) physically capable of cognitive testing; (7) absence of significant neurological diseases or psychiatric disorders that could compromise cognition. Patients with other cerebrovascular diseases, severe systemic diseases, and those taking medicines such as benzodiazepine clonazepam were excluded. Twenty-two healthy young subjects without memory complaints, mental diseases or any cerebrovascular disease were chosen as normal controls (NC) after screening by magnetic resonance angiography (MRA). The case and NC groups were matched in age, educational background, dominant hand and gender. The study was approved by the Institutional Review Board in Huashan Hospital and all participants provided informed consent.
Neuropsychological assessment
fMRI scanning
Participants were scanned using a 3.0 T intraoperative MRI (iMRI) system (Siemens Medical Solutions, Erlangen, Germany). All fMRI data were acquired using gradient echoplanar imaging (EPI), time repetition (TR)/time echo (TE)¼ 2000/35 ms; FOV¼240 mm 240 mm; matrix size¼ 64 64; slice thickness¼4 mm. The scans lasted for approximately 10 min. For the structural images, a fast spoiled gradient recalled echo inversion recovery (FSPGRIR) sequence was used to acquire a 1 mm thick axial section with the following parameters: TR/TE¼ 1000/5 ms, inversion time (IR)¼ 400 ms, Flip angle¼201, inter-slice space¼ 0 mm, FOV¼ 240 mm 240 mm, and acquisition matrix¼256 256.
4.4.
fMRI preprocessing
BOLD data were preprocessed using Statistical Parametric Mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm/). For each partici pant, functional volumes were realigned using least-squares minimization without higher-order corrections for spin history and then normalized to the Montreal Neurological Institute (MNI) template. Images were resampled to 3 mm isotropic voxels and
brain research 1546 (2014) 27–33
then smoothed with a 4 mm full width at half-maximum Gaussian kernel. Afterwards, we adopted the Resting-State fMRI Data Analysis Toolkit (REST, http://rest.restfmri.net) to perform linear trend subtraction and temporal filtering (0.01–0. 08 Hz) on the time series of each voxel to reduce the effect of high-frequency noise and low-frequency drifts (Biswal et al., 1995). Subsequently, the ALFF of each voxel was divided by the global mean ALFF value within the previously obtained whole-brain mask to reduce the global effects of variability among subjects.
4.5.
Statistical analysis
One-sample t-tests were performed on individual ALFF maps for each group in a voxel-wise manner to explore the within-group ALFF patterns (cluster size4216 mm3, Po0.001). Two-sample t-tests were performed between each pair of subgroups on individual ALFF maps (cluster size41800 mm3, Po0.05). Statistical analysis was carried out using SPSS 16.0.
Acknowledgments The authors are grateful to the healthy young adults who agreed to participate in the study. We also thank the Elsevier Language Editing Services for their English editing. Finally, we would also like to thank our families and friends for their encouragement and support.
r e f e r e n c e s
Adler, C.M., Sax, K.W., Holland, S.K., Schmithorst, V., Rosenberg, L., Strakowski, S.M., 2001. Changes in neuronal activation with increasing attention demand in healthy volunteers: an fMRI study. Synapse 42, 266–272. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541. Calviere, L., Ssi, Yan, Kai, G., Catalaa, I., Marlats, F., Bonneville, F., Larrue, V., 2012. Executive dysfunction in adults with moyamoya disease is associated with increased diffusion in frontal white matter. J. Neurol. Neurosurg. Psychiatry 83, 591–593. Cavanna, A.E., Trimble, M.R., 2006. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564–583. Falkenberg, L.E., Westerhausen, R., Specht, K., Hugdahl, K., 2012. Resting-state glutamate level in the anterior cingulate predicts blood-oxygen level-dependent response to cognitive control. Proc. Natl. Acad. Sci. USA 109, 5069–5073. Festa, J.R., Schwarz, L.R., Pliskin, N., Cullum, C.M., Lacritz, L., Charbel, F.T., Mathews, D., Starke, R.M., Connolly, E.S.,
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Marshall, R.M., Lazar, R.M., 2010. Neurocognitive dysfunction in adult moyamoya disease. J. Neurol. 257, 806–815. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic imaging. Nat. Rev. Neurosci. 8, 700–711. Gorelick, P.B., Scuteri, A., Black, S.E., Decarli, C., Greenberg, S.M., Iadecola, C., Launer, L.J., Laurent, S., Lopez, O.L., Nyenhuis, D., Petersen, R.C., Schneider, J.A., Tzourio, C., Arnett, D.K., Bennett, D.A., Chui, H.C., Higashida, R.T., Lindquist, R., Nilsson, P.M., Roman, G.C., Sellke, F.W., Seshadri, S., American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia, 2011. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke 42, 2672–2713. Gur, R.C., Turetsky, B.I., Loughead, J., Waxman, J., Snyder, W., Ragland, J.D., Elliott, M.A., Bilker, W.B., Arnold, S.E., Gur, R.E., 2007. Hemodynamic responses in neural circuitries for detection of visual target and novelty: an event-related fMRI study. Hum. Brain Mapp. 28, 263–274. Hachinski, V., Iadecola, C., Petersen, R.C., Breteler, M.M., Nyenhuis, D.L., Black, S.E., Powers, W.J., DeCarli, C., Merino, J.G., Kalaria, R.N., Vinters, H.V., Holtzman, D.M., Rosenberg, G.A., Wallin, A., Dichgans, M., Marler, J.R., Leblanc, G.G., 2006. National Institute of Neurological Disorders and StrokeCanadian Stroke Network vascular cognitive impairment harmonization standards. Stroke 37, 2220–2241. Jafri, M.J., Pearlson, G.D., Stevens, M., Calhoun, V.D., 2008. A method for functional network connectivity among spatially independent resting-state components in schizoprenia. Neuroimage 39, 1666–1681. Karzmark, P., Zeifert, P.D., Bell-Stephens, T.E., Steinberg, G.K., Dorfman, L.J., 2012. Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery 70, 634–638. Li, R., Wang, S., Zhu, L., Guo, J., Zeng, L., Gong, Q., He, L., Chen, H., 2013. Aberrant functional connectivity of resting state networks in transient ischemic attack. PloS One 8, e71009. Su, S.H., Hai, J., Zhang, L., Yu, F., Wu, Y.F., 2013. Assessment of cognitive function in adult patients with hemorrhagic moyamoya disease who received no surgical revascularization. Eur. J. Neurol. 20, 1081–1087. Suzuki, J., Kodama, N., 1983. Moyamoya disease: a review. Stroke 14, 104–109. Wang, Z., Yan, C., Zhao, C., Qi, Z., Zhou, W., Lu, J., He, Y., Li, K., 2011. Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer0 s disease: a resting-state functional MRI study. Hum. Brain Mapp. 32, 1720–1740. Weinberg, D.G., Rahme, R.J., Aoun, R.G., Batjer, H.H., Bendok, B.R., 2011. Moyamoya disease: functional and neurocognitive outcomes in the pediatric and adult populations. Neurosurg. Focus 30, E21. Xi, Q., Zhao, X., Wang, P., Guo, Q., Jiang, H., Cao, X., He, Y., Yan, C., 2012. Spontaneous brain activity in mild cognitive impairment revealed by amplitude of low-frequency fluctuation analysis: a resting-state fMRI study. Radiol. Med. 117, 865–871.
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study Yu Leia, Yanjiang Lia, Wei Nia, Hanqiang Jianga, Zhong Yangb, Qihao Guoc, Yuxiang Gua,n, Ying Maoa a Huashan Hospital of Fudan University, Department of Neurosurgery, 12# Wulumuqi Zhong Road, Shanghai 200040, China b Huashan Hospital of Fudan University, Department of Radiology, 12# Wulumuqi Zhong Road, Shanghai 200040, China c Huashan Hospital of Fudan University, Department of Neurology, 12# Wulumuqi Zhong Road, Shanghai 200040, China
art i cle i nfo
ab st rac t
Article history:
Adult patients with moyamoya disease (MMD) are reported to suffer from vascular cognitive
Accepted 19 December 2013
impairment (VCI), including considerable impairment of executive function/attention. The
Available online 28 December 2013
spatial pattern of functional brain activity in adult MMD patients with VCI has not been
Keywords:
studied before and can be measured by examining the amplitude of low-frequency
Amplitude of low-frequency fluctuation
fluctuations (ALFF) of blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) during rest. Twenty-three adult patients with MMD were recruited to participate
Blood oxygen level-dependent
in this study, including 11 with VCI and 12 without VCI (NonVCI), as well as 22 healthy young
functional magnetic resonance
adults (normal control, NC). Widespread differences in ALFF were observed between the VCI/
imaging
NonVCI and NC groups in such regions as the frontal, parietal and temporal gyri, with parts
Intrinsic brain activity
of the frontal gyrus, such as the anterior cingulate cortex (ACC) and the right supplemental
Moyamoya disease
motor area (SMA), showing significant differences in ALFF. It is worth to note that regions
Vascular cognitive impairment
such as the parietal gyrus, the right superior frontal gyrus (SFG), the right superior temporal gyrus (STG) and the left caudate nucleus (CN) exhibited significant changes in ALFF during the progressive cognitive decline of MMD. Taken together, our results demonstrate that MMD exhibits a specific intrinsic pattern of ALFF and that this pattern changes with the progression of cognitive decline, providing insight into the pathophysiological nature of this disease. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Moyamoya disease (MMD) is a chronic cerebrovascular disease of uncertain cause. MMD is characterized by the progressive stenosis of the arteries of the circle of Willis, which results in the formation of a collateral network of capillaries, producing the appearance of a “puff of smoke” at the base of the brain on n
angiography (Suzuki and Kodama, 1983). Adult patients with MMD frequently suffer from vascular cognitive impairment (VCI), including considerable impairment in executive function/attention, and with the other three cognitive domains of memory, language and visuospatial functions being impaired to varying degrees (Calviere et al., 2012; Karzmark et al., 2012; Su et al., 2013; Weinberg et al., 2011). The cognitive domains impaired have
Corresponding author. Fax: þ86 21 62489191. E-mail addresses: [email protected] (Y. Lei), [email protected] (Y. Gu).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.12.022
28
brain research 1546 (2014) 27–33
been demonstrated to be different from those impaired in Alzheimer0 s disease (AD) and mild cognitive impairment (MCI) (Hachinski et al., 2006). The amplitude of low-frequency fluctuations (ALFF) (o0.1 Hz) in blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) during rest arises primarily from spontaneous fluctuations of brain physiology and neuronal activity, which provides insight into the intrinsic functional architecture of the brain (Fox and Raichle, 2007). ALFF analysis has been proven to be useful to detect spatial patterns in the brain activity of MCI and AD patients in several fMRI studies (Wang et al., 2011; Xi et al., 2012). The basic pathophysiological nature of VCI in adult patients with MMD is different from that in patients with AD and MCI, resulting in differences in ALFF. To the best of our knowledge, no previously published study has focused on the functional patterns of VCI in adult patients with MMD. This study investigates the spatial pattern of intrinsic brain activity in adult patients with MMD.
than that of the NC group (Po0.05). According to the published clinical criteria for the diagnosis of VCI (Gorelick et al., 2011; Hachinski et al., 2006), 11 patients (47.8%) in the case group were diagnosed with cognitive impairment (VCI subgroup), while the other 12 (52.2%) exhibited normal cognitive function (NonVCI subgroup).
2.2.
ALFF patterns of each group
The ALFF patterns of adult patients with MMD (VCI and NonVCI subgroups) and healthy young adult subjects are presented in Fig. 1. Regions of the frontal gyrus such as the prefrontal gyrus (PFG), anterior cingulate cortex (ACC) and right supplemental motor area (SMA); regions of the parietal gyrus such as the precuneus (PCu) and the inferior parietal lobe (IPL); regions of the temporal gyrus such as the amygdala nucleus (AN), hippocampus (Hip) and parahippocampal gyrus (PHG); and subcortical regions such as the caudate nucleus (CN), thalamus (Tha) and left insular gyrus (IG) showed significant differences in ALFF within each group.
2.
Results
2.3.
2.1.
Demographics and cognitive testing
Differences in ALFF between the VCI and NC groups are highlighted in Fig. 2. Significant decreases in ALFF in the VCI subgroup were found in the parietal gyrus, including the bilateral inferior parietal lobe (IPL), the superior parietal lobe (SPL) and the PCu. Significant increases in ALFF were observed
The case and NC groups did not differ significantly in age, education level, or gender distribution (Table 1). As expected, the MMSE score of the case group was significantly lower
ALFF patterns between groups
Table 1 – Demographic characteristics and cognitive testing of the two groups. Index
NC group (n ¼22)
Case group Total (n ¼23)
VCI subgroup (n ¼11)
t/z/X2 value (P value)a
NonVCI subgroup (n ¼12)
Age (years)
40.279.5
40.2711.2
40.378.1
40.277.2
0.011(0.991)
Male (%)
11(47.8)
4(36.4)
7(58.3)
10(45.5)
0.025(0.873)
Education (years) MMSE
8.375.0
6.175.0
10.374.1
9.474.2
0.611(0.545)
24.375.5
19.674.3
28.571.5
29.071.2
2.722(0.007)
MMSE ¼Mini-mental state examination. a Comparison between the case and NC groups.
Fig. 1 – ALFF patterns of VCI subgroup, NonVCI subgroup and healthy young adult subjects. Frontal gyrus locations such as the prefrontal gyrus (PFG), anterior cingulate cortex (ACC) and right supplemental motor area (SMA), parietal gyrus locations such as the precuneus (PCu) and inferior parietal lobe (IPL), temporal gyrus regions such as the amygdala nucleus (AN), hippocampus (Hip) and parahippocampal (PHG), and some subcortical regions such as the caudate nucleus (CN), thalamus (Tha) and left insular gyrus (IG) exhibited significant ALFF differences within each group. The statistical threshold was set at Po0.001 with cluster size4216 mm3 (for more details, readers are referred to the corresponding author).
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Fig. 2 – ALFF differences between the VCI and NC groups. Significant decreases in ALFF were observed in the VCI subgroup in regions of the parietal gyrus including the IPL, SPL and PCu. Significant increases in ALFF were observed in regions of the frontal gyrus, including the bilateral ACC, MSFG, orbitofrontal gyrus (OFG), right MFG and right SMA, in parts of the temporal gyrus such as the bilateral FG, Hip, PHG and left AN, and in subcortical regions such as the bilateral CN, Tha and left IG. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
in the frontal gyrus, including the bilateral ACC, the medial superior frontal gyrus (MSFG), the orbitofrontal gyrus (OFG), the right middle frontal gyrus (MFG) and right SMA, in parts of the temporal gyrus such as the bilateral fusiform gyrus (FG), Hip, PHG and left AN, and in subcortical regions such as the bilateral CN, Tha and left IG. No significant differences were observed between the two groups in the occipital gyrus (Table 2). Differences in ALFF between the NonVCI and NC groups are presented in Fig. 3. Interestingly, no significant differences were observed between the two groups in the parietal gyrus. The changes in the ALFF of the right SMA, right SFG and left CN were significantly different from those between the VCI and NC groups as described above. Significant increases in the ALFF of the NonVCI subgroup were observed in the frontal gyrus, including the right MFG, left ACC, left MSFG and bilateral OFG, and in the temporal gyrus, including the left AN, right FG, bilateral Hip and bilateral PHG. No significant differences were observed between the two groups in the occipital gyrus (Table 3). Differences in ALFF between the VCI and NonVCI subgroups are presented in Fig. 4. Compared with the NonVCI subgroup, the right STG of the VCI subgroup exhibited a significant decrease in ALFF. Significant increases were observed in the ALFF of the VCI subgroup in frontal gyrus locations such as the right SMA, right SFG and right ACC, in the temporal gyrus such as the left Hip and left PHG, and in the CN and left FG. No significant differences were observed between the two groups in the occipital gyrus (Table 4).
3.
Discussion
Using ALFF analysis based on the BOLD fMRI at rest, we observed the abnormal intrinsic functional activity of adult
patients with MMD. The results demonstrated changes in the ALFF patterns of many brain regions occurring together with the progressive cognitive decline caused by MMD. The frontal gyrus, notably the PFG, ACC and right SMA, exhibited the most significant difference in ALFF. Significant differences were also observed in the PCu, IPL, AN, Hip, PHG, CN, Tha, right STG and left IG. MMD is a chronic cerebrovascular occlusive disease which leads to transient ischemic attacks, cerebral infarction or intracranial hemorrhage as the disease progresses. Previous studies of adult MMD revealed that VCI is the consequence of ischemic damage to dynamic factors such as cerebral hypoperfusion, rather than to cerebral gray matter (Festa et al., 2010; Karzmark et al., 2012). Executive function/attention has been proven to be the main cognitive domain that deteriorates in VCI (Gorelick et al., 2011). Falkenberg et al. demonstrated that attention was significantly correlated with ACC, right SMA, right dorsolateral PFG, OFG, IPL and right PCu by analyzing the results of magnetic resonance spectroscopy (MRS) combined with resting-state BOLD fMRI (Falkenberg et al., 2012). Compared with the healthy young subjects in our study, patients with VCI exhibited decreases in ALFF in parts of the parietal gyrus including right IPL, left SPL and PCu, indicating functional deficiency, together with increases in ALFF in the frontal gyrus of ACC, right SMA, OFG, MSFG and right MFG, indicating functional compensation. VCI is a long-term complication for adult patients with MMD, indicating that the NonVCI group is at an earlier point in the progression of cognitive decline (Weinberg et al., 2011; Calviere et al., 2012; Karzmark et al., 2012; Su et al., 2013). Brain regions such as the right IPL, left SPL, PCu, right SMA, right SFG, right STG and left CN showed significant changes in ALFF following the deterioration of MMD, with either
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Table 2 – Regions showing differences in ALFF between the VCI and NC groups. Vol (mm3)
Brain regions
MNI coordinates (mm) x
Frontal gyrus
Temporal gyrus
Parietal gyrus
Subcortical regions littelt
Left ACC Right MSFG Right SFGorb Right ACC Right SFG Right SMA Left IFGorb Right MFGorb Left AN Left FG Left Hip Right PHG Right IPL Left SPL Left PCu Left CN Left IG Right Tha
y
Maximum Z z
918 1440 2169 1431 3393
6 12 16 6 16
16 38 24 12 36
22 38 22 24 32
5.158 4.593 4.494 4.483 4.203
2277 1305 216 882 2772 2412 3177 1809 738 6309 891 630 639
14 26 30 20 30 20 22 42 30 0 6 26 6
24 24 54 4 0 16 4 54 60 66 6 12 16
48 22 4 18 46 16 28 50 44 54 12 18 2
3.738 3.667 3.194 5.394 4.911 4.711 4.096 5.121 3.920 3.790 5.352 4.954 4.360
x, y, z ¼ coordinates of primary peak locations in the MNI space; Z ¼ statistical value of peak voxel showing differences in ALFF between the VCI and NC groups (positive values indicate VCI4NC and vice versa, Po0.05). ACC ¼ anterior cingulate cortex; MSFG ¼ medial superior frontal gyrus; SFGorb ¼ superior frontal gyrus, orbital; SFG ¼superior frontal gyrus; SMA ¼supplemental motor area; IFGorb ¼ inferior frontal gyrus, orbital; MFGorb ¼middle frontal gyrus, orbital; AN¼ amygdala nucleus; FG ¼fusiform gyrus; Hip ¼ hippocampus; PHG ¼parahippocampal gyrus; IPL ¼ inferior parietal lobe; SPL¼ superior parietal lobe; PCu ¼ precuneus; CN ¼caudate nucleus; IG ¼insular gyrus; Tha ¼ thalamus.
Fig. 3 – ALFF differences between the NonVCI and NC groups. Significant decreases in ALFF were observed in the NonVCI subgroup in the right SMA, right SFG and left CN. Significant increases in ALFF were observed in the frontal gyrus, including in the right MFG, left ACC, left MSFG and bilateral OFG, and in regions of the temporal gyrus such as the left AN, right FG, bilateral Hip and bilateral PHG. Little difference was observed between the two groups in the parietal gyrus. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
negative or positive changes, indicating either functional deficiency or compensation. It has been revealed that the right IPL played a crucial role so as to maintain attentive control and to respond to salient new information (Adler
et al., 2001; Gur et al., 2007). Also, the PCu is a major association area that connects with other brain regions such as the IPL, SPL, SMA and ACC to subserve a variety of behavioral functions (Cavanna and Trimble, 2006).
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Table 3 – Regions showing differences in ALFF between the NonVCI and NC groups. Vol (mm3)
Brain regions
Frontal gyrus
Temporal gyrus
Subcortical regions
Right MFG Left ACC Left MSFG Left IFGorb Left SFGorb Right SMA Right SFG Left AN Right Hip Right PHG Right FG Left CN
243 567 783 1062 486 2943 1566 522 1116 1233 1152 180
MNI coordinates (mm)
Maximum Z
x
y
z
36 6 6 32 12 4 24 20 22 20 32 14
38 18 32 24 18 12 2 4 6 6 12 10
14 22 50 18 22 62 66 16 22 22 46 24
6.034 4.592 4.117 3.674 3.079 4.296 4.082 4.447 4.391 4.391 3.673 3.548
x, y, z ¼coordinates of primary peak locations in the MNI space; Z¼ statistical value of peak voxel showing differences in ALFF between the VCI and NC groups (positive values indicate NonVCI4NC and vice versa, Po0.05). MFG ¼middle frontal gyrus; SFGorb ¼superior frontal gyrus, orbital.
Fig. 4 – ALFF differences between the VCI and NonVCI subgroups. A significant decrease in ALFF was observed in the VCI subgroup in the right STG. Significant increases in ALFF were observed in the frontal gyrus, including the right SMA, right SFG and right ACC, in regions of the temporal gyrus such as the left Hip and left PHG, and in the left FG and bilateral CN. The statistical threshold was set at Po0.05 with a cluster size41800 mm3 (for more details, readers are referred to the corresponding author).
It has been demonstrated that an inherent activity of brain regions in the resting state is spatially organized as a network with specific coherent patterns (Jafri et al., 2008). In our study, the regions showing ALFF differences belong to different restingstate networks (RSNs) such as the dorsal attention network (DAN), central-executive network (CEN), self-referential network (SRN) and default mode network (DMN) (Li et al., 2013). These RSNs contribute fully or partially to cognitive domains of executive function/attention. These signal changes following the deterioration of MMD in our study have not been described before. It may indicate strengthening or weakening of functional connectivity within the RSNs, thus resulting in neurocognition
decline. Further investigation is needed to understand these findings in detail. In addition, regions of the temporal gyrus such as the Hip, PHG, FG and AN revealed significant increases in ALFF in VCI subjects compared with NonVCI and NC subjects. The results demonstrated that the episodic memory of VCI patients was also affected but was mainly functionally compensated, which was supported by previous studies (Karzmark et al., 2012; Xi et al., 2012). Two additional issues require further investigation. First, we determined that MMD patients without cognitive impairment (NonVCI) also revealed significant changes in ALFF in
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Table 4 – Regions showing differences in ALFF between the VCI and NonVCI subgroups. Vol (mm3)
Brain regions
Frontal gyrus
Temporal gyrus Parietal gyrus Subcortical regions
Right SMA Right SFG Right ACC Left Hip Left PHG Right STG Left FG Left CN
MNI coordinates (mm)
4257 927 54 279 693 1242 711 216
Maximum Z
x
y
z
12 16 8 36 30 58 36 6
6 6 6 34 40 6 16 2
56 60 28 10 10 10 34 12
5.6385 3.942 3.1203 5.2225 3.1372 3.8511 3.6145 3.2883
x, y, z ¼coordinates of primary peak locations in the MNI space; Z ¼statistical value of peak voxel showing ALFF differences between VCI and NonVCI subgroups (Positive values indicate VCI4 NonVCI and vice versa, Po0.05). STG ¼ superior temporal gyrus.
comparison with healthy young subjects. As cognitive impairment is a continuous process in adult patients with MMD, the brain regions with changes in ALFF may help to understand the intrinsic activity dynamics and the underlying neurophysiological mechanism of the disease. In addition, several regions, such as the ACC, right SMA and PCu, exhibited significant changes in ALFF in our study, indicating that they could serve as markers to differentiate VCI/NonVCI patients from the normal control. Taken together, our results demonstrate that MMD patients exhibit a specific pattern of ALFF and that this pattern changes following cognitive impairment, thereby providing insight into the pathophysiological nature of this disease.
4.
Experimental procedures
4.1.
Participants
4.2.
A battery of neuropsychological tests was administered to participants by a trained rater who was unaware of the study aim or the patient diagnosis. The tests covered global cognition, executive, memory, language, and visuospatial functions, including the Mini-mental state examination (MMSE), the Trail Making Test (TMT), the Auditory Verbal Learning Test (AVLT), the verbal fluency test (VFT), the Rey–Osterrieth complex figure test (CFT), etc. The MMSE was adopted as a screening test for cognitive impairment. We adopted the practical diagnostic criteria for VCI provided by the American Heart Association/American Stroke Association. The criteria are based on two factors: presence of cognitive impairment demonstrated by neuropsychological tests and presence of vascular disease demonstrated by neuroimaging. And the two factors should be correlated (Gorelick et al., 2011).
4.3. Twenty-three adult patients with MMD (the case group) were recruited consecutively from the Department of Neurosurgery, Huashan Hospital, from January to June 2013. Inclusion criteria were as follows: (1) right-handed Chinese people aged over 18 years; (2) no evidence of recent or remote infarct in the cerebral cortical, basal ganglia, brainstem or cerebellum; (3) no evidence of recent or remote intracerebral hemorrhage; (4) diagnosis confirmed by digital subtraction angiography (DSA); (5) no surgical intervention before recruitment; (6) physically capable of cognitive testing; (7) absence of significant neurological diseases or psychiatric disorders that could compromise cognition. Patients with other cerebrovascular diseases, severe systemic diseases, and those taking medicines such as benzodiazepine clonazepam were excluded. Twenty-two healthy young subjects without memory complaints, mental diseases or any cerebrovascular disease were chosen as normal controls (NC) after screening by magnetic resonance angiography (MRA). The case and NC groups were matched in age, educational background, dominant hand and gender. The study was approved by the Institutional Review Board in Huashan Hospital and all participants provided informed consent.
Neuropsychological assessment
fMRI scanning
Participants were scanned using a 3.0 T intraoperative MRI (iMRI) system (Siemens Medical Solutions, Erlangen, Germany). All fMRI data were acquired using gradient echoplanar imaging (EPI), time repetition (TR)/time echo (TE)¼ 2000/35 ms; FOV¼240 mm 240 mm; matrix size¼ 64 64; slice thickness¼4 mm. The scans lasted for approximately 10 min. For the structural images, a fast spoiled gradient recalled echo inversion recovery (FSPGRIR) sequence was used to acquire a 1 mm thick axial section with the following parameters: TR/TE¼ 1000/5 ms, inversion time (IR)¼ 400 ms, Flip angle¼201, inter-slice space¼ 0 mm, FOV¼ 240 mm 240 mm, and acquisition matrix¼256 256.
4.4.
fMRI preprocessing
BOLD data were preprocessed using Statistical Parametric Mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm/). For each partici pant, functional volumes were realigned using least-squares minimization without higher-order corrections for spin history and then normalized to the Montreal Neurological Institute (MNI) template. Images were resampled to 3 mm isotropic voxels and
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then smoothed with a 4 mm full width at half-maximum Gaussian kernel. Afterwards, we adopted the Resting-State fMRI Data Analysis Toolkit (REST, http://rest.restfmri.net) to perform linear trend subtraction and temporal filtering (0.01–0. 08 Hz) on the time series of each voxel to reduce the effect of high-frequency noise and low-frequency drifts (Biswal et al., 1995). Subsequently, the ALFF of each voxel was divided by the global mean ALFF value within the previously obtained whole-brain mask to reduce the global effects of variability among subjects.
4.5.
Statistical analysis
One-sample t-tests were performed on individual ALFF maps for each group in a voxel-wise manner to explore the within-group ALFF patterns (cluster size4216 mm3, Po0.001). Two-sample t-tests were performed between each pair of subgroups on individual ALFF maps (cluster size41800 mm3, Po0.05). Statistical analysis was carried out using SPSS 16.0.
Acknowledgments The authors are grateful to the healthy young adults who agreed to participate in the study. We also thank the Elsevier Language Editing Services for their English editing. Finally, we would also like to thank our families and friends for their encouragement and support.
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