The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network.

Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients' language scores. These findings shed light on our understanding of metaplasticity in glioma. Glioma-induced language network reorganizations were investigated under a dynamic "meta-networking" (network of networks) framework. In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states. The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network. Language network dynamics significantly predicted individual patients' language scores.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing.

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Hidden Markov Modeling Reveals Prolonged "Baseline" State and Shortened Antagonistic State across the Adult Lifespan.

The brain networks undergo functional reorganization across the whole lifespan, but the dynamic patterns behind the reorganization remain largely unclear. This study models the dynamics of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age on two adult lifespan datasets of 176/157 subjects (aged 20-80 years). Results for both datasets showed that 1) older adults tended to spend less time on a state where default mode network (DMN) and attentional networks show antagonistic activity, 2) older adults spent more time on a "baseline" state with moderate-level activation of all networks, accompanied with lower transition probabilities from this state to the others and higher transition probabilities from the others to this state, and 3) HMM exhibited higher sensitivity in uncovering the age effects compared with temporal clustering method. Our results suggest that the aging brain is characterized by the shortening of the antagonistic instances between DMN and attention systems, as well as the prolongation of the inactive period of all networks, which might reflect the shift of the dynamical working point near criticality in older adults.

Functional Connectivity Changes in Multiple-Frequency Bands in Acute Basal Ganglia Ischemic Stroke Patients: A Machine Learning Approach.

Purpose: Several functional magnetic resonance imaging (fMRI) studies have investigated the resting-state functional connectivity (rs-FC) changes in the primary motor cortex (M1) in patients with acute basal ganglia ischemic stroke (BGIS). However, the frequency-specific FC changes of M1 in acute BGIS patients are still unclear. Our study was aimed at exploring the altered FC of M1 in three frequency bands and the potential features as biomarkers for the identification by using a support vector machine (SVM). Methods: We included 28 acute BGIS patients and 42 healthy controls (HCs). Seed-based FC of two regions of interest (ROI, bilateral M1s) were calculated in conventional, slow-5, and slow-4 frequency bands. The abnormal voxel-wise FC values were defined as the features for SVM in different frequency bands. Results: In the ipsilesional M1, the acute BGIS patients exhibited decreased FC with the right lingual gyrus in the conventional and slow-4 frequency band. Besides, the acute BGIS patients showed increased FC with the right medial superior frontal gyrus (SFGmed) in the conventional and slow-5 frequency band and decreased FC with the left lingual gyrus in the slow-5 frequency band. In the contralesional M1, the BGIS patients showed lower FC with the right SFGmed in the conventional frequency band. The higher FC values with the right lingual gyrus and left SFGmed were detected in the slow-4 frequency band. In the slow-5 frequency band, the BGIS patients showed decreased FC with the left calcarine sulcus. SVM results showed that the combined features (slow-4+slow-5) had the highest accuracy in classification prediction of acute BGIS patients, with an area under curve (AUC) of 0.86. Conclusion: Acute BGIS patients had frequency-specific alterations in FC; SVM is a promising method for exploring these frequency-dependent FC alterations. The abnormal brain regions might be potential targets for future researchers in the rehabilitation and treatment of stroke patients.

Resting EEG in alpha band predicts individual differences in visual size perception.

Human visual size perception results from an interaction of external sensory information and internal state. The cognitive mechanisms involved in the processing of context-dependent visual size perception have been found to be innate in nature to some extent, suggesting that visual size perception might correlate with human intrinsic brain activity. Here we recorded human resting alpha activity (8-12 Hz), which is an inverse indicator of sustained alertness. Moreover, we measured an object's perceived size in a two-alternative forced-choice manner and the Ebbinghaus illusion magnitude which is a classic illustration of context-dependent visual size perception. The results showed that alpha activity along the ventral visual pathway, including left V1, right LOC and bilateral inferior temporal gyrus, negatively correlated with an object's perceived size. Moreover, alpha activity in the left superior temporal gyrus positively correlated with size discrimination threshold and size illusion magnitude. The findings provide clear evidence that human visual size perception scales as a function of intrinsic alertness, with higher alertness linking to larger perceived size of objects and better performance in size discrimination and size illusion tasks, and suggest that individual variation in resting-state brain activity provides a neural explanation for individual variation in cognitive performance of normal participants.

Motor-symptom laterality affects acquisition in Parkinson's disease: A cognitive and functional magnetic resonance imaging study.

BACKGROUND AND OBJECTIVES: Asymmetric onset of motor symptoms in PD can affect cognitive function. We examined whether motor-symptom laterality could affect feedback-based associative learning and explored its underlying neural mechanism by functional magnetic resonance imaging in PD patients. METHODS: We recruited 63 early-stage medication-naïve PD patients (29 left-onset medication-naïve patients, 34 right-onset medication-naïve patients) and 38 matched normal controls. Subjects completed an acquired equivalence task (including acquisition, retention, and generalization) and resting-state functional magnetic resonance imaging scans. Learning accuracy and response time in each phase of the task were recorded for behavioral measures. Regional homogeneity was used to analyze resting-state functional magnetic resonance imaging data, with regional homogeneity lateralization to evaluate hemispheric functional asymmetry in the striatum. RESULTS: Left-onset patients made significantly more errors in acquisition (feedback-based associative learning) than right-onset patients and normal controls, whereas right-onset patients performed as well as normal controls. There was no significant difference among these three groups in the accuracy of either retention or generalization phase. The three groups did not show significant differences in response time. In the left-onset group, there was an inverse relationship between acquisition errors and regional homogeneity in the right dorsal rostral putamen. There were no significant regional homogeneity changes in either the left or the right dorsal rostral putamen in right-onset patients when compared to controls. CONCLUSIONS: Motor-symptom laterality could affect feedback-based associative learning in PD, with left-onset medication-naïve patients being selectively impaired. Dysfunction in the right dorsal rostral putamen may underlie the observed deficit in associative learning in patients with left-sided onset.© 2016 International Parkinson and Movement Disorder Society.

The self and its resting state in consciousness: an investigation of the vegetative state.

Recent studies have demonstrated resting-state abnormalities in midline regions in vegetative state/unresponsive wakefulness syndrome and minimally conscious state patients. However, the functional implications of these resting-state abnormalities remain unclear. Recent findings in healthy subjects have revealed a close overlap between the neural substrate of self-referential processing and the resting-state activity in cortical midline regions. As such, we investigated task-related neural activity during active self-referential processing and various measures of resting-state activity in 11 patients with disorders of consciousness (DOC) and 12 healthy control subjects. Overall, the results revealed that DOC patients exhibited task-specific signal changes in anterior and posterior midline regions, including the perigenual anterior cingulate cortex (PACC) and posterior cingulate cortex (PCC). However, the degree of signal change was significantly lower in DOC patients compared with that in healthy subjects. Moreover, reduced signal differentiation in the PACC predicted the degree of consciousness in DOC patients. Importantly, the same midline regions (PACC and PCC) in DOC patients also exhibited severe abnormalities in the measures of resting-state activity, that is functional connectivity and the amplitude of low-frequency fluctuations. Taken together, our results provide the first evidence of neural abnormalities in both the self-referential processing and the resting state in midline regions in DOC patients. This novel finding has important implications for clinical utility and general understanding of the relationship between the self, the resting state, and consciousness.

Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study.

The anatomical connectivity of the human cerebral cortex resembles a "small-world" architecture, which is characterized by the coexistence of structurally segregated and integrative connectivity patterns. However, organizational differences in networks among individuals remain largely unknown. Here, we utilize diffusion tensor imaging tractography and graph-theoretical approaches to investigate the effects of sex and brain size on the topological organization of human cortical anatomical network. Weighted cortical networks were constructed from 72 young healthy participants by measuring anatomical connection densities between 78 cortical regions. As expected, all participants showed a small-world topology (high local clustering and short paths between nodes), which suggests a highly efficient topological organization. Furthermore, we found that females had greater local efficiencies than males. Moreover, smaller brains showed higher local efficiency in females but not in males, suggesting an interaction between sex and brain size. Specifically, we show that several brain regions (e.g., the precuneus, precentral gyrus, and lingual gyrus) had significant associations between nodal centrality and sex or brain size. Our findings suggest that anatomical network organization in the human brain is associated with sex and brain size and provide insights into the understanding of the structural substrates that underlie individual differences in behavior and cognition.

Raised SPINK1 levels play a role in angiogenesis and the transendothelial migration of ALL cells.

The present study was designed to assess whether raised Serine protease inhibitor Kazal type 1 (SPINK1) expressions modulates angiogenesis. Human umbilical vein endothelial cells (HUVECs) exposed to SPINK1 were noted to exhibit raised expressions of interleukin-8 (IL-8) as well as VCAM-1 and ICAM-1 cell adhesion molecules in a dose-dependent manner. In co-culture system of HUVECs and Acute lymphoblastic leukemia (ALL) cells, SPINK1 exposure also resulted in enhanced endothelial cell motility and ALL cells trans-endothelial migration. High concentrations of SPINK1 caused in vitro cellular reorganization into tubes in Matrigel-cultured HUVECs and induced in vivo vascularization and brain infiltration of NOD/SCID ALL model mice. The further transcriptomic analysis indicated that SPINK1 treatment altered several biological processes of endothelial cells and led to activation of the MAPK pathway. This study is the first to determine the neovascularization effects of raised SPINK1.

Increased functional connectivity of white-matter in myotonic dystrophy type 1.

Background: Myotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders. Materials and methods: Using resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores. Results: We identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks. Conclusion: Results demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.

Hypothesis of subcortical visual pathway impairment in schizophrenia.

Schizophrenia is a severe mental disease involving both neurological and psychiatric abnormalities. Previous studies mainly focus on damage to high-order cognitive dysfunction, which is related to high-level cortical regions such as the prefrontal and temporal lobes. Recent research reveals that impairment of low-level sensory processing occurs in the early stage of schizophrenia, which may be due to impairment of the subcortical magnocellular visual pathway. Moreover, the structure and function of some important nuclei in a subcortical visual pathway are reported to be abnormal in patients with schizophrenia. Inspired by the above evidence, we propose a hypothesis that impairment of the Superior Colliculus-Pulvinar-Amygdala subcortical visual pathway may be involved in the pathological mechanisms of early stages of schizophrenia. And we propose a possible method to detect dysfunction of this subcortical pathway through examining topological processing, which may help early diagnosis of schizophrenia.

Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data.

It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.

Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis.

Increasing evidence has shown that the resting state brain connectivity of default mode network (DMN) which are important for social cognition are disrupted in autism spectrum disorder (ASD). However, previous neuroimaging studies did not present consistent results. Therefore, we performed a meta-analysis of resting-state functional connectivity (rsFC) studies of DMN in the individuals with ASD and healthy controls (HCs) to provide a new perspective for investigating the pathophysiology of ASD. We carried out a search using the terms: ("ASD" OR "Autism") AND ("resting state" OR "rest") AND ("DMN" OR "default mode network") in PubMed, Web of Science and Embase to identify the researches published before January 2020. Ten resting state datasets including 203 patients and 208 HCs were included. Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method was applied to identify group differences. In comparison with the HCs, the patients with ASD showed increased connectivity in cerebellum, right middle temporal gyrus, superior occipital gyrus, right supramarginal gyrus, supplementary motor area and putamen. Decreased connectivity was discovered in some nodes of DMN, such as medial prefrontal cortex, precuneus and angular gyrus. These results may help us to further clarify the neurobiological mechanisms in patients with ASD.

Phase Transformation of Doped LiCoPO4 during Galvanostatic Cycling.

In situ X-ray diffraction was employed to investigate the crystal structure changes in Cr/Si co-doped Li(Co,Fe)PO4 cathode material during a galvanostatic charge/discharge process at a slow rate of C/30. The evolution of the X-ray patterns revealed that the phase transformation between the Cr/Si-Li(Co,Fe)PO4 and Cr/Si-(Co,Fe)PO4 is a two-step process, which involves the formation of an intermediate compound of Cr/Si-Li0.62(Co,Fe)PO4 upon the extraction of Li ions from the pristine phase. Different from the previously reported two biphasic transition steps, the phase transformation of the Cr/Si-Li(Co,Fe)PO4 followed a solid solution and a biphasic reaction pathway at different stages of the delithiation/lithiation process, respectively.

Highly Stable Red Quantum Dot Light-Emitting Diodes with Long T95 Operation Lifetimes.

Quantum dot light-emitting diodes (QLEDs) with an excellent external quantum efficiency (EQE) and an excellent lifetime almost meet the requirements for low-brightness displays. However, the short operation lifetime under high brightness limits the application of QLEDs in outdoor displays and lightings. Herein, we report a highly efficient, stable red QLED using co-doped lithium and magnesium as well as a magnesium oxide shell-coated zinc oxide nanoparticle layer as an electron transport layer (ETL). The optimized QLED has a high peak EQE of 20.6%, a low efficiency roll-off at high current, and a remarkably long lifetime T95 of >11000 h at 1000 cd m-2, which is an indication of the realization of the most stable red QLED to date. The improvement in the long term stability of the QLED is attributed to the use of a co-doped and shell-coated zinc oxide ETL with a reduced level of electron injection to improve the charge balance in the device.

Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level.

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.

High-Voltage Lithium-Ion Battery Using Substituted LiCoPO4: Electrochemical and Safety Performance of 1.2 Ah Pouch Cell.

A LiCoPO4-based high-voltage lithium-ion battery was fabricated in the format of a 1.2 Ah pouch cell that exhibited a highly stable cycle life at a cut-off voltage of 4.9 V. The high-voltage stability was achieved using a Fe-Cr-Si multi-ion-substituted LiCoPO4 cathode and lithium bis(fluorosulfonyl)imide in 1-methyl-1-propylpyrrolidinium bis(fluorosulfony)imide as the electrolyte. Due to the improved electrochemical stability at high voltage, the cell exhibited a stable capacity retention of 91% after 290 cycles without any gas evolution related to electrolyte decomposition at high voltage. In addition to improved cycling stability, the nominal 5 V LiCoPO4 pouch cell also exhibited excellent safety performance during a nail penetration safety test compared with a state-of-the-art lithium ion battery. Meanwhile, the thermal stabilities of the 1.2 Ah pouch cell as well as the delithiated LiCoPO4 were also studied by accelerating rate calorimetry (ARC), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and in situ X-ray diffraction (XRD) analyses and reported.

Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: a resting-state fMRI study.

The thalamus and visual cortex are two key components associated with the alpha power of electroencephalography. However, their functional relationship remains to be elucidated. Here, we employ resting-state functional MRI to investigate the temporal correlations of spontaneous fluctuations between the thalamus [the whole thalamus and its three largest nuclei (bilateral mediodorsal, ventrolateral and pulvinar nuclei)] and visual cortex under both eyes open and eyes closed conditions. The whole thalamus show negative correlations with the visual cortex and positive correlations with its contralateral counterpart in eyes closed condition, but which are significantly decreased in eyes open condition, consistent with previous findings of electroencephalography desynchronization during eyes open resting state. Furthermore, we find that bilateral thalamic mediodorsal nuclei and bilateral ventrolateral nuclei have remarkably similar connectivity maps, and resemble to those of the whole thalamus, suggesting their crucial contributions to the thalamus-visual correlations. The bilateral pulvinar nuclei are found to show distinct functional connectivity patterns, compatible with previous findings of the asymmetry of anatomical and functional organization in the nuclei. Our data provides evidence for the associations of intrinsic spontaneous neuronal activity between the thalamus and visual cortex under different resting conditions, which might have implications on the understanding of the generation and modulation of the alpha rhythm.

Links Between the Amplitude Modulation of Low-Frequency Spontaneous Fluctuation Across Resting State Conditions and Thalamic Functional Connectivity.

A comparison of the different types of resting state reveals some interesting characteristics of spontaneous brain activity that cannot be found in a single condition. Differences in the amplitude of low-frequency fluctuation (ALFF) between the eyes open (EO) and the eyes closed (EC) almost have a spatially distinct pattern with traditional EO-EC activation within sensory systems, suggesting the divergent functional roles of ALFF and activation. However, the underlying mechanism is far from clear. Since the thalamus plays an essential role in sensory processing, one critical step toward understanding the divergences is to depict the relationships between the thalamus and the ALFF modulation in sensory regions. In this preliminary study, we examined the association between the changes of ALFF and the changes of thalamic functional connectivity (FC) between EO and EC. We focused on two visual thalamic nuclei, the lateral geniculate nucleus (LGN) and the pulvinar (Pu). FC results showed that LGN had stronger synchronization with regions in lateral but not in medial visual networks, while Pu had a weaker synchronization with auditory and sensorimotor areas during EO compared with EC. Moreover, the patterns of FC modulation exhibited considerable overlaps with the ALFF modulation, and there were significant correlations between them across subjects. Our findings support the crucial role of the thalamus in amplitude modulation of low-frequency spontaneous activity in sensory systems, and may pave the way to elucidate the mechanisms governing distinction between evoked activation and modulation of low-frequency spontaneous brain activity.