General fluid intelligence (gF) is a current problem-solving ability, which shows high inter-individual differences in humans (Cattell, 1963). At the beginning of the last century, Spearman, 1904, proposed that some general or g factor contributes to our gF. One key component of gF is visuo-spatial intelligence, usually tested by visual materials, shows high g-loading (Colom et al., 2006; Deary et al., 2010; Jung and Haier, 2007). The exact neural mechanisms of the interplay of visuo-spatial intelligence with gF remain yet unclear, though.

The ‘neuro-efficiency’ hypothesis is one explanation for individual differences in gF (Haier et al., 1988). This hypothesis puts forward that the human brain’s ability to suppress irrelevant information leads to more efficient cognitive processing. Correspondingly, using a well-known visual motion paradigm (center-surround antagonism; Liu et al., 2016; Tadin et al., 2003), Melnick et al. found a strong link between suppression index (SI) of motion perception and the scores of the block design test (BDT, a subtest of the Wechsler Adult Intelligence Scale [WAIS]), which measures the visuo-spatial component (3D domain) of gF (Melnick et al., 2013). Motion surround suppression (SI), a specific function of human extrastriate cortical region, middle temporal complex (hMT+), aligns closely with this region’s activities (Gautama and Van Hulle, 2001). Furthermore, hMT+ is a sensory cortex involved in visual perception processing (3D domain; Cumming and DeAngelis, 2001). These findings suggest that hMT+ potentially plays a significant role in 3D visuo-spatial gF by facilitating the efficient processing of 3D visual information and suppressing irrelevant information. However, more evidence is needed to uncover how the hMT+ functions as a core region for 3D visuo-spatial intelligence.

Frontal cortex is usually recognized as the cognitive core region (Duncan et al., 2000; Gray et al., 2003). Strong connectivity between the cognitive regions suggests a mechanism for large-scale information exchange and integration in the brain (Barbey, 2018; Cole et al., 2012). Therefore, the potential conjunctive coding may overlap with the inhibition and/or excitation mechanism of hMT+. Taken together, we hypothesized that 3D visuo-spatial intelligence (as measured by BDT) might be predicted by the inhibitory and/or excitation mechanisms in hMT+ and the integrative functions connecting hMT+ with frontal cortex (Figure 1a).

Figure 1

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To investigate our hypothesis, this work conducted multi-level examination including biochemical (glutamatergic-GABAergic in hMT+), regional-systemic (brain connectivity with hMT+-based), and behavioral (visual motion function in hMT+) levels to reveal if hMT+ contributes to the 3D visuo-spatial component of gF. We employ ultra-high field (7T) magnetic resonance spectroscopy (MRS) technology to reliably resolve GABA and Glu concentrations (Ende, 2015; Liu et al., 2022; Song et al., 2021). To verify the specificity of hMT+, we used early visual cortex (EVC, primarily in V1)-based GABA/Glu as control as it mediates the 2D rather than 3D visual domain (Cumming and DeAngelis, 2001).

Our findings first demonstrate that GABAergic inhibition mechanisms (but not excitatory Glu) in hMT+ region relate to 3D visuo-spatial ability. Further, analysis of functional brain connectivity at rest reveals that the network (between MT+ and frontal cortex) relating to MT+ GABA and perceptual suppression contribute to the visuo-spatial intelligence. Our results provide direct evidence that inhibitory mechanisms centered on GABA levels in MT+ region (a sensory cortex) mediate multi-level visuo-spatial component (3D domain) of gF thus drawing a direct connection of biochemistry, brain connectivity, and behavior.

To determine whether the function of hMT+ cortex contributes to visuo-spatial component (3D domain) of gF, we adopted the experimental design depicted in Figure 1b. Thirty-six healthy subjects participated in this study. Participants underwent two MRI sessions: the first encompassing resting-state fMRI and MRS, and the second solely involving MRS. A 30 min interval separated these sessions, during which participants performed motion discrimination tasks (using center-surround antagonism stimuli; Tadin et al., 2003) and the BDT, which assesses the visuo-spatial ability (3D domain) of gF (Fangmeier et al., 2006). In the motion discrimination tasks, a grating of either large or small size was randomly presented at the center of the screen. The grating drifted either leftward or rightward, and participants were asked to judge the perceived moving direction. While in the BDT, participants were asked to rebuild the figural pattern within a specified time limit using a set of red and white blocks. Both the volume-of-interests (VOIs) of MRS scanning in the left hMT+ (targeted brain area) and the left EVC (primarily in V1, control brain area) had dimensions of 2×2×2 cm³, and the MRS scanning sequences were randomized across the two sessions. The hMT+ MRS VOIs were demarcated using an anatomical landmark (Dumoulin et al., 2000). For 14 subjects, we also utilized fMRI to functionally pinpoint the hMT+ to validate the placement of the VOI (Figure 2a and b). The EVC (primarily in V1) MRS VOIs (Figure 2—figure supplement 1) were anatomically defined (Materials and methods). Here, MRS data after extensive quality control (31/36 in hMT+, and 28/36 in EVC [primarily in V1]) were taken for further analysis (Materials and methods).

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GABA and Glu concentrations in hMT+ and EVC (primarily in V1) and their relation to SI and BDT

An example of an MRS voxel located in hMT+ is shown in Figure 3a. LCModel fittings for GABA spectra from all subjects in hMT+ (n=31) and EVC (primarily in V1) (n=28) are illustrated in Figure 3b (color scale presents the BDT scores). We discerned a significant association between the inter-subjects’ BDT scores and the GABA levels in hMT+ voxels, but not in EVC (primarily in V1) voxels. Quantitative analysis displayed that BDT significantly correlates with GABA concentrations in hMT+ voxels (r=0.39, p=0.03, n=31, Figure 3c). After using partial correlation to control for the effect of age, the relationship remains significant (r_partial = 0.426, p=0.02, one participant excluded due to the age greater than mean + 2.5 SD). In contrast, there was no obvious correlation between BDT and GABA levels in EVC (primarily in V1) voxels (Figure 3—figure supplement 1a). We show that SI significantly correlates with GABA levels in hMT+ voxels (r=0.44, p=0.01, n=31, Figure 3d). In contrast, no significant correlation between SI and GABA concentrations in EVC (primarily in V1) voxels was observed (Figure 3—figure supplement 1b). These findings suggest that the relationship between motion suppression and GABA+ is specific to hMT+, but not in EVC (primarily in V1), which is in line with prior results (Schallmo et al., 2018). LCModel fittings for Glu spectra from all subjects in hMT+ (n=31) and EVC (primarily in V1) (n=28) voxels are presented in Figure 3—figure supplement 2a.

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Unlike in the case of GABA, no significant correlations between BDT and Glu levels were found in both hMT+ and EVC (primarily in V1) voxels (Figure 3—figure supplement 2b and c). While, as expected (Song et al., 2021), we observed significant positive correlations between GABA and Glu concentrations in both hMT+ (r=0.62, p=0.0002, n=31) and EVC (primarily in V1) voxels (r=0.56, p=0.002, n=28; Figure 3—figure supplement 3a and b). Additionally, significant correlations between SI and BDT, duration threshold of small grating and BDT was discerned (r=0.59, p=0.0002, n=34, Figure 3e, r_partial = 0.67, p<0.001, one participant excluded due to the age greater than mean + 2.5 SD; r=–0.43, p=0.016, r_partial = 0.44, p=0.014, Figure 3f). While there was no significant correlation between duration threshold of large grating and BDT (Figure 3g), corroborating previous conclusions (Melnick et al., 2013). Two outliers evident in Figure 3e were excluded, with consistent results depicted in Figure 3—figure supplement 4a. Further, two outliers evident in Figure 3d were excluded, with consistent results depicted in Figure 3—figure supplement 4b .

MT-frontal FC relates to SI and BDT

We next took the left hMT+ as the seed region and separately measured interregional FCs between the seed region and each voxel in the frontal regions (a priori search space). These measurements were correlated with performance in 3D visuo-spatial ability (BDT) to identify FCs with significant correlations. Results from connectivity-BDT analysis are summarized in Table 1 and shown in Figure 4a. We found that brain regions with FC strength to the seed region (left hMT+) significantly correlated with BDT scores were situated within the canonical cognitive cores of fronto-parietal network (FPN) (Brodmann areas [BAs] 6, 9, 10, 46, 47; Assem et al., 2020; Deary et al., 2010; Duncan et al., 2020; Duncan et al., 2000; Gray et al., 2003; Jung and Haier, 2007). Across the whole-brain search, the similar FCs (between hMT+ and frontal cognitive cores) still showed significant correlations with BDT scores (Supplementary file 1; also shown in Figure 4—figure supplement 1a). Additionally, we identified certain parietal regions (BAs 7, 39, 40) with significant correlations between their connectivity to the left hMT+ and the BDT scores (Supplementary file 1; also shown in Figure 4—figure supplement 1a). These significant connections between hMT+ and FPN system suggest that left hMT+ is involved in the efficient information integration network mediating the visuo-spatial component of gF.

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Table 1

FC number	Connected regions	BA	Size	Peak coordinate	r	p
				MNI (x, y, z)
1	Frontal_Sup_Orb_R	11	33	(12,63,–19.5)	–0.57	0.0011
2	Frontal_Inf_Orb_L	47	24	(−34.5,28.5,–13.5)	–0.63	0.0003
3	Frontal_Med_Orb_R	11	41	(3,43.5,–12)	–0.58	0.0009
4	Frontal_Inf_Orb_R	47	48	(−31.5,24,–12)	0.59	0.0008
5	Frontal_Inf_Orb_R	47	29	(25.5,30,–13.5)	0.67	0.0001
6	Insula_L	\	26	(–28.5,27,0)	0.67	0.0001
7	Frontal_Inf_Oper_R	45	41	(43.5,16.5,6)	0.64	0.0002
8	Frontal_Sup_R	10	25	(31.5,57,9)	0.59	0.0008
9	Frontal_Mid_L	10	82	(–33,48,12)	0.62	0.0003
10	Frontal_Inf_Oper_R	44	49	(51,7.5,21)	0.59	0.0007
11	Frontal_Inf_Oper_R	46	96	(49.5,16.5,28.5)	–0.62	0.0003
12	Frontal_Mid_L	10	32	(–31.5,49.5,24)	0.57	0.0012
13	Frontal_Mid_R	10	102	(31.5,36,30)	0.59	0.0009
14	Precentral_L	6	46	(−49.5,–1.5,34.5)	0.59	0.0007
15	Frontal_Mid_R	9	107	(51,19.5,40.5)	–0.67	0.0001
16	Frontal_Sup_L	9	35	(–9,60,37.5)	–0.69	0.0001
17	Frontal_Sup_Medial_R	9	74	(4.5,52.5,43.5)	–0.57	0.0011
18	Frontal_Sup_R	6	136	(28.5,–7.5,63)	–0.64	0.0002
19	Supp_Motor_Area_L	6	48	(–10.5,6,54)	0.63	0.0003
20	Frontal_Mid_L	6	119	(–24,4.5,55.5)	0.63	0.0003
21	Precentral_L	6	229	(−24,–18,66)	0.60	0.0005
22	Frontal_Sup_R	6	32	(16.5,–18,67.5)	0.68	0.0001
23	Precentral_R	6	80	(30,–24,70.5)	0.70	0.0001
24	Precentral_R	6	23	(16.5,–25.5,76.5)	0.70	0.0001

1. Single voxel threshold p<0.005 (t>3.057 or t<–3.057), adjacent size ≥23 voxels (AlphaSim corrected).

To address the question whether spatial suppression plays a role, we correlated hMT+-based global FCs with SI. Though spatial suppression during motion perception (quantified by SI) is considered to be the function of area hMT+(Gautama and Van Hulle, 2001; Tadin et al., 2011), the top-down modulation from the frontal cortex can increase surround suppression (Liu et al., 2016). Our FC-SI analysis in the frontal regions (a priori search space) displayed three brain regions in which FCs strength significantly correlated with SI: right BA4/6, left BA6, and right BA46 (summarized in Supplementary file 2, and shown in Figure 4b). Across the whole-brain search, we identified total seven brain regions in which FCs strength significantly correlated with SI, and three of these were in the frontal cortex. This is consistent with the results obtained by the FC-SI analysis in a priori search space (frontal cortex; Supplementary file 3 and Figure 4—figure supplement 1b).

We also did the V1 FC-BDT correlations as control analysis (Figure 4—figure supplement 2). Only positive correlations in the frontal area were detected, suggesting that in the 3D visuo-spatial intelligence task, V1 plays a role in feedforward information processing. However, hMT+, which showed specific negative correlations in the frontal, be suggested involving in the inhibition mechanism. These results further emphasize the de-redundancy function of hMT+ in 3D visuo-spatial intelligence.

Local hMT+ GABA acts on SI and BDT via global hMT-frontal connectivity

To determine whether local neurotransmitter levels (such as GABA and Glu) in the hMT+ region mediate the broader 3D visuo-spatial ability of BDT, which as a component of gF, is linked to the frontal cortex (Fangmeier et al., 2006), we correlated the significant FCs of hMT-frontal in Figure 4a (also shown in Table 1) with the GABA and Glu levels in hMT+ region. The results revealed that only two FCs significantly correlated with inhibitory GABA levels in hMT+: (1) the FC of left hMT+-right BA46 (significantly negative correlation, r = –0.56, p=0.02, n=29, false discovery rate [FDR] correction, Figure 5a left); (2) the FC of left hMT+-right BA6 (significantly positive correlation, r=0.69, p=0.002, n=29, FDR correction, Figure 5b left; also shown in Table 2). There were no significant correlations between these FCs and the excitatory Glu levels in hMT+ (Table 2). Across the whole-brain search, we obtained the same two hMT+-frontal FCs significantly correlating with both hMT+ GABA levels and BDT (Supplementary file 4), this is consistent with the results in the a priori search space (frontal cortex; Table 2). We then correlated the significant FCs in Figure 5b (also in Supplementary file 2) with GABA and Glu concentrations in hMT+ and found that almost all the correlations are significant except one (between the FC of left hMT+-right BA46 and the Glu levels in hMT+; Supplementary file 5). Among the three FCs, the clusters of two FCs have substantial voxel overlap with the FCs we found by the connectivity-BDT analysis (Figure 5a and b). Across the whole-brain search, there were total seven brain regions in which FCs strength were significantly correlated with SI, all the seven FCs significantly correlated the hMT+ GABA levels, while no FC had significant correlation with the hMT+ Glu levels (Supplementary file 6).

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Table 2

FC number	hMT+ GABA concentrations			hMT+ Glu concentrations
	r	p	FDR	r	p	FDR
1	–0.07	0.72	0.75	–0.11	0.58	0.85
2	–0.28	0.14	0.36	–0.27	0.15	0.81
3	–0.13	0.52	0.59	–0.07	0.71	0.85
4	0.10	0.59	0.64	0.12	0.54	0.85
5	0.14	0.48	0.58	0.24	0.21	0.81
6	0.31	0.11	0.33	0.15	0.43	0.85
7	0.20	0.30	0.48	0.07	0.74	0.85
8	0.14	0.45	0.58	0.05	0.79	0.85
9	0.20	0.30	0.48	0.10	0.60	0.85
10	–0.13	0.49	0.58	–0.16	0.41	0.85
11	–0.56	0.0018	0.02*	–0.22	0.25	0.81
12	0.18	0.34	0.51	0.15	0.43	0.85
13	0.20	0.30	0.48	0.05	0.81	0.85
14	0.39	0.04	0.14	0.22	0.24	0.81
15	–0.40	0.03	0.12	–0.21	0.27	0.81
16	–0.27	0.15	0.36	–0.12	0.53	0.85
17	0.17	0.37	0.52	0.06	0.74	0.85
18	0.26	0.18	0.39	0.16	0.40	0.85
19	0.39	0.03	0.12	0.31	0.10	0.81
20	0.01	0.98	0.98	0.14	0.46	0.85
21	0.40	0.03	0.12	0.24	0.21	0.81
22	0.22	0.25	0.48	0.06	0.76	0.85
23	0.69	0.0001	0.002**	0.47	0.01	0.24
24	0.41	0.03	0.12	0.001	0.97	0.97

1. ^*: p_FDR < 0.05; ^**: p_FDR < 0.01; ^***: p_FDR < 0.001; Bold font indicates the significant correlations survived from multi-correlation correction.

Taken together, our results displayed that the overlap FCs from the analyses of connectivity-behavior (BDT and SI) -GABA are the hMT+-BA46 and hMT+-BA6 (Figure 5a and b). These results suggest that the FCs of hMT+-frontal regions (BA46 and BA6) coupling with local hMT+ GABA provides the neural basis for both the simple motion perception (quantified by SI) and the complex 3D visuo-spatial ability (quantified by BDT).

In order to fully investigate the potential roles of the multiples variables contributing to BDT scores, serial mediation analyses (Hayes, 2013) were applied to both the MR and behavioral data. Following our hypothesis, the independent variable (X) is hMT+ GABA, the dependent variable (Y) is BDT scores, the covariate is the age, and the mediators are FC (M1) and SI (M2). We used the overlap clusters from the analyses of connectivity-BDT-GABA and connectivity-SI-GABA to compute the FC of hMT+-BA46, one participant was excluded due to his age greater than mean + 2.5 SD. The serial mediation model is shown in Figure 5c. GABA levels in hMT+ significantly negatively correlated with the FC of hMT+-BA46 (β = –0.32, p=0.0009), which in turn significantly negatively correlated with SI (β = –0.19, p=0.035), and consequently, significantly positively correlated with BDT (β=38.5, p=0.009). Critically, bootstrapped analyses revealed that our hypothesized indirect effect (i.e. hMT+ GABA → FC of hMT+-BA46 → SI → BDT) was significant (β=2.28, SE = 1.54, 95% CI = [0.03, 5.94]). The model accounted for 34% of the variance in BDT. However, when considering the hMT-BA6 FC as the mediator M1, the serial model does not show a significant indirect effect. Consequently, we explored a mediation model, which revealed that the hMT-BA6 FC totally mediates the relationship between GABA and BDS (Figure 5d). For sensitivity purposes, we tested the alternative models, in which the order of the mediators was reversed. The pathway that hMT+ GABA was predicted to be associated with SI, followed by the FC of hMT+-BA46, and then BDT, did not yield the chained mediation effects on BDT (Figure 5—figure supplement 1).

To summarize (shown in Figure 6), the results from the serial mediation analyses are consistent with our hypothesis. That is, higher GABAergic inhibition in hMT+ relates to stronger negative FC between hMT+ and BA46, leading to enhanced ability for surround suppression (filtering out irrelevant information; Tadin, 2015), which ultimately resulting in more efficient visual 3D processing as key component of gF (the higher BDT scores).

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Here, we provide evidence that hMT+ inhibitory mechanisms mediate processing in the visuo-spatial component (3D domain) of gF on multiple levels, i.e., from molecular over brain connectivity to behavior. First, this study found that higher hMT+ inhibitory GABA levels (but not excitatory Glu) relate to FC between hMT+ and BA46 that contribute to both SI and BDT. Our serial mediation analyses indicate that the inhibitory mechanisms related to hMT+ and its GABA levels in hMT+ (but not Glu), FCs of hMT+-BA46 coupling with hMT+ inhibitory GABA (but not excitatory Glu), and behavior (SI indexing perceptual suppression in hMT+) predict the inter-subject variance in the 3D gF task (BDT; Figure 5c). Second, we demonstrate discrete GABAergic inhibition mechanisms in hMT+ that mediate the strong FCs between hMT+-frontal regions (BA46 and BA6): significant negative correlation with the FC of hMT+-BA46 (Figure 5a), whereas there is significant positive correlation with the FC of hMT+-BA6 (Figure 5b). This indicates that different frontal regions, DLPFC (BA46) and premotor cortex (BA6), contribute uniquely to gF through hMT+-based inhibitory mechanisms.

The goal of our research is to reveal that the inhibitory (not excitatory) mechanism in hMT+ contributes to multi-level processing in 3D visuo-spatial ability (BDT). Monkey electrophysiological experiments revealed that selective attention gates the visual cortex, including area MT, effectively suppressing the irrelevant information (Everling et al., 2002; Treue and Maunsell, 1996). These findings align with the ‘neural efficiency’ hypothesis of intelligence (Haier et al., 1988), which puts forward the human brain’s ability to suppress the repetition of information. Neural suppression is associated with the balance between excitation and inhibition (EIB), usually represented by covariation between Glu and GABA (Ozeki et al., 2009). Here, this study exploited the high spectral resolution afforded by ultra-high field (7T) MRS to reliably resolve GABA measurement, to adequately discriminate the Glu and glutamine signals, and to resolve the high-accuracy Glu measurement (Ende, 2015).

This work implemented the MRS scanning in hMT+ (3D visual domain) and EVC (primarily in V1) (2D visual domain) regions and found that hMT+ inhibitory GABA (but not excitatory Glu) significantly correlated with BDT, i.e., the higher GABA levels in hMT+ (rather than excitatory Glu) relate to higher visual 3D processing (BDT; Figure 3c). Basically, this study contains the data of SI, BDT, GABA in MT+ and EVC (primarily in V1), Glu in MT+, and EVC (primarily in V1)-all six measurements. We made a correlation matrix to reporting all values in Figure 6—figure supplement 1.

We searched the global hMT+-based FCs with the connectivity-BDT analyses (in a priori search space and whole-brain search to valid), and then, correlated these significant FCs with the GABA and Glu concentrations in hMT+. We found two FCs (hMT+-BA46 and hMT+-BA6) significantly correlating with hMT+ inhibitory GABA (whereas no FC significantly correlated with hMT+ excitatory Glu). Accordingly, our results emphasize the importance of hMT+ inhibitory GABA (but not excitatory Glu) in processing the 3D visual-spatial intelligence (BDT).

Our recent human study (Song et al., 2021) and other study’s animal experiments Ozeki et al., 2009; Sato et al., 2016 demonstrated that the conjoint action of inhibition (GABA) and excitation (Glu) underlies visual spatial suppression. In this work, our novel data show the chained mediation effects from local hMT+ GABA to more global BDT: hMT+ GABA → FC (hMT+ and BA46) → SI → BDT. Thereby, our data indicate that inhibitory mechanisms in hMT+, from the biochemical level of GABA over FC to the behavioral level, can predict the inter-subject variance in the 3D gF task (BDT; Figure 5c).

Another interesting finding reveals that GABAergic inhibition in hMT+ coupling with distinct FC patterns between BA46-hMT+ and BA6-hMT+. A previous human fMRI experiment found that the positive and negative correlations between BDT and the activation of frontal regions appeared at different reasoning phases (validation or integration phases during reasoning; Fangmeier et al., 2006). On the one hand, a monkey electrophysiological experiment reported the delayed modulation from PFC (especially in DLPFC; BA46) to area MT during a visual motion task (Zaksas and Pasternak, 2006). Computational models converged with empirical data of awake monkey experiments slowing temporal modulation from PFC to MT/medial superior temporal (MST; Donner et al., 2009; Siegel et al., 2015; Wang, 2002; Wimmer et al., 2015). On the other hand, human MEG studies (Donner et al., 2009; Wilming et al., 2020) reported that the gamma-band activity in the visual cortex (including area MT) exhibited high coherence with the activity in (pre-) motor regions (BA4/6). These results suggest that the relation of long-range FC and local inhibitory mechanism (hMT+ GABA) support our findings that inhibition in hMT+ contributes to efficient long-range integration and coordination in distant brain areas like the prefrontal and premotor cortex.

How does hMT+ assemble into the cognitive system as an intellectual hub rather than a simple input module? The results in Figure 5a and b showed that the overlap brain regions from the analyses of connectivity-BDT-GABA/connectivity-SI-GABA are the hMT+-BA46. This overlap couples with local visual suppression (SI) and consequently plays an important role in intelligence (BDT). The direction discrimination task in this work (the visual motion paradigm of center-surround antagonism) was previously considered a mainly local function of hMT+ (Melnick et al., 2013; Tadin, 2015; Tadin et al., 2003). However, our results with connectivity-SI analyses revealed that both local (FC within BA18) and global brain connectivity (FC between hMT+ and frontal regions) contribute to SI (Supplementary file 3). In human psychophysical experiments (Melnick et al., 2013; Tadin et al., 2003) the brief stimulus duration (~100 ms) in motion discrimination precludes most top-down attentional effects (Wang, 2002; Zaksas and Pasternak, 2006), while attention, which predicted the performance of the motion discrimination task, was sustained throughout the stimulus intervals (Siegel et al., 2015). Furthermore, animal experiments have revealed that the local circuits in the visual cortex combining with top-down modulation and intracortical horizontal connection mediate the visual-spatial suppression (Angelucci et al., 2002; Keller et al., 2020; Li et al., 2019; Zhang et al., 2014).

Our results (shown in Figure 5a and b, right) present the intrinsic binding of local GABAergic inhibition in hMT+, which suppresses redundancy of visual motion processing (SI), and the activity of brain connectivity between hMT+ and frontal regions. These individual inherent traits may contribute to the individual difference in 3D visuo-spatial ability (Figure 5a and b, left). A candidate divisive normalization model (Carandini and Heeger, 2011; Reynolds and Heeger, 2009) can explain how such reverberation affects the process of suppressing the irrelevant information, from perception to intelligence (Melnick et al., 2013; Tadin, 2015). We summarize a framework (Figure 6) to indicate and visualize our findings.

Recently, Duncan et al. demonstrated coding of gF in distributed regions, defining them as part of multi-demand (MD) systems (Assem et al., 2020; Duncan et al., 2020). The MD system encompasses a range of cognitive domains, including working memory, mathematics, language, and relational reasoning. According to Melnick et al., 2013, motion surround suppression (SI) and time thresholds for small and large gratings, which reflect hMT+ functionality, are correlated with Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed indicators. Additionally, Fedorenko et al. identified MD activation regions around the occipito-temporal areas, potentially overlapping with hMT+ (Fedorenko et al., 2013). As a key region in the representation of sensory flows (including optic and auditory flows; Fetsch et al., 2011; Gu et al., 2006), hMT+ shows potential to be central to the MD system. Future research could focus on multi-task paradigms to further investigate the mechanisms of hMT+ and its relationship with broader cognitive functions.

Together, this study offers a comprehensive insight into how the information exchange and integration between the sensory cortex (hMT+) and cognition core of BA46, coupling with the hMT+ GABA, can predict the performance of 3D visuo-spatial ability (BDT). Our results provide direct evidence that a sensory cortex area (hMT+), its GABA biochemistry, FC, and cognition behavior levels can assemble into complex cognition as an intellectual hub.

Source data are provided with this paper and have been archived at Zenodo.

1. 1. Yuan G

   (2024) Zenodo

   GABA-ergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.

   https://doi.org/10.5281/zenodo.13753668

1. 1. Angelucci A
   2. Levitt JB
   3. Walton EJS
   4. Hupe JM
   5. Bullier J
   6. Lund JS

   (2002) Circuits for local and global signal integration in primary visual cortex

   The Journal of Neuroscience 22:8633–8646.

   https://doi.org/10.1523/JNEUROSCI.22-19-08633.2002

   • PubMed
   • Google Scholar
2. 1. Assem M
   2. Glasser MF
   3. Van Essen DC
   4. Duncan J

   (2020) A domain-general cognitive core defined in multimodally parcellated human cortex

   Cerebral Cortex 30:4361–4380.

   https://doi.org/10.1093/cercor/bhaa023

   • PubMed
   • Google Scholar
3. 1. Barbey AK

   (2018) Network neuroscience theory of human intelligence

   Trends in Cognitive Sciences 22:8–20.

   https://doi.org/10.1016/j.tics.2017.10.001

   • Google Scholar
4. 1. Boucard CC
   2. Hoogduin JM
   3. van der Grond J
   4. Cornelissen FW

   (2007) Occipital proton magnetic resonance spectroscopy (1H-MRS) reveals normal metabolite concentrations in retinal visual field defects

   PLOS ONE 2:e222.

   https://doi.org/10.1371/journal.pone.0000222

   • PubMed
   • Google Scholar
5. 1. Brainard DH

   (1997)

   The psychophysics toolbox

   Spatial Vision 10:433–436.

   • PubMed
   • Google Scholar
6. 1. Carandini M
   2. Heeger DJ

   (2011) Normalization as a canonical neural computation

   Nature Reviews. Neuroscience 13:51–62.

   https://doi.org/10.1038/nrn3136

   • PubMed
   • Google Scholar
7. 1. Cattell RB

   (1963) Theory of fluid and crystallized intelligence: A critical experiment

   Journal of Educational Psychology 54:1–22.

   https://doi.org/10.1037/h0046743

   • Google Scholar
8. 1. Cavassila S
   2. Deval S
   3. Huegen C
   4. van Ormondt D
   5. Graveron-Demilly D

   (2001) Cramér-Rao bounds: An evaluation tool for quantitation

   NMR in Biomedicine 14:278–283.

   https://doi.org/10.1002/nbm.701

   • PubMed
   • Google Scholar
9. 1. Chen X
   2. Fan X
   3. Hu Y
   4. Zuo C
   5. Whitfield-Gabrieli S
   6. Holt D
   7. Gong Q
   8. Yang Y
   9. Pizzagalli DA
   10. Du F
   11. Ongur D

   (2019) Regional GABA concentrations modulate inter-network resting-state functional connectivity

   Cerebral Cortex 29:1607–1618.

   https://doi.org/10.1093/cercor/bhy059

   • PubMed
   • Google Scholar
10. 1. Cole MW
    2. Yarkoni T
    3. Repovs G
    4. Anticevic A
    5. Braver TS

    (2012) Global connectivity of prefrontal cortex predicts cognitive control and intelligence

    The Journal of Neuroscience 32:8988–8999.

    https://doi.org/10.1523/JNEUROSCI.0536-12.2012

    • PubMed
    • Google Scholar
11. 1. Colom R
    2. Jung RE
    3. Haier RJ

    (2006) Distributed brain sites for the g-factor of intelligence

    NeuroImage 31:1359–1365.

    https://doi.org/10.1016/j.neuroimage.2006.01.006

    • PubMed
    • Google Scholar
12. 1. Cumming BG
    2. DeAngelis GC

    (2001) The physiology of stereopsis

    Annual Review of Neuroscience 24:203–238.

    https://doi.org/10.1146/annurev.neuro.24.1.203

    • PubMed
    • Google Scholar
13. 1. Deary IJ
    2. Penke L
    3. Johnson W

    (2010) The neuroscience of human intelligence differences

    Nature Reviews. Neuroscience 11:201–211.

    https://doi.org/10.1038/nrn2793

    • PubMed
    • Google Scholar
14. 1. Donner TH
    2. Siegel M
    3. Fries P
    4. Engel AK

    (2009) Buildup of choice-predictive activity in human motor cortex during perceptual decision making

    Current Biology 19:1581–1585.

    https://doi.org/10.1016/j.cub.2009.07.066

    • PubMed
    • Google Scholar
15. 1. Dumoulin SO
    2. Bittar RG
    3. Kabani NJ
    4. Baker CL Jr
    5. Le Goualher G
    6. Bruce Pike G
    7. Evans AC

    (2000) A new anatomical landmark for reliable identification of human area V5/MT: A quantitative analysis of sulcal patterning

    Cerebral Cortex 10:454–463.

    https://doi.org/10.1093/cercor/10.5.454

    • PubMed
    • Google Scholar
16. 1. Duncan J
    2. Seitz RJ
    3. Kolodny J
    4. Bor D
    5. Herzog H
    6. Ahmed A
    7. Newell FN
    8. Emslie H

    (2000) A neural basis for general intelligence

    Science 289:457–460.

    https://doi.org/10.1126/science.289.5478.457

    • PubMed
    • Google Scholar
17. 1. Duncan J
    2. Assem M
    3. Shashidhara S

    (2020) Integrated intelligence from distributed brain activity

    Trends in Cognitive Sciences 24:838–852.

    https://doi.org/10.1016/j.tics.2020.06.012

    • PubMed
    • Google Scholar
18. 1. Ende G

    (2015) Proton magnetic resonance spectroscopy: Relevance of glutamate and GABA to neuropsychology

    Neuropsychology Review 25:315–325.

    https://doi.org/10.1007/s11065-015-9295-8

    • PubMed
    • Google Scholar
19. 1. Ernst T
    2. Kreis R
    3. Ross BD

    (1993) Absolute quantitation of water and metabolites in the human brain. I. compartments and water

    Journal of Magnetic Resonance, Series B 102:1–8.

    https://doi.org/10.1006/jmrb.1993.1055

    • Google Scholar
20. 1. Everling S
    2. Tinsley CJ
    3. Gaffan D
    4. Duncan J

    (2002) Filtering of neural signals by focused attention in the monkey prefrontal cortex

    Nature Neuroscience 5:671–676.

    https://doi.org/10.1038/nn874

    • PubMed
    • Google Scholar
21. 1. Fangmeier T
    2. Knauff M
    3. Ruff CC
    4. Sloutsky V

    (2006) FMRI evidence for a three-stage model of deductive reasoning

    Journal of Cognitive Neuroscience 18:320–334.

    https://doi.org/10.1162/089892906775990651

    • PubMed
    • Google Scholar
22. 1. Fedorenko E
    2. Duncan J
    3. Kanwisher N

    (2013) Broad domain generality in focal regions of frontal and parietal cortex

    PNAS 110:16616–16621.

    https://doi.org/10.1073/pnas.1315235110

    • PubMed
    • Google Scholar
23. 1. Fetsch CR
    2. Pouget A
    3. DeAngelis GC
    4. Angelaki DE

    (2011) Neural correlates of reliability-based cue weighting during multisensory integration

    Nature Neuroscience 15:146–154.

    https://doi.org/10.1038/nn.2983

    • PubMed
    • Google Scholar
24. 1. Frahm J
    2. Bruhn H
    3. Gyngell ML
    4. Merboldt KD
    5. Hänicke W
    6. Sauter R

    (1989) Localized high-resolution proton NMR spectroscopy using stimulated echoes: initial applications to human brain in vivo

    Magnetic Resonance in Medicine 9:79–93.

    https://doi.org/10.1002/mrm.1910090110

    • PubMed
    • Google Scholar
25. 1. Friston KJ
    2. Williams S
    3. Howard R
    4. Frackowiak RS
    5. Turner R

    (1996) Movement-related effects in fMRI time-series

    Magnetic Resonance in Medicine 35:346–355.

    https://doi.org/10.1002/mrm.1910350312

    • PubMed
    • Google Scholar
26. 1. Gautama T
    2. Van Hulle MM

    (2001) Function of center-surround antagonism for motion in visual area MT/V5: A modeling study

    Vision Research 41:3917–3930.

    https://doi.org/10.1016/s0042-6989(01)00246-2

    • PubMed
    • Google Scholar
27. 1. Gray JR
    2. Chabris CF
    3. Braver TS

    (2003) Neural mechanisms of general fluid intelligence

    Nature Neuroscience 6:316–322.

    https://doi.org/10.1038/nn1014

    • PubMed
    • Google Scholar
28. 1. Gruetter R

    (1993) Automatic, localized in vivo adjustment of all first- and second-order shim coils

    Magnetic Resonance in Medicine 29:804–811.

    https://doi.org/10.1002/mrm.1910290613

    • PubMed
    • Google Scholar
29. 1. Gu Y
    2. Watkins PV
    3. Angelaki DE
    4. DeAngelis GC

    (2006) Visual and nonvisual contributions to three-dimensional heading selectivity in the medial superior temporal area

    The Journal of Neuroscience 26:73–85.

    https://doi.org/10.1523/JNEUROSCI.2356-05.2006

    • PubMed
    • Google Scholar
30. 1. Haier RJ
    2. Siegel BV
    3. Nuechterlein KH
    4. Hazlett E
    5. Wu JC
    6. Paek J
    7. Browning HL
    8. Buchsbaum MS

    (1988) Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography

    Intelligence 12:199–217.

    https://doi.org/10.1016/0160-2896(88)90016-5

    • Google Scholar
31. Book

    1. Hayes AF

    (2013)

    Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression‐Based Approach

    New York: The Guilford Press.

    • Google Scholar
32. 1. Huk AC
    2. Dougherty RF
    3. Heeger DJ

    (2002) Retinotopy and functional subdivision of human areas MT and MST

    The Journal of Neuroscience 22:7195–7205.

    https://doi.org/10.1523/JNEUROSCI.22-16-07195.2002

    • PubMed
    • Google Scholar
33. 1. Jung RE
    2. Haier RJ

    (2007) The parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence

    The Behavioral and Brain Sciences 30:135–154.

    https://doi.org/10.1017/S0140525X07001185

    • PubMed
    • Google Scholar
34. 1. Keller AJ
    2. Roth MM
    3. Scanziani M

    (2020) Feedback generates a second receptive field in neurons of the visual cortex

    Nature 582:545–549.

    https://doi.org/10.1038/s41586-020-2319-4

    • PubMed
    • Google Scholar
35. 1. Li M
    2. Song XM
    3. Xu T
    4. Hu D
    5. Roe AW
    6. Li CY

    (2019) Subdomains within orientation columns of primary visual cortex

    Science Advances 5:eaaw0807.

    https://doi.org/10.1126/sciadv.aaw0807

    • PubMed
    • Google Scholar
36. 1. Liu LD
    2. Haefner RM
    3. Pack CC

    (2016) A neural basis for the spatial suppression of visual motion perception

    eLife 5:e16167.

    https://doi.org/10.7554/eLife.16167

    • PubMed
    • Google Scholar
37. 1. Liu DY
    2. Ju X
    3. Gao Y
    4. Han JF
    5. Li Z
    6. Hu XW
    7. Tan ZL
    8. Northoff G
    9. Song XM

    (2022) From molecular to behavior: Higher order occipital cortex in major depressive disorder

    Cerebral Cortex 32:2129–2139.

    https://doi.org/10.1093/cercor/bhab343

    • PubMed
    • Google Scholar
38. 1. Marjańska M
    2. Auerbach EJ
    3. Valabrègue R
    4. Van de Moortele PF
    5. Adriany G
    6. Garwood M

    (2012) Localized 1H NMR spectroscopy in different regions of human brain in vivo at 7 T: T2 relaxation times and concentrations of cerebral metabolites

    NMR in Biomedicine 25:332–339.

    https://doi.org/10.1002/nbm.1754

    • PubMed
    • Google Scholar
39. 1. Melnick MD
    2. Harrison BR
    3. Park S
    4. Bennetto L
    5. Tadin D

    (2013) A strong interactive link between sensory discriminations and intelligence

    Current Biology 23:1013–1017.

    https://doi.org/10.1016/j.cub.2013.04.053

    • PubMed
    • Google Scholar
40. 1. Ozeki H
    2. Finn IM
    3. Schaffer ES
    4. Miller KD
    5. Ferster D

    (2009) Inhibitory stabilization of the cortical network underlies visual surround suppression

    Neuron 62:578–592.

    https://doi.org/10.1016/j.neuron.2009.03.028

    • PubMed
    • Google Scholar
41. 1. Reynolds JH
    2. Heeger DJ

    (2009) The normalization model of attention

    Neuron 61:168–185.

    https://doi.org/10.1016/j.neuron.2009.01.002

    • PubMed
    • Google Scholar
42. 1. Sato TK
    2. Haider B
    3. Häusser M
    4. Carandini M

    (2016) An excitatory basis for divisive normalization in visual cortex

    Nature Neuroscience 19:568–570.

    https://doi.org/10.1038/nn.4249

    • PubMed
    • Google Scholar
43. 1. Schallmo MP
    2. Kale AM
    3. Millin R
    4. Flevaris AV
    5. Brkanac Z
    6. Edden RA
    7. Bernier RA
    8. Murray SO

    (2018) Suppression and facilitation of human neural responses

    eLife 7:e30334.

    https://doi.org/10.7554/eLife.30334

    • PubMed
    • Google Scholar
44. 1. Siegel M
    2. Buschman TJ
    3. Miller EK

    (2015) Cortical information flow during flexible sensorimotor decisions

    Science 348:1352–1355.

    https://doi.org/10.1126/science.aab0551

    • PubMed
    • Google Scholar
45. 1. Song M
    2. Zhou Y
    3. Li J
    4. Liu Y
    5. Tian L
    6. Yu C
    7. Jiang T

    (2008) Brain spontaneous functional connectivity and intelligence

    NeuroImage 41:1168–1176.

    https://doi.org/10.1016/j.neuroimage.2008.02.036

    • PubMed
    • Google Scholar
46. 1. Song XM
    2. Hu XW
    3. Li Z
    4. Gao Y
    5. Ju X
    6. Liu DY
    7. Wang QN
    8. Xue C
    9. Cai YC
    10. Bai R
    11. Tan ZL
    12. Northoff G

    (2021) Reduction of higher-order occipital GABA and impaired visual perception in acute major depressive disorder

    Molecular Psychiatry 26:6747–6755.

    https://doi.org/10.1038/s41380-021-01090-5

    • PubMed
    • Google Scholar
47. 1. Spearman C

    (1904) “General intelligence,” objectively determined and measured

    The American Journal of Psychology 15:201.

    https://doi.org/10.2307/1412107

    • Google Scholar
48. 1. Tadin D
    2. Lappin JS
    3. Gilroy LA
    4. Blake R

    (2003) Perceptual consequences of centre-surround antagonism in visual motion processing

    Nature 424:312–315.

    https://doi.org/10.1038/nature01800

    • PubMed
    • Google Scholar
49. 1. Tadin D
    2. Silvanto J
    3. Pascual-Leone A
    4. Battelli L

    (2011) Improved motion perception and impaired spatial suppression following disruption of cortical area MT/V5

    The Journal of Neuroscience 31:1279–1283.

    https://doi.org/10.1523/JNEUROSCI.4121-10.2011

    • PubMed
    • Google Scholar
50. 1. Tadin D

    (2015) Suppressive mechanisms in visual motion processing: From perception to intelligence

    Vision Research 115:58–70.

    https://doi.org/10.1016/j.visres.2015.08.005

    • PubMed
    • Google Scholar
51. 1. Tkác I
    2. Starcuk Z
    3. Choi IY
    4. Gruetter R

    (1999) In vivo1H NMR spectroscopy of rat brain at 1 ms echo time

    Magnetic Resonance in Medicine 41:649–656.

    https://doi.org/10.1002/(sici)1522-2594(199904)41:4<649::aid-mrm2>3.0.co;2-g

    • Google Scholar
52. 1. Treue S
    2. Maunsell JH

    (1996) Attentional modulation of visual motion processing in cortical areas MT and MST

    Nature 382:539–541.

    https://doi.org/10.1038/382539a0

    • PubMed
    • Google Scholar
53. 1. Wang XJ

    (2002) Probabilistic decision making by slow reverberation in cortical circuits

    Neuron 36:955–968.

    https://doi.org/10.1016/s0896-6273(02)01092-9

    • PubMed
    • Google Scholar
54. Book

    1. Wechsler D

    (2008)

    Wechsler Memory Scale–Fourth Edition (WMS-IV) Technical and Interpretive Manual

    London: Pearson.

    • Google Scholar
55. 1. Wilming N
    2. Murphy PR
    3. Meyniel F
    4. Donner TH

    (2020) Large-scale dynamics of perceptual decision information across human cortex

    Nature Communications 11:5109.

    https://doi.org/10.1038/s41467-020-18826-6

    • PubMed
    • Google Scholar
56. 1. Wimmer K
    2. Compte A
    3. Roxin A
    4. Peixoto D
    5. Renart A
    6. de la Rocha J

    (2015) Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

    Nature Communications 6:6177.

    https://doi.org/10.1038/ncomms7177

    • PubMed
    • Google Scholar
57. 1. Yan CG
    2. Wang XD
    3. Zuo XN
    4. Zang YF

    (2016) DPABI: Data processing & analysis for (resting-state) brain imaging

    Neuroinformatics 14:339–351.

    https://doi.org/10.1007/s12021-016-9299-4

    • PubMed
    • Google Scholar
58. 1. Zaksas D
    2. Pasternak T

    (2006) Directional signals in the prefrontal cortex and in area MT during a working memory for visual motion task

    The Journal of Neuroscience 26:11726–11742.

    https://doi.org/10.1523/JNEUROSCI.3420-06.2006

    • PubMed
    • Google Scholar
59. 1. Zhang Y
    2. Brady M
    3. Smith S

    (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

    IEEE Transactions on Medical Imaging 20:45–57.

    https://doi.org/10.1109/42.906424

    • PubMed
    • Google Scholar
60. 1. Zhang S
    2. Xu M
    3. Kamigaki T
    4. Hoang Do JP
    5. Chang WC
    6. Jenvay S
    7. Miyamichi K
    8. Luo L
    9. Dan Y

    (2014) Long-range and local circuits for top-down modulation of visual cortex processing

    Science 345:660–665.

    https://doi.org/10.1126/science.1254126

    • Google Scholar

Author details

1. Yuan Gao

   Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Yong-Chun Cai

   Competing interests

   No competing interests declared

2. Yong-Chun Cai

   Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China

   Contribution

   Software, Methodology, Writing – original draft

   Contributed equally with

   Yuan Gao

   Competing interests

   No competing interests declared

3. Dong-Yu Liu

   1. Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China
   2. Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

4. Juan Yu

   1. Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China
   2. Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

5. Jue Wang

   Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Ming Li

   College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

7. Bin Xu

   Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China

   Contribution

   Data curation

   Competing interests

   No competing interests declared

8. Tengfei Wang

   Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1585-4143

9. Gang Chen

   University of Ottawa Institute of Mental Health Research, University of Ottawa, Ottawa, Canada

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Georg Northoff

    Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Hangzhou, China

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5236-0951

11. Ruiliang Bai

    MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou, China

    Contribution

    Software, Formal analysis, Methodology, Writing – review and editing

    For correspondence

    ruiliangbai@zju.edu.cn

    Competing interests

    No competing interests declared

12. Xue Mei Song

    1. Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China
    2. Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    songxuemei@zju.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3624-4245

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