Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study

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Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study

https://doi.org/10.21203/rs.3.rs-2725867/v1

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Changes in dopamine and fronto-striato-thalamic (FST) circuit functional connectivity are prominent in schizophrenia. Dopamine is thought to underlie connectivity changes, but experimental evidence for this hypothesis is lacking. Previous studies examined the association in some of the connections using positron emission tomography (PET) and functional MRI (fMRI); however, PET has disadvantages in scanning patients, such as invasiveness. Excessive dopamine induces neuromelanin (NM) accumulation, and NM-MRI is suggested as a noninvasive proxy measure of dopamine function. We aimed to investigate the association between NM and FST circuit connectivity at the network level in patients with schizophrenia. We analysed substantia nigra NM-MRI and resting-state fMRI data from 29 schizophrenia patients and 63 age- and sex-matched healthy controls (HCs). We identified the FST subnetwork with abnormal connectivity found in schizophrenia patients compared to that of HCs and investigated the relationship between constituting connectivity and NM. We found higher levels of NM (t = -2.12, p = 0.037) and a hypoconnected FST subnetwork (FWER-corrected p = 0.014) in schizophrenia patients than in HCs. In the hypoconnected subnetwork of schizophrenia patients, lower left supplementary motor area-left caudate connectivity was associated with higher NM levels (β = -0.38, p = 0.042). We demonstrated the association between NM and FST circuit connectivity. Considering that the NM-MRI signal reflects dopamine activity, our results suggest that dopamine underlies changes in FST circuit connectivity, which supports the dopamine hypothesis. In addition, this study reveals implications for the future use of NM-MRI in investigations of the dopamine system.

Biological sciences/Neuroscience

Health sciences/Diseases/Psychiatric disorders/Schizophrenia

Schizophrenia is closely related to dopamine impairment. This relationship became known through the discovery of antipsychotic drugs targeting dopamine receptors, and all licensed antipsychotic drugs used in clinical practice today affect the dopamine system^1,2. In postmortem studies, the concentrations of dopamine were increased in schizophrenia patients^3,4. Recent positron emission tomography (PET) and single photon emission computed tomography (SPECT) studies reported robust increases in in vivo striatal dopamine synthesis and release^5,6. According to the dopamine hypothesis, the most influential hypothesis regarding schizophrenia, schizophrenia occurs when increased striatal dopamine caused by various risk factors induces functional impairment⁷.

Dopamine changes in the midbrain are presumed to cause dysfunctions through connectivity changes in the fronto-striato-thalamic (FST) circuit^8,9. In the FST circuit, which links the frontal cortex, basal ganglia, and thalamus, dopaminergic signalling from the midbrain affects the cortex via basal ganglia regions, including the striatum, substantia nigra (SN), and globus pallidus^10,11. Functional connectivity alterations in the FST circuit have been consistently reported in schizophrenia¹². Changes in striatal connectivity are one of the most prominent findings in schizophrenia^13,14. Previous studies speculated the involvement of dopamine in the underlying pathophysiology of dysconnectivity given the characteristics of the structures¹². However, functional MRI (fMRI)-only studies cannot show a direct association with molecules.

Previous studies have attempted to reveal the association between dopamine function and FST circuit connectivity using both PET and fMRI. In these multimodal studies, there were associations between dopamine synthesis capacity and cortico-thalamic and fronto-striatal connectivity and between dopamine receptor density and striatal connectivity in schizophrenia patients^14–16. However, prior investigations examined the association only for limited connections. In particular, basal ganglia regions with a small volume were excluded due to inaccurate delineation¹⁷. Therefore, further studies are needed to examine the association at the network level, including the small basal ganglia regions. In addition, PET imaging has several disadvantages, such as radioactive substance use, long acquisition time, and limited accessibility, that make it difficult to scan patients. Hence, alternative molecular imaging methods can contribute to further investigations of patients.

Neuromelanin-MRI (NM-MRI) is a noninvasive proxy measure of dopamine function^18,19. Neuromelanin (NM) is synthesized by the oxidation of cytosolic dopamine and thus accumulates in the midbrain²⁰. In nonneurodegenerative conditions, NM is produced as an alternative to excess dopamine and plays a neuroprotective role^21,22. Preclinical studies have shown that increased dopamine results in NM accumulation in the SN²³. NM-MRI captures paramagnetic NM-iron complexes, the form in which NM is present in cells, and the signal is proportional to the concentration of NM¹⁸. Furthermore, the NM-MRI signal is correlated with PET dopamine measures, including striatal dopamine release^24,25. Taken together, NM-MRI signal reflects dopamine activity in the nigrostriatal pathway¹⁸.

In this study, we aimed to investigate the association between FST circuit functional connectivity and NM in schizophrenia patients using fMRI and NM-MRI. We examined for the first time the association between dopamine system measures and individual connectivity in the FST circuit at the network level in schizophrenia. Here, we used NM-MRI considering its advantages over traditional molecular imaging methods, such as its noninvasiveness. Given that the NM-MRI signal reflects dopamine function, the association can contribute to the demonstration of the dopamine hypothesis. For this aim, we identified dysconnected subnetworks in the FST circuit and examined the relationship between its constituting connectivity and the NM-MRI signal. We hypothesized that schizophrenia patients would show changes in NM and FST circuit connectivity and that there would be a significant association between them.

2.1. Participants

We acquired data from 30 schizophrenia patients and 64 age- and sex-matched healthy controls. Schizophrenia patients were recruited from the inpatient and outpatient clinics of the Department of Neuropsychiatry and Seoul Youth Clinic (www.youthclinic.org), a centre for the prospective and longitudinal investigation of people at high risk for schizophrenia, at Seoul National University Hospital (SNUH). In this study, schizophrenia patients were subjects who were diagnosed with schizophrenia according to the Structured Clinical Interview for DSM-Ⅳ Axis I Disorders (SCID-I)²⁶. Symptom severity was assessed using the Positive and Negative Symptom Scale (PANSS)²⁷. The Global Assessment of Functioning (GAF) scale was used to evaluate the overall functioning of schizophrenia patients. The Hamilton Rating Scale for Depression (HAMD)²⁸ and the Hamilton Rating Scale for Anxiety (HAMA)²⁹ were used to evaluate the severity of participants’ depression and anxiety. Healthy controls were recruited via internet advertisements. To screen for the presence of psychiatric disorders or symptoms, healthy controls were evaluated using the Structured Clinical Interview for DSM-IV-Non-Patient Version (SCID-NP). Healthy controls with a past or current axis-I diagnosis and any first- to third-degree biological relatives who had a lifetime history of psychotic disorders were excluded from this study. The subjects’ intelligence quotient (IQ) was measured with the Korean version of the Wechsler Adult Intelligence Scale (K-WAIS)³⁰.

The exclusion criteria for all participants included evidence of neurological disease or clinically significant head injury, substance abuse or dependence (except for nicotine), or intellectual disability (IQ < 70). Written informed consent was obtained from all participants and the parents of subjects under the age of 18 after a full explanation of the procedures was provided (IRB No. H-1110-009-380, 1905/001–010). This study was conducted according to the Declaration of Helsinki (2013) and approved by the Institutional Review Board of SNUH (IRB No. H-2210-123-1371).

2.2. Image acquisition and preprocessing

We acquired anatomical, resting-state functional, and NM images using a 3T MRI scanner (Siemens Magnetom Trio). T1-weighted anatomical images were scanned using a magnetization-prepared rapid gradient echo sequence (voxel dimension 0.8 mm isotropic, repetition time/echo time 2400/2.19 ms, flip angle 8°, and slices 224). Resting-state functional images were acquired for 6 min and 44 s using a gradient echo planer imaging pulse sequence (voxel dimension 2.3 mm isotropic, repetition time/echo time 1500/30 ms, flip angle 85°, and slices 64). Field map images consisting of echo planar imaging data with opposite phase encoding directions (right-to-left and left-to-right) were also collected for susceptibility distortion correction for functional images (voxel dimension 2.3 mm isotropic, repetition time/echo time 4200/30 ms, flip angle 85°, and slices 64). During the fMRI session, participants were instructed to remain relaxed, keep their eyes closed, and not fall asleep. We scanned NM-sensitive images using a 3D gradient echo sequence (voxel dimension 0.8 × 0.8 × 1.5 mm³, repetition time/echo time 80/4.94 ms, flip angle 25°, and slices 32). Before each excitation, a magnetization transfer pulse was applied to enhance the NM contrast³¹.

We preprocessed the brain imaging data using ENIGMA HALFpipe version 1.2.1 (https://github.com/HALFpipe)³², a semiautomated pipeline that relies on fMRIPrep³³. The pipeline included susceptibility distortion estimation, spatial normalization, grand mean scaling, independent component analysis (ICA)-based denoising, and temporal filtering. For spatial normalization, the MNI152NLin2009cAsym template (2 mm) was defined as the standard space. Grand mean scaling was applied with a mean value of 10,000, and denoising was performed with the ICA-based automatic removal of motion artefacts (ICA-AROMA) method. Temporal filtering was conducted using a frequency-based temporal filter (0.01–0.1 Hz). Quality assessment was performed according to the ENIGMA HALFpipe quality control manual, and two subjects were excluded from further analyses due to high motion (mean framewise displacement (FD) > 0.5 mm, maximum FD > 3 mm).

2.3. ROI definition and rs-fMRI analysis

We created an FST atlas spanning the bilateral frontal cortex, striatum, thalamus, globus pallidus, and SN using 3 different atlases (Fig. 1). For the frontal cortex, masks constituting the sensorimotor cortex (precentral gyrus, supplementary motor area (SMA), and postcentral gyrus), dorsolateral prefrontal cortex (dorsolateral superior frontal gyrus and middle frontal gyrus), and ventromedial prefrontal cortex (medial superior frontal gyrus and medial orbital superior frontal gyrus) were derived from the automatic anatomical labelling atlas 2³⁴. The thalamus, caudate, putamen, and accumbens masks were obtained from the Harvard-Oxford subcortical atlas. For the globus pallidus, external and internal globus pallidus masks from the Pauli et al. (2018) atlas were combined. SN pars compacta and SN pars reticulata masks were also derived from the Pauli et al. (2018) atlas. The ROI masks were combined to create an FST atlas consisting of 28 regions.

Atlas-based connectivity analysis was performed in HALFpipe version 1.2.1, and time series were extracted from the 28 nodes of the FST atlas. Functional connectivity between pairs of nodes was estimated by calculating the Pearson correlation coefficient. As a result, a pairwise connectivity matrix between atlas regions was calculated for each participant.

2.4. Functional network analysis

We used the network-based statistics (NBS) approach to test whether there were dysconnected subnetworks in the FST circuit in schizophrenia patients. We performed a t test in the NBS toolbox version 1.2³⁵ using the precalculated connectivity matrix to identify suprathreshold connections (t > 2.5). Based on the empirical null distribution of the largest network component size (5000 permutations), we found subnetworks exhibiting greater network size than a threshold of FWER-corrected p < 0.05. We used the component intensity measure to the network size because of our focal interest in the FST circuit³⁶.

2.5. NM-MRI analysis

NM images were analysed in FSLeyes version 1.0.13 using the manual segmentation method following Wang et al. (2018)³⁷. First, three contiguous slices with the greatest SN area in the middle slice were selected. Then, 3 mm diameter circular masks were drawn at the lateral and medial parts of the SN. As a reference region, 4.5 mm diameter circular masks were drawn at the crus cerebri (CC). As a result, a total of 12 (4*3) and 6 (2*3) masks were drawn for the SN and CC, respectively. Next, we calculated the contrast ratio (CR) using the average signal intensity of SN (SI_SN) and CC (SI_CC) with the following equation: CR = (SI_SN – SI_CC)/SI_CC. We used the upper quartile voxel values when calculating the SI_SN. We set this threshold to avoid including regions other than the SN that make the average value inaccurate considering the small size of the SN. Manual segmentation was performed twice blindly by two independent researchers (S.C. and J.L.), and reliability between trials was calculated. Values calculated by the first rater were used in subsequent analyses.

2.6. Statistical analysis

The statistical analyses were conducted using SPSS version 25. Demographic differences were tested using a t test or chi-square test. We examined NM differences between groups using a t test and NM reliability between raters using a reliability test. Based on the functional network analysis results, we performed multiple linear regression analysis to find specific subnetwork connections accounting for the NM level. We used a stepwise regression method for feature selection.

3.1. Demographics

We used data from 29 schizophrenia patients (average age 25.55 years, male 48.28%) and 63 healthy controls (24.13 years, 47.62%) in the analysis (Table 1). There were no significant differences in age, sex, or handedness between groups, although the IQ of healthy controls was higher than that of patients (t = 6.01, p < 0.001).

Table 1

Participants’ demographics and clinical characteristics.
	HCs (n = 63)	SCZ (n = 29)	t or X²	p
Age (years)^a	24.13 (3.70)	25.55 (4.43)	-1.61	0.110
Sex (male/female)	30/33	14/15	0.00	0.953
Handedness (right/left)	56/7	24/4	0.18	0.668
Education (years) ^a		13.78 (1.91)
IQ ^a	127.55 (7.77)	102.64 (17.58)	6.91	< 0.001*
DOI (months) ^a		73.52 (46.70)
PANSS ^a
Positive symptoms		11.48 (3.09)
Negative symptoms		13.33 (4.59)
General symptoms		24.04 (4.70)
GAF ^a		56.37 (13.87)
HAMA ^a		2.96 (2.41)
HAMD ^a		3.96 (2.95)
HCs: healthy controls; SCZ: schizophrenia patients; IQ: intelligence quotient; DOI: duration of illness; PANSS: Positive and Negative Syndrome Scale; GAF: Global Assessment of Functioning; HAMA: Hamilton Rating Scale for Anxiety; HAMD: Hamilton Rating Scale for Depression. ^a The values are presented as the mean (standard deviation). (*) indicates significance at p < 0.05.

3.2. NM alterations

The interrater reliability test showed excellent reliability between trials (ICC = 0.933, p < 0.001). In subsequent analyses, we used the values calculated by the first rater. Next, we examined group differences in NM CR (Fig. 2). There was a statistically significant increase in NM CR in schizophrenia patients compared to that in healthy controls (t = -2.12, p = 0.037, Cohen’s d = 0.43).

3.3. Aberrant FST subnetworks

We found a hypoconnected FST subnetwork in patients compared to healthy controls (FWER-corrected p = 0.014), which consisted of 18 nodes and 26 edges (Table 2, Fig. 3). The hypoconnected subnetwork included cortico-cortical, striato-cortical, and nigro-striatal connections. In contrast, there was no significant hyperconnected subnetwork in schizophrenia patients.

Table 2

Hypoconnected FST subnetwork in patients with schizophrenia.
Individual connections		Connectivity strength		t
		HCs	SCZ
L PreCG	L MFG	0.69	0.56	3.19
L SFG	L SMA	0.67	0.54	2.78
R SFG	L SMA	0.63	0.49	2.65
	R SMA	0.65	0.50	3.18
L MFG	L SMA	0.66	0.50	3.64
	R SMA	0.57	0.42	3.11
	L PoCG	0.53	0.41	2.54
R MFG	L SMA	0.59	0.48	2.56
	R SMA	0.64	0.53	2.63
L SMA	L SFGmedial	0.52	0.38	2.56
	L Caudate	0.59	0.46	2.73
	R Caudate	0.55	0.43	2.60
	L Putamen	0.60	0.44	3.32
	R Putamen	0.57	0.40	3.28
R SMA	L Caudate	0.52	0.39	2.65
	R Caudate	0.52	0.39	2.61
	L Putamen	0.59	0.42	3.33
	R Putamen	0.59	0.42	3.39
L SFGmedial	L PFCventmed	0.79	0.66	4.07
	R PFCventmed	0.73	0.62	3.15
	L PoCG	0.39	0.21	3.01
	R PoCG	0.36	0.20	2.54
R SFGmedial	R PFCventmed	0.74	0.64	2.77
L PFCventmed	L PoCG	0.39	0.23	2.62
	R PoCG	0.35	0.19	2.64
L Caudate	R SNc	0.27	0.15	2.62
HCs: healthy controls; SCZ: schizophrenia patients; PreCG: precentral gyrus; SFG: superior frontal gyrus, dorsal lateral; MFG: middle frontal gyrus; SMA: supplementary motor area; SFGmedial: superior frontal gyrus, medial; PFCventmed: superior frontal gyrus, medial orbital; PoCG: postcentral gyrus; SNc: substantia nigra pars compacta.

3.4. Association between NM and FST connectivity

We predicted the NM level from the subnetwork connectivity using multiple linear regression analysis (Fig. 3). The left SMA-left caudate connection was selected as a significant predictor (F = 4.54, p = 0.042), while the other connections were excluded from the model. The model explained 11.20% of the variance (adjusted R² = 0.11), and there was a negative effect between the predictor and dependent variable (β = -0.38).

In this study, we confirmed changes in NM and functional connectivity of the FST circuit and found a significant association between these changes in schizophrenia patients. In the circuit, functional connectivity impairment was primarily in the cortex, striatum, and substantia nigra, among which left SMA-left caudate connectivity was associated with NM. This association indicates that the two pathological changes are interrelated in schizophrenia pathophysiology. Moreover, given that NM-MRI provides a proxy measure for dopamine activity, these results suggest that dopamine underlies the connectivity changes in the FST circuit.

We observed a higher NM-MRI signal in schizophrenia patients than in healthy controls; a higher signal indicates increased NM accumulation in the SN¹⁸. This result is consistent with the findings of previous NM-MRI and postmortem studies that reported increased NM levels in schizophrenia patients^38–41. In dopamine metabolism, excessive dopamine induces an increase in NM synthesis²². Moreover, previous multimodal studies reported a positive correlation between NM-MRI signals and PET dopamine measures, such as dopamine release and dopamine receptor availability^18,24,25. In this context, the higher NM-MRI signal in our study may suggest an increase in dopaminergic function. As mentioned above, increased dopamine is one of the most prominent abnormalities in schizophrenia⁸.

Our study found a hypoconnected FST circuit subnetwork in schizophrenia patients, which includes the cortex, striatum, and substantia nigra. Consistent with this, previous studies have shown hypoconnectivity in cortico-cortical, striato-cortical, and nigro-striatal connections in schizophrenia patients^12,42,43. Preclinical models of schizophrenia pathophysiology explain this hypoconnectivity: spontaneous phasic dopamine release in schizophrenia patients leads to increased noise in dopamine signalling in the striatum, which in turn reduces functional connectivity in the FST circuit^8,44. In contrast, some studies have reported increased connectivity, such as in the thalamic subnuclei, in schizophrenia patients ^13,14. These connections may not have been found in our study due to network-level statistics or the inclusion of the total thalamus rather than the subnuclei.

The dysconnected FST subnetwork included many striatal connections. The striatum is where dopaminergic projections from the midbrain arrive, and connectivity changes in this region are among the most consistent findings in schizophrenia⁸. Specifically, striatal connectivity alterations were prominent in the caudate¹⁴. In addition, cortical connectivity changes based on the aberrant striatum regions were primarily in the sensorimotor cortex¹². These findings are in line with our results showing multiple caudate-sensorimotor cortex dysconnectivity. In our study, we also found connectivity changes in the nigro-striatal pathway. Previous studies suggested that reduced nigro-striatal connectivity may reflect reduced tonic activity and increased phasic activity in the pathway, given that rs-fMRI measures low-frequency fluctuations that are central to tonic activity¹².

The FST circuit connectivity significantly predicted NM levels in schizophrenia patients. More specifically, lower left SMA-left caudate connectivity was associated with higher NM levels. Considering that NM levels reflect dopamine function, this result is consistent with previous findings showing an association between dopamine measures and striatal connectivity in schizophrenia patients^14–16. In addition, our results of the association primarily identified in the SMA-caudate connection correspond with regional characteristics. According to recent neurochemistry studies, dopaminergic aberrations were greater in the dorsal striatum than in the limbic striatum^5,45,46. The dorsal striatum encompasses the caudate region and receives cortical projections mainly from the SMA, motor area, and dorsolateral prefrontal cortex¹⁰. Taken together, this study added to other evidence suggesting that midbrain dopamine changes underlie aberrant connectivity of the FST circuit.

This study suggests the possibility of using NM-MRI for various investigations of the dopamine system. For example, treatment response studies reported a linkage between responsiveness and dopamine and noted a need for using molecular imaging for response prediction^47–49. PET is a typical molecular imaging method but has limitations in clinical settings due to its invasiveness, cost, etc. NM-MRI is safer in terms of radiation exposure and has better accessibility. In this study, we confirmed that NM-MRI results are consistent with existing molecular imaging results and examined the association between NM-MRI signals and functional connectivity for the first time. Overall, this study has implications for the future use of NM-MRI to examine dopamine function in research and clinical practice.

The main strength of this study is that it examined the association between FST circuit individual connectivity and dopamine system molecules at the network level in schizophrenia patients. Furthermore, this study showed the results of investigating the dopamine system using NM-MRI, a promising molecular imaging method. This study has several limitations. Schizophrenia patients in our study were medicated. Although there was no significant association between medication status and variables, the effect of medication should be considered when interpreting the results. Next, some studies reported different patterns of connectivity among subregions in the FST circuit, particularly in the thalamic subregions¹⁴. Future studies using higher resolution are needed to examine the subregions. Finally, our association results do not indicate a causal relationship. Further investigations, for example, using sophisticated models, are required to determine causality within the pathophysiology.

We demonstrated a negative association between NM and FST circuit connectivity in schizophrenia patients using NM-MRI and fMRI. This shows a linkage between abnormal neurochemistry and functional abnormalities in the pathophysiology of schizophrenia. Furthermore, considering that the NM-MRI signal reflects dopamine function, our results suggest that dopamine underlies connectivity changes in the FST circuit. Together, these results expand our understanding of schizophrenia pathophysiology by providing experimental evidence supporting the dopamine hypothesis. In addition, this study has implications for the future use of NM-MRI in studies on the dopamine system.

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and the KBRI basic research program through the Korea Brain Research Institute, funded by the Ministry of Science & ICT (grant nos. 2019R1C1C1002457, 2020M3E5D9079910, and 21-BR-03-01). This research was supported by Research Program 2020 funded by Seoul National University College of Medicine Research Foundation.

Conflict of interest

The authors declare no conflict of interest.

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The authors have declared there is NO conflict of interest to disclose

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Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study

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Abstract

Figures

1. Introduction

2. Methods

2.1. Participants

2.2. Image acquisition and preprocessing

2.3. ROI definition and rs-fMRI analysis

2.4. Functional network analysis

2.5. NM-MRI analysis

2.6. Statistical analysis

3. Results

3.1. Demographics

3.2. NM alterations

3.3. Aberrant FST subnetworks

3.4. Association between NM and FST connectivity

4. Discussion

Declarations

References

Additional Declarations

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