Subcortical-cortical structural connectivity alternations and their application in cognitive impairments of schizophrenia

doi:10.21203/rs.3.rs-4722455/v1

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Subcortical-cortical structural connectivity alternations and their application in cognitive impairments of schizophrenia

https://doi.org/10.21203/rs.3.rs-4722455/v1

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Background

It has been suggested that white matter involves in regulation of various cognitive activities of schizophrenia(SZ). The complexity of SZ along with the lack of specific brain abnormalities indicates widespread systemic structural foundations in schizophrenia. To detect and visualize the abnormal patterns of whole-brain white matter network and their significance in cognitive impairment of SZ through diffusion-weighted magnetic resonance imaging (DWI) and tractography.

Methods

This cross-sectional study progressively recruited 61 SZ and 63 matched healthy controls(HC) from August 2019 and December 2022 at the Shanghai Mental Health Center. Probabilistic tractography was conducted, and a full-cortex connectomes of 82 brain regions based on Desikan-Killiany atlas were created to depict the number of streamlines between brain regions. Network-based statistic (NBS) analysis was performed to test between-group differences in brain subnetwork connectivity. Graph theoretical metrics were assessed to characterize the topological organization of structural networks. Pearson correlation analysis was conducted to explore the correlation between general data, clinical features, and significant network features.

Results

The NBS analysis indicated one component with stronger connectivity (p < 0.001) containing 53 connections among HC individuals compared with patients with SZ. These connections mainly located in the subcortical regions including thalamus, hippocampus, putamen as well as accumbens, and cortical regions mainly consisting of Insula, media lorbito-frontal, and anterior cingulate (threshold edge t > 3.10, component p < 0.001, NBS corrected). Further graph theoretical analysis revealed that SZ patients exhibited impairments in global efficiency and local topological properties, indicating disruptions in the ability of transiting and integrating information in the brain network. Correlation analysis shows that these structural network abnormalities were significantly associated with cognitive dysfunction in SZ.

Conclusions：

These findings here provided a new perspective for understanding the mechanisms behind the core symptoms of schizophrenia and emphasize the important pathological role of the white matter pathway in schizophrenia.

Schizophrenia

Connectomics

Diffusion magnetic resonance imaging

Graph theory

Structural connectivity

Tractography

Question

What is the abnormal patterns of whole-brain white matter network and their significance in cognitive impairment of schizophrenia(SZ)?

Findings

In this cross-sectional study, subcortical-cortical dysconnectivity in SZ was identified, and these abnormalities were closely related to cognitive impairment of SZ.

Meaning

These findings provide a new perspective for understanding the mechanisms behind the core symptoms of SZ and emphasize the important pathological role of the white matter pathway in SZ.

Schizophrenia (SZ) is a chronic, severe and disabling illness, characterized by various clinical symptoms, including positive, negative, affective and cognitive symptoms[1], and significant impairments and deterioration in social and working functioning[2, 3]. Due to its severe and long-lasting symptoms, treatment and the understanding of the neurobiological processes of SZ has been somewhat challenging.

Abundant evidence has supported the notion that SZ is intrinsically linked to disturbances of brain connectivity between different regions[4, 5]. Till now, the focus has remained primarily on cortical community structure. Meta-analysis suggested that cortical regions, such as the prefrontal cortex, posterior cingulate cortex, and lateral prefrontal cortex, interconnect and interact with each other [6], and the connectivity disturbances between cortical regions are closely connected to the symptoms of SZ[6-9]. However, less work has been focused on contributions from major subcortical regions[10]. Subcortical regions, including striatum, thalamus, hippocampus, and amygdala, are crucial in various cognitive, emotional, reward[11, 12] and physiological functions though intra-subcortical communication as well as with cortical regions[13]. The dysconnectivity of subcortical and other brain regions may implying in the neurobiological basis of core symptoms such as cognitive impairments and negative symptoms in SZ. Thus, a complete and accurate description of brain structure and function necessarily requires consideration of the extensive cortical-subcortical architecture in SZ.

Cognitive impairment is a core feature of SZ. Individuals with SZ present extensive cognitive impairment across multiple cognitive domains, especially in processing speed and executive functioning[14, 15]. Furthermore, widespread white matter abnormalities have been identified to contribute to cognitive impairment in SZ[16, 17], as white matter contains complicate fiber architectures, which could carry and transfer neural activity information of the central nervous system [18, 19] [20]. Specifically, slower processing speed might be directly related to disrupted white matter connectivity in SZ[21, 22]. White matter microstructure might mediate the association between SZ and various cognitive domains such as language/processing speed/working memory/non-verbal memory[23]. While some putative relationships between white matter and cognitive performance in SZ have been identified,

Diffusion-weighted magnetic resonance imaging (DWI) provides a non-invasive approach to delineate the white matter tracts of the brain, as well as the anatomic pathophysiology underlying cognitive impairments in SZ. However, our current understanding of the relationship between white matter and cognitive impairments in SZ arises mainly from microstructure analysis, which do not provide more biologically accurate representations of white matter pathways, such as the total amount of structural connectivity between distant pairs of brain regions. Contraverly, probabilistic algorithms are able to model multiple fiber orientations per voxel [24], enabling the reconstruction and visualization of white matter pathways in the brain , and ideally suited for brain network-level analysis[25]. Till now, several dMRI tractography studies of SZhave been reported, but most studies have been performed with relatively small sample sizes, or focused on particular regions of interest[26] [27] [28] [29] [30] [31].Though the disruption in brain structure of SZ have been identified, the correlates of disruption of brain anatomical networks and cognitive performance in SZ are poorly understood, especially, valuable insights into distinct cortical projections to subcortical structures in SZ, from a respective of whole-brain, are limited.

Therefore, the aim of the study was to examined the patterns of structural dysconnectivity in SZ, particularly between cortical and subcortical regions using a straightforward dMRI probabilistic approach and graph theory. Subsequently, we identified if psychiatric symptomatology is linked to structural brain network topography. We hypothesized that individuals with SZ exhibited specific abnormal structural connectivity between cortical-subcortical regions, which was closely associated with patients’ psychopathologies and symptom severity.

Participants

Participants were 61 SZ who met the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria, and 63 matched healthy controls(HC) recruited from the same geographical areas through advertisement. Patients were progressively recruited at the Shanghai Mental Health Center outpatient department between August 2019 and December 2022. For each patient with SZ, the severity of symptoms were assessed by two experienced psychiatrists using the Positive and Negative Syndrome Scale (PANSS) [32]. We also assessed the cognitive performances by the Repeatable Battery for RBANS [33], Temporal Experience of pleasure,TEPS) and Snaith‑Hamilton Pleasure Scale(SHAPS) were used for assessing pleasure of each participant[34, 35]. Individuals with other psychiatric disorders, a history of substance use, medical comorbidities including hypertension, hyperglycemia and hyperlipidemia, pregnancy, breastfeeding, electroconvulsive therapy within the last six months were excluded.

The study protocol was approved by the Institutional Ethics Review Board of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine. All investigations were performed in strict accordance with the Declaration of Helsinki. Before the recruitment, all participants provided written informed consent.

MRI acquisition

T1-weighted imaging and DWI data were acquired on a 3.0-Tesla and 64-channel head coil Siemens Magneton Prisma system (Siemens Healthcare, Erlangen, Germany). T1 images were acquired using a three-dimensional magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence [voxel size = 0.9 × 0.9 × 0.9 mm³, repetition time (TR) = 2000 ms, echo time (TE) = 2.32 ms, flip angle = 8°, and number of slices = 208, slice thickness = 0.9mm]. DTI data were acquired with a single-shot echo-planar imaging sequence (repetition time = 3500 ms, echo time = 86 ms, flip angle = 90°, number of slice = 92, slice thickness = 1.5 mm, voxel size = 1.5 × 1.5 × 1.5 mm³, b values = 0 and 1000 s/mm²).

Image preprocessing

Structural images were preprocessed to extract brain tissue from images of the whole head using the automated FreeSurfer pipeline (version 6.0.0).[36] The image was segmented into 82 distinct regions including 68 cortices and 14 subcortical regions based on Desikan-Killiany atlas. Individual structural images were then registered to the diffusion space using bbregister. T1-weighted diffusion data were pre-processed by open-source software library FMRIB's Software Library (FSL) (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide). Briefly, brain masks were generated from b0 image to detect nonbrain tissue using the Brain Extraction Tool (BET). Eddy current distortions and subject motions were then corrected in the individual DWI data.

Structural connectomes construction

To create structural connectomes, a node was assigned to each of the 82 regions from the Desikan-Killiany anatomical parellation. Each brain region was selected as a seed region, and its connectivity probability to the other 81 regions was calculated. By selecting each brain region as a seed region, and its streamline number to the remaining 81 regions was calculated by FSL probabilistic tractography with default options (number of samples = 5000, number of steps per sample = 2000, step length = 0.5 mm, and curvature threshold = 0.2) of the probtrackx2 GPU. Thus, an 82 × 82 probability weighted matrix was created to represent the constructed network.

Data analysis

Network-based statistics

We conducted a network-based statistic (NBS) analysis between SZ and HC groups to examine subnetwork connectivity within the structural connectome. The analysis was completed within the framework of component p-value and edge p-value being below 0.001 and 0.05 respectively, age and gender being the covariates for two-sample t-tests. Subsequently, specific network edges that displayed significant differences between SZ and HC were identified. To examine the significance of each subnetwork, permutation tests were employed. We randomly assigned the subjects into the HC and SZ groups 5000 times, generating a null distribution of the network size based on these randomized groupings.

Graph Theory Analysis Graph theoretical metrics were assessed using GRETNA (version 2.0.0) to characterize the topological organization of structural networks. We computed the strength and efficiency profiles for each network including shortest path length (L_p), global efficiency (E_glob), local efficiency (E_loc), as well as nodal properties including node efficiency (Ne), nodal local efficiency (NLe), degree and betweenness centrality (Dc and Bc), nodal clust coefficiency (NCp) in the binary networks with a set of network sparsity from 0.05 to 0.50 with an interval of 0.05 and 100 random networks. False discovery rate (FDR) correction was used to determine the statistical significance of group differences.

Between-Group Statistical Comparison and Correlation Analysis

Statistical Analysis

All statistical tests were two-tailed. Statistical significance was set at P < 0.05. Demographic data between HC and SZ were computed using independent t-test or chi-square tests for categorical variables.

Strength is calculated as the total number of connections (streamlines) of each node. We compared the mean streamlines across edges in each subnetwork component by t-test. We averaged streamline connectivity strength between subcortical regions, such as the thalamus, hippocampus and amygdala, to the cortex. Additionally, considering the substantial projections from the insula to other cortical areas, we also calculated the average connection strength of insula projections. Then group differences were compared. Furthermore, we conducted exploratory analysis. After controlling for gender and age, Pearson correlation analysis was used to explore the correlation between clinical disease characteristics, cognitive function status, and significant network features in patients with SZ.

Demographic and clinical characteristics

Table 1 summarized the demographic and clinical characteristics of the analysis sample. SZ and HC groups were well matched with each other in age, sex, and education (t = -0.556, p = 0.579; χ²= 3.217, p = 0.073; t = 1.291, p = 0.199). SZ patients showed significant anhedonia (t = 3.120, p =0.002 for TEPS score and t = -3.072, p =0.003 for SHARPs) and lower QOL (t = 2.725, p =0.007). On cognitive profile, patients with SZ showed significantly lower scores on RBANS test (All p < 0.05).

Network based Statistic

The NBS analysis indicated one component with stronger connectivity (p < 0.001) containing 53 connections among HC individuals compared with patients with SZ (Table 2). These connections mainly located in the subcortical regions including thalamus, hippocampus, putamen as well as accumbens-area, and cortical regions mainly consisting of Insula, media lorbito-frontal, and anterior cingulate (threshold edge t > 3.10, component p < 0.001, NBS corrected). Conversely, no significantly component was identified among individuals with SZ relative to HC individuals by NBS analysis (p = 0.396, NBS corrected).

This study also compared the differences in the average strength of differential subnetwork, insula and other cortical brain regions, as well as the connections among subcortical brain regions through independent sample t-tests. The results showed that the average connection strength in the significantly different sub networks of SZ was significantly lower than that of the normal control group (p < 0.001) (Figure 1A). The average connectivity strength between the insula and other cortical brain regions in the significantly different subnetworks of SZ patients (Figure 2B), and the average connectivity strength between the subcortical brain regions of the hippocampus (Figure 1C), thalamus (Figure 1D), and amygdala (Figure 1E) and the cortex were significantly lower than those of the normal control group (p ≤ 0.001)

Graph-theoretical properties of structural network

At the whole level, there were no significant differences in global and nodal efficiency between groups. In the comparison of significant different subnetworks, there were widespread differences in graph theory attributes between SZ and HC, involving multiple brain regions in the frontal, temporal, parietal, and occipital lobes (Figure 2). Individuals with SZ exhibited enhanced global efficiency in the Eloc(t = -3.66，p < 0.001). and decreased local efficiency in Bc(t = 3.15, p < 0.001), Dc(t = 4.33, p < 0.001), Ne(t = 2.62, p = 0.013), Ncp(t = 7.61,p < 0.001) as well as NLe(t = 5.14, p < 0.001) compared to HC individuals.

Association between significant different subnetwork with clinical parameters

The results showed that, in the SZ group, the average connectivity strength of significantly different subnetwork was negatively correlated with the patient's disease course (r = -0.317, p = 0.013) and age (r = -0.523, p = 0.000), and positively correlated with the subitems such as line localization of visual breadth (r = 0.418, p = 0.001) and picture naming of speech function (r = 0.276, p = 0.036). However, we did not find any correlation between the average connectivity strength of significant subnetwork and patient PANSS total score, negative symptom score, positive symptom score, and general psychopathological symptom score.

Additionally, the connection strength from the thalamus to the cortex (r = 0.270, p = 0.040), as well as significant edge strengths between the right thalamus and the right postcentral gyrus (r = 0.332, p = 0.011), the right hippocampus and the left superior temporal gyrus (r = 0.262, p = 0.047), the right inferior parietal lobe and the right transverse temporal gyrus (r = 0.303, p = 0.021), the right postcentral gyrus and the right precentral gyrus (r = 0.294, p = 0.025), the right postcentral gyrus to the right superior temporal lobe (r = 0.361, p = 0.005), and the right precentral gyrus to the right insula (r = 0.327, p = 0.012) showed significant positive correlations with the RBANS total score. The results of correlation analysis was presented in Figure 3.

In current study, we explored the difference in probabilistic brain network between SZ and HC, and their correlation with morphological changes in SZ. Based on whole brain analysis, the results showed that SZ exhibited extensive dysconnectivity, involving key areas of the brain, particularly those involved in cognitive function and emotional regulation.This weakening of connectivity not only involved cortical connectivity, but also significantly affected the function of subcortical brain regions, proving the important role of subcortical structures in SZ. Our research also suggested that abnormalities in these structural networks are significantly associated with cognitive impairment in patients with SZ. For example, the abnormal white matter pathway revealed by DWI analysis was closely related to cognitive dimensions such as processing speed, working memory, and executive function in patients. These findings provide a new perspective for understanding the mechanisms behind the core symptoms of SZ and emphasize the important pathological role of the white matter pathway in SZ.

Previous studies have suggested that white matter contains complex fibrous structures that can carry and transmit neural activity information from the central nervous system [18-20], and widespread white matter abnormalities may be the cause of cognitive impairment in SZ [16, 17]. The white matter structure may mediate the connection between SZ and various cognitive domains, such as language, processing speed, working memory, and nonverbal memory [23]. In current study, the subnetwork that showed alternations in SZ overlaps with cognitive function and emotional regulation circuits, and it included connections between thalamus, hippocampus, amygdala, caudate, nucleus accumbens and insula, medial orbitofrontal cortex, anterior cingulate gyrus et al. Reduced connectivity within this connectome could affect cognitive function in SZ, and may be reflected in the correlation between subnetwork connectivity and RBANS, as well as its subitems. These finding was in keeping with previous studies, finding the connectivity between fiber bundles related to the basal ganglia or corticostriatal structures between groups were correlated with different cognitive domains such as language, executive function, or processing speed[37]. Moreover, study based on deterministic fiber tracking algorithms also found significant abnormalities in crucial brain regions for cognitive and emotional integration, such as the inferior frontal gyrus and bilateral insula[29]. Altogether with these previous studies, the current study further emphasized the importance of white pathways in the core symptoms of SZ, and may served as potential neural markers for clinical changes in SZ.

Interestingly, our study found that brain regions associated with cognitive impairment in patients with SZ were all located in the right brain. Specifically, probabilistic fiber bundle connections between right subcortical and right cortical regions, as well as connections between right cortical regions, have a significant correlation with the total RBANS score. A previous study has indicated alternations in connections between the right inferior frontal gyrus and temporal parietal structure, and their high correlation with poorer semantic fluency in right-handed SZ[38]. Moreover, as compared to HC, individuals with SZ also exhibited asymmetric white matter abnormalities in the frontal lobe fibers, and abnormal fractional anisotropy (FA) values between the right frontal lobe fiber bundle, the right thalamus and both the right anterior cingulate cortex and the right ventrolateral prefrontal cortex have been reported to exhibit significant ties to poorer speech memory performance[26], lower overall functional scores and lower social and role functional scores[39]. To examine the underlying reasons, abnormal axon pruning affecting cognitive development may be important. Insufficient axonal pruning may reduce information transmission efficiency by generating redundant brain networks[40], thereby affecting the cognitive performance of patients. Further, the above results were illustrated by the graph theory analysis. The results showed that differences of graph metrics between groups involved multiple brain regions. Significant changes in local efficacy and node characteristics indicate a reduced speed of information transmission between brain regions, leading to cognitive impairment in SZ. The findings here may give some hint to understand the anatomical substrates and mechanism of cognitive impairment in SZ.

There are several limitations. First, this is a cross-sectional analysis,and it is not possible to determine the causal relationship between structural networks, clinical symptoms, cognitive function, etc. Second, patients in current study comprised both subjects on medication and those not receiving any. The medication may alleviate cognitive impairment and clinical symptoms in certain individuals, thus having potential influence on the findings here. Third, our finding that cognitive impairment in SZ is linked to fiber connectivity between specific regions of the right brain required further validation.

This study discovered that cognitive impairment in patients with schizophrenia (SZ) is linked to fiber connectivity between specific regions of the right brain, although this lateralization finding requires further validation. The participants included were all right-handed, precluding any correlation analysis based on hand preference grouping. Future studies should investigate whether hand dominance specifically influences the relationship between brain asymmetry and cognitive function in SZ patients. Because we were unable to conduct a correlation analysis based on hand preference grouping. Future research should evaluate whether hand use plays a specific role in the relationship between brain asymmetry and cognition in patients with SZ.

In summary, based on diffusion tractography and graph theory methods, this

study investigated the whole-brain structural dysconnection in SZ. Patients with SZ had reduced connection of between cortical and subcortical brain regions, obstacles in information transmission and integration in the brain network. The identified connectivity profile exhibited significantly ties with cognitive impairment, and highlighted the important role of white matter pathway in SZ.

Conflict of interest

The authors declare that there is no conflict of interests.

Data availability statement

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

Funding statement

This work was supported by the National Key Research and Development Program of China (2018YFC1314302), the National Natural Science Foundation of China (82271538 and 82101590)

Author Contributions

Juanjuan Ren and Chen Zhang conceived and designed the study. Rong Zhang, Yewei Wang, Xiaochang Chen, Lirong Fu, Qingyi Li collected the data. Juanjuan Ren and Xiaoxia Lei carried out main data analyses. Juanjuan Ren and Xiaoxia Lei interpreted the results. Juanjuan Ren drafted the manuscript. All authors contributed to the manuscript for important intellectual content, approved the final manuscript and agree to take responsibility according to the journal guidelines

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Table 1 Comparison of demographic and clinical information between groups

	HC(n=63)	SZ(n=61)	t/χ2	P
Age（years）	27.13±6.15	27.92±9.31	-0.556	0.579
Sex（male/female）	26/37	35/26	3.217	0.073
Education (years)	14.03±3.16	13.23±3.67	1.291	0.199
Duration of illness (years)		3.67±3.44
PANSS-total
Total		72.28±18.84
Positive		17.97±7.03
Negative		18.00±6.68
General		36.31±9.36
TEPS	81.86±21.41	68.58±25.98	3.120	0.002
SHARPs	19.33±7.08	23.98±9.67	-3.072	0.003
RBANS-total	507.77±53.82	411.03±56.34	9.539	0.000
List learning	31.20 ± 4.403	23.26±6.61	7.709	0.000
Story memory	17.00±3.88	10.59±4.235	8.584	0.000
Immediate memory	93.65±17.81	69.19±16.10	7.818	0.000
Figure copy	19.22±1.52	18.10±2.34	3.056	0.003
Line orientation	17.65±2.56	15.47±3.51	3.870	0.000
Visual span	102.08±15.30	88.60±15.60	4.741	0.000
Semantic Fluency	22.45±4.24	15.74±5.26	7.640	0.000
Speech function	97.62±14.81	80.38±15.86	6.161	0.000
Digit span	15.53±0.98	14.19±2.34	4.087	0.000
Coding	58.90±11.98	42.45±11.53	7.595	0.000
Attention	116.80±12.73	95.60±15.36	8.174	0.000
Sum of List	9.60±1.780	5.14±2.71	7.029	0.000
List recognition	19.55±1.27	18.93±1.81	2.161	0.033
Sum of story	9.60±1.80	5.14±2.71	10.503	0.000
Sum of Figure	15.90±3.43	10.78±5.09	6.388	0.000
Delayed memory	97.43±13.63	77.14±17.33	7.085	0.000

Abbreviations: PANSS, Positive and Negative Syndrome Scale; TEPS, Temporal Experience of Pleasure Scale. SHARPS, Snaith‑Hamilton Pleasure Scale; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status

Table 2 Component comprising tracts with weaker streamline counts in healthy controls compared with schizophrenia in NBS analysis

Connections	Test statistic (t values)
Thalamus-Proper
Left Thalamus-Proper to Left inferiortemporal	3.11
Left Thalamus-Proper to Left lingual	3.20
Left Thalamus-Proper to Left medialorbitofrontal	3.55
Right Thalamus-Proper to Right fusiform	3.41
Right Thalamus-Proper to Right lingual	3.77
Right Thalamus-Proper to Right postcentral	3.21
Right Thalamus-Proper to Right insula	3.55
Hippocampus
Right Hippocampus to Left inferiortemporal	3.41
Right Hippocampus to Left fusiform.	3.35
Right Hippocampus to Left superiortemporal	3.52
Putamen
Left Putamen to Left Accumbens-area.	3.52
Accumbens-area
Left Accumbens-area to Left medialorbitofrontal	3.36
Left Accumbens-area to Left middletemporal	3.26
Insula
Left inferiorparietal to Left insula.	3.19
Left parsopercularis to Left insula.	3.15
Right parsopercularis to Right insula.	3.65
Right postcentral to Right insula.	6.78
Right precentral to Right insula.	3.74
Right transversetemporal to Right insula.	3.95
Left rostralmiddlefrontal to Right insula.	3.48
Left rostralmiddlefrontal to Left insula.	4.42
Left superiortemporal to Left insula.	3.73
Left insula to Right rostralmiddlefrontal	3.27
Left insula to Right superiorfrontal	3.35
Left superiorfrontal to Right insula.	3.43
Lateralorbitofrontal
Left lateralorbitofrontal to Left rostralmiddlefrontal	4.24
Left inferiorparietal to Left medialorbitofrontal	3.37
Left medialorbitofrontal to Left rostralmiddlefrontal	3.95
Left medialorbitofrontal to Left superiorfrontal	4.05
Right medialorbitofrontal to Right superiorfrontal	3.26
Left superiorfrontal to Right caudalmiddlefrontal	3.27
Rostralanteriorcingulate
Left rostralanteriorcingulate to Right rostralanteriorcingulate.	3.88
Left rostralmiddlefrontal to Right rostralanteriorcingulate.	3.16
Left rostralanteriorcingulate to Left superiorfrontal	4.48
Left rostralanteriorcingulate to Right superiorfrontal	4.61
Right caudalanteriorcingulate to Right superiorfrontal	3.35
Left caudalanteriorcingulate to Right caudalanteriorcingulate.	3.41
Left medialorbitofrontal to Right rostralanteriorcingulate.	3.74
Inferiortemporal
Right cuneus to Right inferiortemporal	3.37
Right fusiform to Right inferiortemporal	4.06
Right inferiorparietal to Right transversetemporal	3.28
Right postcentral to Right superiortemporal	3.37
Left inferiortemporal to Right caudalmiddlefrontal	3.51
Left superiortemporal to Right caudalmiddlefrontal	3.4
Right precentral to Right superiortemporal	3.67
Left precentral to Left superiortemporal	3.53
Lingual
Left lateraloccipital to Left lingual	3.23
Left fusiform to Left lingual	4.1
Right precentral to Right supramarginal	3.16
Right parsopercularis to Right precentral	3.19
Right postcentral to Right precentral	3.54
Right postcentral to Right transversetemporal	4.33
Left superiorfrontal to Left frontalpole.	4.82

Note: tracts with weaker streamline counts were group to subcortical and cortical regions

No competing interests reported.

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Subcortical-cortical structural connectivity alternations and their application in cognitive impairments of schizophrenia

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