Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis

Journal Pre-proof Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis

Minghui Hua, Yanmin Peng, Yuan Zhou, Wen Qin, Chunshui Yu, Meng Liang PII:

S0278-5846(19)30644-X

DOI:

https://doi.org/10.1016/j.pnpbp.2019.109837

Reference:

PNP 109837

To appear in:

Progress in Neuropsychopharmacology & Biological Psychiatry

Received date:

5 August 2019

Revised date:

22 November 2019

Accepted date:

6 December 2019

Please cite this article as: M. Hua, Y. Peng, Y. Zhou, et al., Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis, Progress in Neuropsychopharmacology & Biological Psychiatry(2019), https://doi.org/10.1016/j.pnpbp.2019.109837

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

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Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis

Minghui Hua a, Yanmin Peng a, Yuan Zhou b, Wen Qin c, Chunshui Yu Liang

a, *

f

School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging,

Tianjin Medical University, Tianjin, China.

CAS Key Laboratory of Behavioral Science and Magnetic Resonance Imaging

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b

, Meng

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a

a, c, d

c

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Research Center, Institute of Psychology, Beijing, China.

Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin

d

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Medical University General Hospital, Tianjin, China. CAS Center for Excellence in Brain Science and Intelligence Technolo gy, Chinese

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Academy of Sciences, Shanghai, China.

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*Corresponding author at: Dr. Meng Liang

School of Medical Imaging, Tianjin Medical University 1 Guangdong Road, Hexi District, Tianjin, 300203, China Email: [email protected] Phone/Fax: +86 (0)22 8333 6087

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Abstract Background Numerous neuroimaging studies have revealed that schizophrenia was characterized by wide-spread dysconnection among brain regions during rest measured by functional connectivity (FC). In contrast with FC, effective connectivity (EC)

investigation

of

schizophrenic

brain.

However,

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mechanistic

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provides information about directionality of brain connections and is thus valuable in a

systematic

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characterization of whole-brain resting-state EC (rsEC) and how it captures different

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information compared with resting-state FC (rsFC) in schizophrenia are still lacking.

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Aims

To systematically characterize the abnormalities of rsEC, compared with rsFC, in

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Method

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schizophrenia diagnosis.

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schizophrenia, and to test its discriminative power as a neuroimaging marker for

Whole-brain rsEC and rsFC networks were constructed using resting-state fMRI data and compared between 103 patients with schizophrenia and 110 healthy participants. Pattern classifications between patients and controls based on whole-brain rsEC and rsFC were further performed using multivariate pattern analysis. Results We identified 17 rsEC significantly disrupted (mostly decreased) in patients, among which all were associated with the thalamus and 15 were from limbic areas (including hippocampus, parahippocampus and cingulate cortex) to the thalamus. In contrast, 2

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abnormal rsFC were widely distributed in the whole brain. The classification accuracies for distinguishing patients and controls using whole-brain rsEC and rsFC patterns were 78.6% and 82.7%, respectively, and was further improved to 84.5% when combining rsEC and rsFC. Conclusions

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Schizophrenia is featured by disrupted ‘limbic areas-to-thalamus’ rsEC, in contrast

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with diffusively altered rsFC. Moreover, both rsEC and rsFC contain valuable and

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complementary information which may be used as diagnostic markers for

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schizophrenia.

Keywords: functional MRI; functional connectivity; multivariate pattern analysis;

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Granger causality; pattern classification.

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1. Introduction It is now widely accepted that brain-wide dysconnection is one of the main mechanisms of the pathophysiology of schizophrenia (Wotruba et al., 2014). Extensive evidence from brain imaging studies has shown that both anatomical and functional connectivity among widely-distributed brain regions are disrupted in

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schizophrenia. Diffusion tensor imaging studies showed reduced fractional anisotropy

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(FA) values in some particular white matter tracks such as the internal capsule,

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thalamus and corpus callosum (Wagner et al., 2015) and at the whole brain level

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(White et al., 2011), or even interact with glucose metabolism to affect cognitive

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ability (Zhang et al., 2019), in patients with schizophrenia, suggesting damages to anatomical neural fibers through which neural activities are coordinated among

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different regions. This is further corroborated by numerous evidence of disrupted

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functional connectivity (FC) among widely-distributed brain regions during sensory

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(e.g., audio- visual task) or cognitive (e.g., working memory) tasks (Meyer-Lindenberg et al., 2001; Wertz et al., 2019) as well as during rest (Liang et al., 2006; Venkataraman et al., 2012) in schizophrenia. For example, reduced FC of the fronto-temporal (Raij et al., 2009), thalamo-cortical (Woodward and Heckers, 2016) and cortico-cerebellar pathways (Lungu et al., 2013) have been widely reported in schizophrenia. However, FC only measures the instantaneous (i.e., zero-time lagged) temporal correlation (thus undirectional) between spatially distinct brain regions. In contrast, effective connectivity (EC) measures causal (thus directional) influence the neural activities in one region exerts to those in another region (Friston, 1994). 4

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Therefore, EC analysis in schizophrenia is able to depict the information flow among brain regions and is thus particularly important for providing more mechanistic interpretations of neuropathology of schizophrenia. Previous studies using EC analysis has revealed disrupted EC among regions involved in a variety of tasks such as finger-tapping (Moussa-Tooks et al., 2019), emotion processing (Potvin et al., 2017)

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and working memory (Deserno et al., 2012) or even during rest (Palaniyappan et al.,

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2013; Wang et al., 2015; Liao et al., 2018). For example, reduced EC from the inferior

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parietal lobule to the inferior frontal gyrus during working memory task (Nielsen et al.,

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2017) and within the striatal-default mode network loop during rest (Wang et al., 2015)

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have been reported in schizophrenia. However, all previous EC studies were hypothesis-driven – most of them were task-based and mainly focused on some

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certain networks involved in pre-defined tasks (Moussa-Tooks et al., 2019), whereas

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only a few examined resting-state EC but focused on some seed region selected a

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priori (Wang et al., 2015). These seed-region-based EC studies have limited scope of examination of EC within the brain as they only examine the EC between the seed region and any other region but ignore EC between non-seed regions. However, increasing evidence has shown that schizophrenia is featured by multidimensional cognitive deficits that are contributed by abnormal interactions between many brain regions or within large-scale networks (Karbasforoushan and Woodward, 2012; Sheffield and Barch, 2016). To systematically characterize the resting-state EC abnormalities in schizophrenia, it is necessary to perform a whole-brain investigation of dysconnectivity between every pair of regions in the entire brain. However, a 5

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systematical characterization of the abnormalities of resting-state EC (rsEC) at whole-brain level in schizophrenia is still lacking. Moreover, resting-state fMRI is more readily applicable in terms of clinical practice compared to task-based fMRI. This is also important considering that an objective tool for clinical diagnosis of schizophrenia is in urgent need because the currently adopted methods for diagnosing

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schizophrenia is very subjective and often problematic.

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Here, we extended EC analysis to construct whole-brain rsEC networks using Granger

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causality analysis of resting-state fMRI data. Granger causality is a technique to

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examine EC based on fMRI (Goebel et al., 2003; Liao et al., 2010, 2011) or

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electroencephalogram (EEG) data (Hesse et al., 2003) and has been proved very useful in exploring the interactions between different brain regions during certain

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tasks or during rest in health and a variety of brain disorders (Ji et al., 2013; Liao et al.,

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2010; Palaniyappan et al., 2013). In the present study, we first compared brain-wise

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rsEC between 103 patients with schizophrenia and 110 healthy participants to characterize the pattern of rsEC alterations in schizophrenia. Second, as EC and FC measure inter-regional interactions in different ways, we also performed the same analyses using resting-state FC (rsFC) to additionally examine how FC and EC capture brain connectivity abnormalities in schizophrenia differently. Finally, to test the discriminative power of whole-brain rsEC pattern as a neuroimaging marker for schizophrenia diagnosis, we further performed multivariate pattern analysis (MVPA) to classify between patients and controls based on the whole-brain rsEC, rsFC and their combination. 6

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2. Materials and methods 2.1. Participants One hundred and thirteen patients with schizophrenia and one hundred and fourteen healthy controls were recruited in this study. The inclusion criteria for all participants were: (1) aged 16 to 60 years, (2) right-handedness, (3) no contraindication for MRI,

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and (4) no substance abuse. All patients were diagnosed with Structured Clinical

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Interview for DSM-IV by experienced psychiatrists and did not have any other

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systemic disease, chronic disease or head trauma. The severity of symptoms was

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quantified with the Positive and Negative Syndrome Scale (PANSS; Table 1). The

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healthy controls did not have any psychiatric disorders or first-degree relatives with psychotic disorders. After image quality control (see details in “Data preprocessing”

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section below), 103 patients with schizophrenia and 110 healthy controls were kept in

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the final analyses. This study was approved by the Ethics Committee of Tianjin

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Medical University General Hospital. The written informed consent form was obtained from every participant before data acquirement.

2.2. MRI data acquisition The whole brain sagittal 3D T1-weighted structural images (voxel size: 1×1×1 mm3 ) and resting-state functional images (repetition time: 2000 ms; in-plane resolution: 3.4375×3.4375 mm2 ; slice thickness: 4 mm; duration: 6 min) were acquired using a 3.0T MR scanner (Discovery MR750, General Electric, Milwaukee, WI, USA) (see Supplementary Materials for detailed parameters). 7

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2.3. Data preprocessing The resting-state fMRI data were preprocessed using the software package DPARSF (Data Processing Assistant for Resting-State fMRI; http://rfmri.org/DPARSF) (Chao-Gan Y, 2010). The preprocessing steps included discarding the first 10 volumes, slice-timing, realignment, spatially normalization and smoothing (see Supplementary

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Materials for more details). The signals of each voxel were also cleaned by regressing

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out the nuisance covariates (including 24 head motion parameters (Yuan et al., 2016),

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white matter, cerebrospinal fluid and global signals), band-pass filtering (0.01-0.1 Hz)

2.4. Whole-brain parcellation

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and linear detrending.

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The whole brain was divided into 272 regions including 246 regions in the cerebrum

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and 26 regions in the cerebellum. The cerebrum parcellation was performed according

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to the Brainnetome atlas which divides the gray matter of the cerebrum into 246 subregions (http://atlas.brainnetome.org/bnatlas.html) (Fan et al., 2016). As this atlas does not include the cerebellum, the AAL (Anatomical Automatic Labeling) atlas was used to define the subregions in the cerebellum (Tzourio-Mazoyer et al., 2002). The fMRI time series of all voxels within each region were averaged to obtain an averaged time series for each region.

2.5. rsEC network construction rsEC analysis was performed using the software packa ge REST (www.restfmri.net). 8

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The bivariate coefficient-based Granger causality analysis approach (Chen et al., 2009) (based on autoregressive model with time lag order of 1) was adopted to investigate the causal relationship between each pair of brain regions, resulting in a whole-brain rsEC network for each participant. In this approach, the causal relationship between two regions is estimated by assessing whether previous signals of the fMRI time

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series in region A can predict the current signal of the time series in region B (i.e., the

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activity in A causes the activity in B).

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2.6. rsFC network construction

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rsFC analysis was also performed using REST (www.restfmri.net). rsFC between each pair of regions was estimated using Pearson's correlation coefficients between the

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each participant.

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averaged time series of these two regions, resulting in a whole-brain rsFC network for

2.7. Univariate analysis

The statistical significance of each rsEC (from region A to region B, or vice versa) in each group (patients and controls) was determined by permutation test (n=1000) and corrected for multiple comparisons (P<0.05, corrected for family-wise error (FWE)) using an in-house MATLAB (R2015b) script. In brief, in each permutation step, after randomly shuffling the signs of all participants for each EC, one-sample t tests were performed to compare every rsEC with zero and the maximal absolute t value across all rsEC was selected; this procedure was repeated 1000 times and resulted in 1000 9

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maximal absolute t values which were used to generate the null distribution for calculating the P value of each rsEC (Nichols and Holmes, 2001; Winkler et al., 2014). Note that, because the null distribution was generated using the maximal t values across all rsEC of the entire brain, the resultant P values were automatically corrected for FWE (Nichols and Hayasaka, 2003; Poldrack et al., 2011). Whether a given rsEC

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was significantly different between patients and controls was also determined by a

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similar permutation test with only one exception that two-sample t tests, instead of

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one-sample t tests, were used during the permutation procedure (P<0.05, FWE

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corrected). The statistical significance of each rsFC within each group and its

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difference between groups were determined using the same permutation procedures. Detailed procedures for permutation and multiple comparisons correction are

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2.8. MVPA

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provided in Supplementary Materials.

We further performed MVPA (implemented using an in- house MATLAB script) to investigate whether the spatial patterns of whole-brain rsEC could discriminate patients with schizophrenia from controls. In contrast with the above univariate analysis which compares every single rsEC one by one between groups, MVPA is a multivariate, machine learning, technique which examines the pattern of all rsEC within the entire brain at once by solving a classification problem. Here we adopted a support vector machine (SVM), a supervised classifier proven to be very successful in identifying neuroimaging biomarkers in various diseases including schizophrenia 10

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(Burges, 1998; Klöppel et al., 2009, 2008; Orban et al., 2018; Orrù et al., 2012), to identify a classification hyperplane between patients and controls based on the pattern of all rsEC in the whole brain. All participants were split into training and test dataset using a leave-one-out cross validation (LOOCV) procedure, and the final classification accuracy was determined by the percentage of correctly classified

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participants across all LOOCV steps. The statistical significance of the classification

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accuracy was determined by permutation test (n=1000) (detailed permutation

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procedure are provided in Supplementary Materials). We also extracted the mean

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weight of each rsEC across all LOOCV steps to quantify its contribution to the overall

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classification accuracy. To characterize the rsEC with high contributions, we further extracted the top 1% rsEC with the highest absolute weights and examined whether

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they overlap with those significantly altered rsEC identified by the univariate analysis.

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The same MVPA procedure was also performed using whole-brain rsFC and,

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importantly, also the combination of rsEC and rsFC to investigate whether the spatial patterns of whole-brain rsFC could discriminate patients from controls and whether the combination of the two could improve the classification performance. In addition, as a very large number of rsEC and rsFC were used in the MVPA which might present overfitting problem, we also repeated the same MVPA procedure using the rsEC, rsFC and their combination constructed based on the AAL atlas with a much smaller brain parcellations (116 regions) (see more details in Supplementary Materials).

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3. Results 3.1. Demographic and clinical characteristics of participants The demographics of all participants and the clinical characteristics of the patients are shown in Table 1. There was no significant difference in age or gender between the

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two groups.

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Table 1. Demographic and clinical characteristics of subjects

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Patients with schizophrenia 103

Age (years)

33.9 ± 9.6

Gender (female/male)

49/54

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Number of subjects

Antipsychotic dosage (mg/d)

Positive score

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PANSS

116.6 ± 95.9

110 33.7 ± 11.0

0.856

65/45

0.092

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16.8 ± 7.7

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20.0 ± 8.9

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General score

34.3 ± 10.5

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Total score

71.1 ± 22.3

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Negative score

P value

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Duration of illness (months)

436.7 ± 338.4

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(chlorpromazine equivalents)

Healthy controls

Note: The P values of age and gender were obtained with two -sample t-test and chi-square test, respectively. PANSS, Positive and Negative Syndrome Scale.

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3.2. Univariate statistical analysis The number of rsEC identified to be significantly present in the control group and the patient group are 2028 and 887, respectively (Figures 1A&B). The differences in rsEC between patients and controls are shown in Figure 1C. In total, 17 rsEC were identified to be significantly different between patients and controls (Figures 3A-D,

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Figure 2, detailed statistical results are listed in Table S1). Within- group analysis

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confirmed that these connections were significantly present in at least one of the two

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groups. Notably, all 17 rsEC were associated with various subregions of the thalamus

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(covering both anterior and posterior, medial and lateral parts of the thalamus) and

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mainly from the limbic areas (including the left hippocampus, parahippocampus and cingulate gyrus) to these thalamic regions. Indeed, 15 out of the 17 rsEC were from

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the limbic areas to the thalamus (10 were from the parahippocampus, 3 from the

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hippocampus and 2 from the cingulate gyrus). The remaining two rsEC included one

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from the cerebellum to the thalamus and one from the thalamus to the basal ganglia. Among these 17 rsEC, 14 had positive values in both groups but significantly decreased in patients, and 3 had positive values in the control group but changed to negative values in the patient group. The number of rsFC identified to be significantly present in the control group and the patient group are 8191 and 7191, respectively (Figures 1D&E). The differences in rsFC between patients and controls are shown in Figure 1F and Figures 3E-H. In total, 243 rsFC were identified to be significantly different between patients and controls, and these connections were confirmed to be significantly present in at least one of the 13

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two groups. Interestingly, none of the 243 rsFC involves the connections detected to be abnormal by rsEC, indicating that rsFC and rsEC capture different information about the relationship between two brain regions. Figure 3E shows that the abnormal rsFC were widely distributed within the whole brain. These abnormal rsFC included three types of changes: (1) the majority of these rsFC (172 out of 243) has positive

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values in both groups but significantly decreased in patients (Figure 3F); (2) 68 rsFC

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had positive values in the control group but changed to negative values in the patient

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group (Figure 3G); (3) 3 rsFC had negative values in both groups but the absolute

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value significantly increased in patients (Figure 3H).

Figure 1. The rsEC and rsFC matrices in healthy controls (A, D) and patients with schizophrenia (B, E), and the differences in rsEC (C) and rsFC (F) between groups. Each entry of the matrices indicates a connectivity. The color of each entry indicates the T value of one-sample t test (A, B, D, E) or the T value of two-sample t test (C, F). 14

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Figure 2. The rsEC significantly different between patients and controls (p<0.05, corrected). PhG, Parahippocampal Gyrus. Tha, Thalamus. CG, Cingulate Gyrus. Hipp, Hippocampus. BG, Basal Ganglia. Cer, Cerebellum. The code in the parenthesis after each brain region indicates the specific subregion according to the Brainnetome atlas.

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Figure 3. The spatial distribution of the rsEC (left panel) and rsFC (right panel) significantly different between patients with schizophrenia and healthy controls. All significantly different rsEC (A; blue: decreased in patients; red: increased in patients) can be

largely

divided

into

three

categories:

rsEC from

the

hippocampus

and

parahippocampal gyrus to the thalamus (B), from the cingulate gyrus to the thalamus (C),

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and from the cerebellum to the thalamus and from the thalamus to the basal ganglia (D).

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Black arrows denote the connectivity direction. All significantly different rsFC (E) can be

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largely divided into three categories: rsFC that are positive in controls but decreased

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(either still positive or close to zero) in patients (F), rsFC that are positive in controls but

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changed to negative values in patients (G), and rsFC that are negative in controls but increased (still negative, increased absolute value) in patients (H). Brain regions are

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grouped according to their anatomical locations and arranged in circles.

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3.3. MVPA based on whole-brain rsEC, rsFC and their combination The classification accuracies and their corresponding null distributions obtained using the whole-brain rsEC, rsFC and their combination are shown in Figures 4A, C and E, respectively. We found that the patients with schizophrenia were successfully distinguished from the healthy controls with the accuracies of 78.6% (rsEC), 82.7% (rsFC) and 84.5% (the combination of rsEC and rsFC) and all were significantly higher than chance level (P<0.001). The classification accuracy of the combined pattern is higher than those obtained from the rsEC or rsFC alone. Similar results were also obtained when the AAL atlas was used for brain parcellation and 17

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construction of rsEC and rsFC (Figure S1). The feature weight maps corresponding to the three classifications are shown in Figures 4B, D and F, respectively. Three out of the 17 significantly altered rsEC identified by univariate T tests were overlapping with the top 1% (i.e., 737) rsEC with the highest absolute weights indicated by MVPA. All these three rsEC were from the

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hippocampus to the thalamus. For the top 1% rsEC with the highest weights indicated

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by MVPA, nine brain regions located in the inferior temporal gyrus, orbital gyrus and

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cerebellum were involved in more than 20 rsEC with top 1% weights.

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Figure 4. MVPA results of the classification between patients with schizophrenia and controls obtained using whole-brain rsEC (A, B), rsFC (C, D) and their combination (E, F). Panels A, C and E show the classification accuracies (red vertical lines), along with the corresponding null distributions (blue bell shapes centered around chance level accuracy of 50%). P-values were calculated as the proportion of how many (out of 1000) permutations generated accuracy greater than or equal to the actual classification accuracy. If none out of 1000 permutations reached the actual accuracy, the P-value is labeled as P < 0.001 (i.e., <1/1000). Panels B, D and F show the feature weight maps of each classification – higher weights indicate higher contributions to the classification. 19

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4. Discussion In the present study, we aimed to systematically characterize the abnormalities of whole-brain rsEC (measured by Granger causality) in schizophrenia using both univariate and multivariate approaches. As a contrast, we also performed the same analyses using whole-brain rsFC. We obtained three main findings. First and most importantly, all identified rsEC alterations were associated with the thalamus,

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especially in the ‘limbic areas-to-thalamus’ pathways, suggesting that disruptions of

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information transfer from the limbic areas to the thalamus is an important

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characteristic of schizophrenia. Second, in contrast with the results of rsEC but

widely

distributed

within

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consistent with many previous findings, we observed that rsFC alterations were the

entire

brain,

indicating

that

inter-regional

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dysconnectivity is a ubiquitous characteristic of schizophrenia. Third, the whole-brain

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rsEC patterns and the whole-brain rsFC patterns contain useful and complementary

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information that can distinguish patients with schizophrenia from healthy controls, making them good candidates for computer-aided diagnosis of schizophrenia.

4.1. Abnormal thalamus-associated rsEC in schizophrenia The most pronounced findings of the present study are that all identified abnormal rsEC were associated with the thalamus, and interestingly, most of them (15 out of 17) were reduced rsEC from limbic areas (including parahippocampus, hippocampus and posterior cingulate cortex) to the thalamic regions. This is a rather str iking result as we adopted a whole-brain searching strategy but almost all identified rsEC alterations 20

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were spatially restricted to the pathways from limbic areas to the thalamus. These altered rsEC involved a wide range of thalamic subregions, which is co nsistent with the result of a recent study focusing on subdivisions of the thalamus in schizophrenia (Gong et al., 2019). Our observation highlights that disrupted information flow from these limbic areas to the thalamus may play a key role in the pathophysiology of

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schizophrenia. Indeed, the thalamus is known to act as a relay station playing an

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important role in bidirectional transmission of neuronal signals into or out of cortical

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areas including limbic system (Sherman and Guillery, 2018), and all these identified

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areas have been previously indicated to be altered structurally and/or functionally in

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schizophrenia. For example, the thalamus has been suggested to have structural and functional damages in schizophrenia, such as reduced grey matter volume and white

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matter volume (Gur et al., 1998; Konick and Friedman, 2001; Wagner et al., 2013). A

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large body of evidence from post-mortem (Harrison et al., 2003; McDonald et al.,

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2000) and imaging studies (Honea et al., 2005; Wright et al., 2000) also revealed abnormal pathology of parahippocampal gyrus and hippocampus in schizophrenia such as altered glutamatergic neurotransmission, decreased volumes and altered neural activity during tasks and rest in schizophrenia (Achim and Lepage, 2005; Di Giorgio et al., 2013; Rasetti et al., 2014). It is also reported that the posterior cingulate cortex (PCC), a key node of the default mode network (DMN) usually showing stronger activity during rest but deactivated during cognitive tasks in healthy people (Singh and Fawcett, 2008), failed to deactivate in patients with schizophrenia during some cognitive tasks (Hasenkamp et al., 2011). All these areas have been suggested to 21

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play a key role in many cognitive functions such as working memory and attention (Epstein et al., 2017; Hahn et al., 2007; Hampson et al., 2006; Rasetti et al., 2014) that are known to be impaired in patients with schizophrenia (Choi et al., 2012; Gur et al., 2007; Leavitt and Goldberg, 2009; Liddle et al., 2006; Tregellas et al., 2009). In fact, disrupted functional connectivity between the thalamus and these limbic areas has been reported in schizophrenia (Du et al., 2018; Jankowski et al., 2013; Samudra et al.,

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2015) and other memory-related diseases such as Alzheimer’s disease and amnestic

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mild cognitive impairment (Alderson et al., 2017; Zhang et al., 2009). Together with

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the evidence of dense reciprocal interconnections between the thalamus and these

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limbic areas (Aggleton et al., 2010; Vann et al., 2009), some hypotheses based on disrupted neural circuits involving thalamus and limbic areas, in particular, the Papez

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circuit, have been proposed (Vertes et al., 2001). The Papez circuit, with the

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hippocampus and the thalamus as two key nodes, is a neural circuit crucial for

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learning, memory and emotional processing and is likely hypothesized to play an important role in cognitive impairments in schizophrenia. In the present study, the three rsEC from the hippocampus to the thalamus detected by univariate analysis were also shown to have highest contributions to the classification between patients and controls partly support this hypothesis. Our finding based on directional rsEC provides further evidence that the disruption of the information flow in these circuits is more severe for the pathway from the limbic areas to the thalamus than other parts of the circuits and that this disruption can be detected even when the patients were not performing any particular task but simply resting. 22

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We also observed disrupted rsEC from cerebellum to thalamus and from thalamus to basal ganglia in patients with schizophrenia. Although the cerebellum and the basal ganglia are traditionally considered to be related to motor function (Mink, 1996; Morton and Bastian, 2004), accumulating evidence revealed that they were also implicated in many cognitive functions (Everitt and Robbins, 2005; Ravizza et al.,

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2006; Shatner et al., 2011). For instance, damages of these areas not only induced the

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dysfunction of motion, but also caused deficits of cognition and emotion (Middleton

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and Strick, 2000). It is also established that there are dense anatomical connections

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between the thalamus and the two subcortical areas (Bostan and Strick, 2010; Haber

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and Calzavara, 2009). Moreover, the two subcortical areas not only have projections to non- motor cortical areas such as frontal areas via the thalamus (Middleton and

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Strick, 1994), but also receive feedback modulations from these cortical areas through

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the thalamus (Bostan and Strick, 2010). Therefore, it has been hypothesized that the

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cognitive influences of the cerebellum and the basal ganglia may be explained by an important role of the thalamus in relaying and even mediating the influences of cognitive processes on motor processes via the cerebello-thalamo-cortical circuits (Prevosto and Sommer, 2013). Our finding fits well with this hypothesis and may partly explain the cognitive impairments in schizophrenia.

4.2. rsEC and rsFC capture different information about dysconnectivity in schizophrenia We observed that the significantly altered rsFC involved the majority of brain regions 23

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in the entire brain and most of these abnormal rsFC were decreased in patients with schizophrenia compared with healthy controls. This finding is in line with many previous studies (Liang et al., 2006; Venkataraman et al., 2012) and supported by reduced global efficiency of the whole brain rsFC network reported in a recent study (Li et al., 2019). In addition, some of the identified abnormal rsFC, mainly between

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thalamus, basal ganglia and cerebellum, were changed from positive values (healthy

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controls) to negative values (patients with schizophrenia). Moreover, a few rsFC that

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were negative in healthy controls were found to become significantly more negative

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in patients, including the rsFC between precuneus and inferior parietal lobule, and

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between postcentral gyrus and cingulate gyrus. The meaning of negative rsFC is currently controversial and is thought to be related to the global signal regression

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(GSR) (Fox et al., 2009; He and Liu, 2012; Murphy et al., 2009; Nalci et al., 2017) –

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although some researchers proposed that negative rsFC might be mathematical

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artifacts introduced by GSR (Murphy et al., 2009), more evidence has been found supporting that negative rsFC could not be solely determined by the mathematical operation of GSR but inherent in resting-state fMRI signals (Fox et al., 2009; Nalci et al., 2017). In the present study, we chose to perform GSR prior to constructing rsFC networks because it is widely used and has been shown to greatly improve the spatial specificity of rsFC maps (Fox et al., 2009; He and Liu, 2012). The observation of widely-distributed rsFC alterations is in clear contrast with the observation of rsEC alterations which are mainly restricted to the pathways from limbic areas to thalamus. rsFC measures the temporal synchronicity between neural 24

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activities of two distinct regions, whereas rsEC by Granger causality measures the influence of the neural activity of one region at a previous time on the neural activity of another region at the current time. Therefore, it seems that the brain-wide damages of the crosstalk between different regions are mainly reflected by disrupted synchronicity (measured by rsFC), while damages in the causal influence between

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regions (measured by rsEC) are relatively subtle and spatially more restricted in

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schizophrenia. Furthermore, when looking at the abnormal connectivity detected by

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the two methods more closely, we noticed that there was no overlap between the rsEC

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and the rsFC identified to be significantly different between patients with

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schizophrenia and controls. This suggests that the communications between different regions may be damaged in different ways and have different underlying mechanisms

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in schizophrenia. Our findings also suggest that both rsEC and rsFC are effective in

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about their relationship.

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detecting disrupted connectivity between regions but captures different information

4.3. Distinguishing patients with schizophrenia from controls using whole-brain rsEC and rsFC patterns Clinical diagnosis of schizophrenia mainly relies on subjective assessment based on questionnaires and psychiatrists’ experiences and thus its reliability is often questionable (Helzer et al., 1977; Pies, 2007). Therefore, researchers have been trying to develop a neuroimaging tool for objective diagnosis of schizophrenia in recent years. In the present study, we found that both the whole-brain rsEC patterns and 25

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whole-brain rsFC patterns can successfully distinguish between patients with schizophrenia and controls, suggesting that patients with schizophrenia have their characteristic rsEC pattern and rsFC pattern relative to healthy controls. Importantly, combining rsFC and rsEC further improved the classification accuracy, confirming that rsEC and rsFC capture different and, to some extent, complementary

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characteristics of the functional dysconnectivity in schizophrenia. It is worth noting

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that the improvement of classification accuracy is relatively small (1.8% increment)

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after combining rsEC and rsFC in the present study. Two reasons might explain the

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small improvement of classification performance after feature fusion. First, there

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might be a “ceiling effect” when the classification accuracy before feature fusion is already high (82.7% for rsFC in the present study) – big improvement after feature

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fusion is usually achieved when the classification performance before feature fusion is

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relatively poor, whereas the improvement is usually small when the classification

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performance before feature fusion is alread y high, as seen in many previous studies (Cabral et al., 2016; Guo et al., 2018; Punjabi et al., 2018 ; Guo et al., 2018; Liang et al., 2019). Second, although rsEC and rsFC are two different measures and are likely to capture different information, they both provide information about brain connectivity from the functional perspective. Future studies are needed to test whether structural measures would be able to provide more complementary information when merging with functional measures. Nevertheless, our results indicate that rsEC can be used as another candidate marker for developing a neuroimaging diagnostic tool for schizophrenia. 26

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Moreover, although 3 of the 17 rsEC identified by the univariate T tests also have the highest contributions (within the top 1%) to the classification between patients and controls, the overall pattern utilized by the classifier (Figure 4B) was quite different from the univariate pattern of the rsEC alterations (Figure 1C). The observed rsEC weight map (Figure 4B) indicates that the altered rsEC in schizophrenia are probably

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more diffusively distributed within the brain than detected by traditional univariate

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statistical tests. One possible reason is that traditional univariate statistical tests only

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control the false positive error and thus may have high false negative error (i.e., many

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altered rsEC may remain undetected), whereas any information useful for improving

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classification performance (i.e., even the rsEC showing weak differences between groups may be useful for the classification) would be utilized by MVPA. Having said

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this, it should also be noted that, it is possible that some significantly altered rsEC

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may have low weights because they contain redundant information with other rsEC

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and thus are not used by the classifier. Taken together, our results suggests that multivariate approaches may reveal different information from that detected by univariate analyses, and the two methods should be jointly used to better understand the rsEC alterations within the schizophrenic brain. It should be noted that, although many previous studies inferred "Granger causality" using fMRI data with TR ≥ 2s (Cottam et al., 2018; Ji et al., 2013; Liao et al., 2018; Palaniyappan et al., 2013; Zheng et al., 2017), the results of the present study may still be limited by the relatively low temporal resolution of fMRI data (TR = 2s) as the “Granger causal relationship” between brain regions is inferred using the temporal 27

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precedence of fMRI signals. Another limitation is that the above results were derived from chronic patients with schizophrenia and thus may be confounded by the effect of treatment. First-episode and drug-naïve patients with schizophrenia need to be tested in the future to see whether chronic and first-episode patients with schizophrenia

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share the same pattern of rsEC disruption.

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5. Conclusion

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The main results of the present study suggest disrupted ‘limbic areas-to-thalamus’

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pathways in patients with schizophrenia, in contrast with wide-spread abnormal rsFC

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in the entire brain. Both connectivity measures (i.e., rsEC and rsFC), especially their combination, show good discriminative power between patients with schizophrenia

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diagnosis of schizophrenia.

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and healthy controls and thus can be used as a neuroimaging marker for the objective

Acknowledgements This

work

was

supported

by

National Key

R&D

Program of China

(2017YFC0909201 and 2018YFC1314300), National Natural Science Foundation of China (81571659, 81971694 and 81771818), and Tianjin Key Technology R&D Program (17ZXMFSY00090).

Conflict of interest All authors declare that they have no conflicts of interest. 28

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Oncotarget. https://doi.org/10.18632/oncotarget.15335

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with depression: A resting-state functional MR imaging study with granger causality analysis.

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Highlights: Schizophrenia is featured by disrupted rsEC from limbic areas to thalamus

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In contrast, rsFC alterations are widely distributed within the entire brain

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rsEC and rsFC capture different information about dysconnectivity in

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Combination of rsEC and rsFC may be used as diagnostic markers for

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schizophrenia

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schizophrenia

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Author Statement

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Minghui Hua: Conceptualization; Formal analysis; Methodology; Investigation; Writing-Original Draft. Yanmin Peng: Methodology; Software. Yuan Zhou: Writing-Review and Editing. Wen Qin: Data Curation; Funding acquisition; Methodology. Chunshui Yu: Supervision; Funding acquisition. Meng Liang: Conceptualization, Data Curation; Funding acquisition; Methodology; Investigation; Project administration; Supervision; Writing-Review and Editing.

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Conflict of Interest

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Declarations of interest: none.

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Ethics Statement

This study was approved by the Ethics Committee of Tianjin Medical University General Hospital.

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The written informed consent form was obtained from every participant before data acquirement.

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This manuscript has not been published or presented elsewhere in part or in entirety and is not

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under consideration by another journal. We have read and understood your journal’s policies,

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and we believe that neither the manuscript nor the study violates any of these. There are no

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conflicts of interest to declare.

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