Investigating changes in the premotor cortex-derived frontal-striatal-thalamic subcircuit in attenuated psychosis syndrome

doi:10.21203/rs.3.rs-3756787/v1

Download PDF

Research Article

Investigating changes in the premotor cortex-derived frontal-striatal-thalamic subcircuit in attenuated psychosis syndrome

https://doi.org/10.21203/rs.3.rs-3756787/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 28 Aug, 2024

Read the published version in Brain Imaging and Behavior →

You are reading this latest preprint version

Frontal-striatal-thalamic circuit impairment is presumed to underlie schizophrenia. Individuals with attenuated psychosis syndrome (APS) show longitudinal volume reduction of the putamen in the striatum, which has a neural connection with the premotor cortex through frontal-striatal-thalamic subcircuit. However, comprehensive investigations into the biological changes in the frontal-striatal-thalamic subcircuit originating from the premotor cortex are lacking in APS. We investigated differences in fractional anisotropy (FA) values between the striatum and premotor cortex (ST-PREM) and between the thalamus and premotor cortex (T-PREM) in individuals with APS and healthy controls, using a novel method TractSeg. Our study comprised 36 individuals with APS and 38 healthy controls. There was a significant difference between the control and APS groups in the right T-PREM (Odds ratio = 1.76, p = 0.02). Other factors, such as age, sex, other values of FA, and antipsychotic medication, were not associated with differences between groups. However, while FA value reduction of ST-PREM and T-PREM in schizophrenia has been previously reported, in the present study on APS, the alteration of the FA value was limited to T-PREM in APS. This finding suggests that ST-PREM impairment is not predominant in APS but emerges in schizophrenia. Impairment of the neural network originating from the premotor cortex can lead to catatonia and aberrant mirror neuron networks that are presumed to provoke various psychotic symptoms of schizophrenia. Our findings highlight the potential role of changes in a segment of the frontal-thalamic pathway derived from the premotor cortex as a biological basis of APS.

attenuated psychosis syndrome

frontal-striatal-thalamic circuit

premotor cortex

thalamus

striatum

TractSeg

Since antipsychotics are dopamine antagonists, impairment of the dopaminergic nervous system has been considered to be involved in schizophrenia. Dopamine receptors are abundant in the striatum, which is located between the frontal lobe and thalamus. The frontal lobe plays a critical role in internal representation, while the thalamus receives information from environmental events through almost all the sensory nerves (Alexander et al., 1986; Potvin et al., 2019). Therefore, dopamine receptors in the striatum are considered to mediate the “salience” between environmental events and internal representations (Kapur, 2003; Pankow et al., 2012). Dysfunctions in the frontal-striatal-thalamic circuit can lead to misprocessing of information between environmental events and internal representations, resulting in the inappropriate assignment of salience. Aberrant salience is considered a crucial biological factor underlying self-disorders, a core symptom of schizophrenia (Leube et al., 2008; Shepherd 2013; Mishara et al., 2016).

Numerous studies have substantiated the “functional” abnormality between the frontal lobe, striatum, and thalamus using fMRI or PET in schizophrenia (Cocchi et al., 2014; Xu et al., 2021). Further, recent research has also begun to reveal “structural” changes in the frontal lobe-striatum-thalamus circuit. The fractional anisotropy (FA) value derived with diffusion tensor imaging (DTI) is a parameter of white matter fiber bundles integrity, which connect and relay information among brain regions (Kelly et al., 2018). Several studies have reported fragmented evidence of reduced FA values between the frontal lobe, striatum, and thalamic subregions in schizophrenia (de Leeuw et al., 2015; Keshavan et al., 2020; Csukly et al., 2021). Notably, the advent of a novel method, TractSeg, has enabled accurate analysis of the FA value of the whole brain, including a part of the frontal-striatal-thalamic subcircuits (Wasserthal et al., 2018). Wasserthal et al. (2020) revealed significant reductions of FA values in the left and right thalamus-premotor cortex, left and right striatum-frontal-orbital cortices, and both striatum-premotor cortices in catatonic individuals with schizophrenia compared to that in healthy controls. Additionally, they noted a significant difference in the FA values of the right striatum-fronto-orbital and left striatum-premotor cortices between non-catatonic individuals and healthy controls. Altogether, the aforementioned findings corroborate the broad range of disruption of white matter fiber bundles in frontal-striatal-thalamic circuit in schizophrenia.

However, it remains unclear whether the pathological changes in the frontal-striatal-thalamic circuit appear before onset of psychosis, including schizophrenia. Few studies have examined whether changes in frontal-striatal-thalamic circuit arise even in attenuated psychosis syndrome (APS), which exhibits subthreshold psychotic symptoms. Furthermore, the frontal-striatal-thalamic circuit comprises different parallel subcircuits originating from different frontal cortex regions. Moreover, the biological changes caused by a particular subcircuit and its mechanism of appearance are yet to be elucidated.

In our previous investigation on the frontal-striatal-thalamic circuit, we focused on structural changes in the subregions of the striatum, i.e., the putamen, caudate, and nucleus accumbens, in individuals with an at-risk mental state (ARMS); we observed a significant reduction in the right putamen volume over a 1-year follow-up period (Katagiri et al., 2019). Notably, most of the participants with ARMS were individuals with APS, suggesting potential biological changes in the putamen in APS. The putamen connects to the premotor cortex via the frontal-striatal-thalamic subcircuit. This subcircuit originates from the premotor cortex and connects with putamen in the striatum and thalamus. The premotor cortex is responsible for planning movements and is a major part of the mirror neuron network (Martin, 2012; Gallese et al., 1996). Biological changes originating from the premotor cortex neural networks are considered to be related to catatonia, and aberrant mirror neuron networks which underlie several psychotic symptoms of schizophrenia (Pridmore et al., 2008; Mehta et al., 2014; Peters et al., 2016; Heyes and Catmur, 2022).

To date, no study has investigated the impairment of neural networks originating from the premotor cortex in individuals with APS. Furthermore, comprehensive analyses of structural changes in the frontal-striatal-thalamic subcircuit, presumed to be one of the biological bases of several psychotic symptoms in individuals with APS, are lacking (Waszczuk et al., 2021). Therefore, in this study, we hypothesized that biological changes in the frontal-striatal-thalamic subcircuit originate from the premotor cortex in individuals with APS. To this end, we analyzed FA values between the striatum-premotor cortex (ST-PREM) and thalamus-premotor cortex (T-PREM) using a novel method, TractSeg (Wasserthal et al., 2018). In addition, we investigated the influence of age, sex, handedness, and antipsychotic medication on these FA values.

2.1. Participants

Participants in this study were individuals with APS, aged 18–40 years, who were recruited from the Toho University Omori Medical Center. The diagnosis of APS was established using the Structured Interview for Prodromal Syndrome (SIPS) (in Japanese) (Miller et al., 2003; Kobayashi et al., 2007) at the time of their first consultation. The SIPS includes the Scale of Prodromal Symptoms (SOPS). The dosage of antipsychotics prescribed for individuals with APS was expressed as milligram equivalents of chlorpromazine (Leucht et al., 2015). Healthy controls were recruited from among students of a neighboring university, their relatives and acquaintances, and independent sources in the community. Healthy controls were identified using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) Non-Patient Edition (in Japanese) (First et al., 1997; Takahashi et al., 2010). To determine the handedness, we used the Edinburgh Handedness Inventory (Oldfield 1971) in both control and APS groups. Written informed consent was obtained from all the participants after the study was fully explained. This study was approved by the Ethics Committee of Toho University (Approval No: A22045).

2.2. Image acquisition

Individuals with APS and controls underwent MRI. The MRI data were obtained using a 1.5-T scanner (Signa HDxt; GE Medical Systems, Waukesha, WI, USA) with a single-shot, spin-echo echo-planar imaging sequence. Diffusion MRI data were acquired using the following parameters: “b” was set at 1,000 s/mm² along 30 noncollinear directions; a single b value of 0 s/mm²; echo time (TE) = 77 ms; repetition time (TR) = 13000 ms; a 3 mm³ isotropic voxel; field of view (FOV) = 240 × 240 mm; and 60 slices.

2.3. Image processing

Raw images were initially denoised and corrected for Gibbs ringing, per the methods outlined by Veraart et al. (2016) and Kellner et al. (2016). Next, correction for eddy current and motion was performed using the eddy tool in the FMRIB Software Library (FSL Version 6.0.4) (Andersson and Sotiropoulos, 2016). Finally, the images were minimally smoothed with a Gaussian kernel with sigma value of 1 mm to suppress the effects of residual noise and Gibbs artifacts (Maximov et al., 2019). The DTI FA map was computed using standard weighted least square fit implemented in MRTrix3, as described by Veraart et al. (2013). Segmentation of the white matter tracts was performed using TractSeg, which allows semi-automatic reconstruction of the fiber bundles in subjects’ native space (Wasserthal et al., 2018). For the purpose of this study, we focused on the thalamus-premotor (T-PREM) and striatum-premotor (ST-PREM) tracts (Fig. 1).

(Insert Fig. 1 here)

2.4. Statistical analyses

All datasets were analyzed using SPSS version 28 (IBM Corp., Armonk, NY, USA). In the first step, we analyzed differences between the whole group of healthy controls (Control-W) and the whole group of individuals with APS (APS-W). A t-test was used to examine differences in age and bilateral ST-PREM and T-PREM, and chi-squared test was used to investigate the differences in sex between Control-W and APS-W groups. Further, considering the influence of age and sex (Schwehm et al., 2016), a logistic regression model was used to analyze the adjusted odds ratio (OR) with 95% confidence intervals (CI) for the risk of APS-W associated with FA values of bilateral ST-PREM and T-PREM, with adjustments for potential confounding factors such as age and sex.

In the second step, to exclude the influence of handedness and antipsychotic medication, we further analyzed the differences between the right-handed healthy control group (Control-R) and right-handed and non-medicated APS group (APS-R-N). Similar to the first step, a t-test was used to investigate differences in age and bilateral ST-PREM and T-PREM, and chi-squared test was used to examine the differences in sex between Control-R and APS-R-N groups. Subsequently a logistic regression model was used to analyze the adjusted OR with 95% CI for the risk of APS-R-N associated with FA values of bilateral ST-PREM and T-PREM, with adjustments for potential confounding factors such as age and sex.

As FA values range from 0 to 1, the FA value for each region of the bilateral ST-PREM and T-PREM was multiplied by 10² to calculate the OR. Multicollinearity was checked using the tolerance test and variance inflation factor (VIF). A p value of < 0.05 was considered to indicate a statistically significant difference.

3.1. Demographics

The study groups and the number of participants in each group were as follows (Supplement Figure 1):

Control-W group, n=38 participants (25 male, 13 female)
APS-W group, n=36 participants (14 male, 22 female)
Control-R group, n=36 (23 male, 13 female) right-handed individuals of the Control-W group
APS-R group, n=33 (12 male, 21 female) right-handed individuals of the APS-W group
APS-R-N group, n=19 (10 male, 9 female) individuals of the ARMS-R group that were not on antipsychotics

3.2. Comparison between Control-W and APS-W groups

In the first step, we analyzed differences between Control-W and APS-W groups. The t-test revealed significant differences in age (p=0.03) and FA value reduction of the right T-PREM (p=0.001) in the APS-W group when compared to the Control-W group. The chi-squared test revealed a significant difference in sex (p=0.04) between the Control-W and APS-W groups (Table 1).

(Insert Table 1 here)

To determine the influence of age and sex, a logistic regression model was used. There was a significant difference between Control-W and APS-W groups, with an adjusted OR of 1.76 in the right T-PREM (B=0.56, 95% CI: 1.10–2.82, p=0.02). Other factors, such as age, sex, and other values of FA, were not associated with differences between individuals with APS and healthy controls. All VIF values were less than five (Table 2).

(Insert Table 2 here)

3.3. Comparison of the Control-R and APS-R-N groups

In the second step, to exclude the influence of handedness and antipsychotic medication, we investigated the difference between Control-R and APS-R-N groups. There were no significant differences in terms of sex (chi-squared test, p=0.56) and age (t-test, p=0.32) between the Control-R and APS-R-N groups (Table 3). However, a significant FA value reduction of the right T-PREM (p=0.001) was observed in the APS-R-N group when compared to the Control-R group.

(Insert Table 3 here)

The logistic regression analysis revealed differences between Control-R and APS-R-N groups, with an adjusted OR of 2.22 in the right T-PREM (B=0.80, 95% CI: 1.2–4.11, p=0.01). Other factors, such as age, sex, and other FA values, did not show any association with the differences observed between individuals with APS-R-N and Control-R. All the VIF values were less than five (Table 4).

(Insert Table 4 here)

Dysfunction of the frontal-striatal-thalamic circuit is considered a pathogenesis of APS as well as schizophrenia (Mishara et al., 2016; Pantelis et al., 1997; Dandash et al., 2017; Howes et al., 2009a,b; Steullet et al., 2020). We previously reported a reduction in the longitudinal volume of the right putamen in the striatum in individuals with ARMS, most of whom were individuals with APS (Katagiri et al., 2019). As the putamen connects to the premotor cortex through frontal-striatal-thalamic subcircuit originating from the premotor cortex, we hypothesized that biological changes in the frontal-striatal-thalamic subcircuit derived from the premotor cortex in individuals with APS. In the current study, we employed a novel method, TractSeg, to analyse the FA values between ST-PREM and T-PREM and examined the effects of age, sex, handedness, and the use of antipsychotic medication on these FA values. Individuals with APS showed a reduction in FA values in the right T-PREM, suggesting the potential for biological alterations in a segment of the frontal-striatal-thalamic circuit, originating from the premotor cortex in APS.

4. 1. FA value changes in the frontal-striatal-thalamic circuit originate from the premotor cortex in APS

In the first step, logistic regression analysis with adjustments for potential confounding factors, such as age and sex, revealed a significantly lower FA value in the right T-PREM in the APS-W group than that in the Control-W group. In the second step, we excluded the influence of handedness and antipsychotic medication. Although the number of participants of both groups were relatively low, there were no significant differences in terms of sex and age between the groups. The logistic regression analysis revealed a significantly lower FA value in the right T-PREM in the APS-R-N group than in the Control-R group, consistent with results of the first step. These results suggest that there are biological changes of white matter fiber bundle, which connects and relays information among brain regions, between the right premotor cortex and thalamus in individuals with APS, regardless of sex, age, handedness, and medication.

Lesions of the premotor cortex cause apraxia (Martin, 2012), and dysfunction of the premotor cortex is supposed to underlie catatonia that is observed in several psychiatric diseases, including schizophrenia (Northoff, 2002; Walther et al., 2019). Additionally, the premotor cortex is a major part of the mirror neuron network (Gallese et al., 1996; Burns, 2006), and an aberrant mirror neuron network is related to affective echo-phenomena symptoms, ego-boundary disturbances, negative symptoms, social cognition impairments, and catatonia (Pridmore et al., 2008; Mehta et al., 2014). Recent studies suggest that pathological changes in the premotor cortex, the lowest layer of the lateral frontal cortex, underlie impairment of a broad range of the lateral frontal cortex, including the prefrontal cortex, which is in the upper layer (Abe and Hanakawa, 2009; Koechlin and Summerfield, 2007; Yeo et al., 2011; Fine and Hayden, 2022). While many studies have corroborated that impairment in the premotor cortex relates to schizophrenia, our report, indicating that the impairment of neural network originating from the premotor cortex underlie APS, provides a new perspective.

4.2. Reduction of the FA value of the T-PREM in APS

Few studies have investigated whether impairments in frontal-thalamus and frontal-striatum emerge simultaneously and within the same subcircuit in APS (Cho et al., 2016). In the present study, we comprehensively and simultaneously investigated the structural changes in the frontal-striatal-thalamic subcircuit that originate from the premotor cortex in individuals with APS. Wasserthal et al. (2020) reported FA value reduction in both ST-PREM and T-PREM using TractSeg in individuals with schizophrenia. However, in the present study, the alteration in the FA value was limited to T-PREM in APS, suggesting that ST-PREM impairment is not predominant in APS but emerges in schizophrenia (Walther et al., 2019). It has been long hypothesized that impairment of the thalamus-frontal cortex could secondly provoke an adverse hyperdopaminergic state in the striatum, resulting in the onset of schizophrenia (Davis et al., 1991; Steullet, 2020; Dorph-Petersen and Lewis, 2017; Meyer-Lindenberg et al., 2002; Shin et al., 2022). Therefore, it is possible that biological changes in the T-PREM rather than the ST-PREM may be related to the emergence of subthreshold psychotic symptoms in APS.

4.3. Pathophysiology of aberrant frontal-striatal-thalamic circuit

The longitudinal volume reduction of the right putamen observed in the study by Katagiri et al. (2019), coupled with the reduction of FA value of right T-PREM, suggests a biological change in the right side of a subcircuit of the frontal-striatal-thalamic circuit. This subcircuit, originating from the premotor cortex, is referred to as the “skeletomotor loop.” Previous animal studies have suggested that these loops not only control the motor movement of skeletal muscles but also integrates voluntary motor information with reward information via dopaminergic and other signals (Isomura et al., 2013). However, most previous studies on frontal-striatal-thalamic subcircuits were conducted on animals (Hunnicutt et al., 2016; Haber et al., 2009). Human studies have revealed a more complex and numerous sets of frontal-striatal-thalamic subcircuits, which contain features of multiple conventional subcircuits rather than corresponding precisely to just one (Cocchi et al., 2014; Draganski et al., 2008).

More recent evidence suggests that impairments relate to frontal-striatal-thalamic circuits underlie several psychotic disorders, such as depressive disorder, attention deficit hyperactivity disorder, substance misuse, as well as schizophrenia (Peters et al., 2016; Hong, 2023). Meanwhile, an increasing number of epidemiological studies have shown that APS, previously referred to as the “schizophrenia prodrome,” does not necessarily progress to schizophrenia exclusively, but to other several psychiatric disorders, such as depressive disorder, personality disorder and substance misuse. This has led to the proposal of the concept of transdiagnostic psychiatry (McGorry et al., 2018). Altogether, these findings raise the possibility that disruption of the frontal-striatal-thalamic circuit may be one of the biological bases of APS which then transitions to various diseases. Further follow-up studies, considering transdiagnostic psychiatry, are needed to clarify differences in the changes in the frontal-striatal-thalamic circuits during the transition from APS to various psychiatric disorders.

4.4. Limitations

Our study had certain limitations. First, the sample size was small, limiting the generalizability of our findings; additional large-scale studies with age- and sex-matched participants are warranted to further validate them. Moreover, our investigation was limited to the frontal-striatal-thalamic circuit derived from the premotor cortex. Previous studies on schizophrenia have repeatedly reported functional and structural changes in the prefrontal cortex rather than in the premotor cortex. Further studies are needed to investigate the relationship between the frontal-striatal-thalamic subcircuits derived from other frontal regions, such as the prefrontal cortex.

Frontal-striatal-thalamic circuit impairment is presumed to be one of the biological bases of several psychotic symptoms, including self-disorders, which is considered a core symptom of schizophrenia. In this study, we demonstrated a significant reduction in FA values in the right T-PREM, which can be considered to be part of the frontal-striatal-thalamic circuit derived from the premotor cortex in individuals with APS, compared with healthy controls. Our results indicate that the reduction of neural connection between the premotor cortex and thalamus in individuals with APS could contribute to the emergence of subthreshold psychotic symptoms. This study provides insights into the biological underpinnings of APS and its associated symptoms.

Author Contributions

NK, MM, and TN designed the study and wrote the manuscript. JS, HT, YA, and NK collected the data. NK, KK, TU, and MH contributed to image processing and data analysis. All the authors contributed to and approved the final version of the manuscript.

Acknowledgements

The authors would like to thank T. Ookubo for technical assistance with the DTI. We also thank N. Kitano for technical assistance with the SIPS/SOPS, SCID and Edinburgh Handedness Inventory tests.

Funding

Naoyuki Katagiri was supported by a JSPS KAKENHI grant [20K07980].

Ethics Approval

This study was approved by the Ethics Committee of Toho University (Approval No: A22045).

Data availability statements

The datasets generated and analyzed during the current study are not publicly available due the fact that they constitute an excerpt of research in progress but are available from the corresponding author on reasonable request.

Conflicts of Interest

None of the authors have a conflict of interest to declare.

Consent to participate

Written informed consent was obtained from all the participants after the study was fully explained.

Consent for publication

Not applicable

Abe, M., & Hanakawa, T. (2009). Functional coupling underlying motor and cognitive functions of the dorsal premotor cortex. Behavioural Brain Research, 198(1), 13–23. https://doi.org/10.1016/j.bbr.2008.10.046
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. https://doi.org/10.1146/annurev.ne.09.030186.002041
Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063–1078. https://doi.org/10.1016/j.neuroimage.2015.10.019
Burns, J. (2006). The social brain hypothesis of schizophrenia. World Psychiatry, 5(2), 77–81.
Cho, K. I., Shenton, M. E., Kubicki, M., Jung, W. H., Lee, T. Y., Yun, J. Y., Kim, S. N., & Kwon, J. S. (2016). Altered thalamo-cortical white matter connectivity: Probabilistic tractography study in clinical-high risk for psychosis and first-episode psychosis. Schizophrenia Bulletin, 42(3), 723–731. https://doi.org/10.1093/schbul/sbv169
Cocchi, L., Harding, I. H., Lord, A., Pantelis, C., Yucel, M., & Zalesky, A. (2014). Disruption of structure-function coupling in the schizophrenia connectome. NeuroImage. Clinical, 4, 779–787. https://doi.org/10.1016/j.nicl.2014.05.004
Csukly, G., Szabó, Á., Polgár, P., Farkas, K., Gyebnár, G., Kozák, L. R., & Stefanics, G. (2021). Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: A combined DTI/DCM study. Psychological Medicine, 51(12), 2083–2093. https://doi.org/10.1017/S0033291720000859
Dandash, O., Pantelis, C., & Fornito, A. (2017). Dopamine, fronto-striato-thalamic circuits and risk for psychosis. Schizophrenia Research, 180, 48–57. https://doi.org/10.1016/j.schres.2016.08.020
Davis, K. L., Kahn, R. S., Ko, G., & Davidson, M. (1991). Dopamine in schizophrenia: A review and reconceptualization. American Journal of Psychiatry, 148(11), 1474–1486. https://doi.org/10.1176/ajp.148.11.1474
de Leeuw, M., Kahn, R. S., & Vink, M. (2015). Fronto-striatal dysfunction during reward processing in unaffected siblings of schizophrenia patients. Schizophrenia Bulletin, 41(1), 94–103. https://doi.org/10.1093/schbul/sbu153
Dorph-Petersen, K. A., & Lewis, D. A. (2017). Postmortem structural studies of the thalamus in schizophrenia. Schizophrenia Research, 180, 28–35. https://doi.org/10.1016/j.schres.2016.08.007
Draganski, B., Kherif, F., Klöppel, S., Cook, P. A., Alexander, D. C., Parker, G. J., Deichmann, R., Ashburner, J., & Frackowiak, R. S. (2008). Evidence for segregated and integrative connectivity patterns in the human basal ganglia. Journal of Neuroscience, 28(28), 7143–7152. https://doi.org/10.1523/JNEUROSCI.1486-08.2008
Fine, J. M., & Hayden, B. Y. (2022). The whole prefrontal cortex is premotor cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 377(1844), 20200524. https://doi.org/10.1098/rstb.2020.0524
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. (1997). Structural clinical interview for DSM-IV axis I disorders (SCID-IV). Biometrics Research Department, New York State Psychiatric Institute.
Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119(2), 593–609. https://doi.org/10.1093/brain/119.2.593
Haber, S. N., & Calzavara, R. (2009). The cortico-basal ganglia integrative network: The role of the thalamus. Brain Research Bulletin, 78(2–3), 69–74. https://doi.org/10.1016/j.brainresbull.2008.09.013
Hong, S. B. (2023). Thalamocortical functional connectivity in youth with attention-deficit/hyperactivity disorder. Journal of Psychiatry and Neuroscience, 48(1), E50–E60. https://doi: 10.1503/jpn.220109. https://doi.org/10.1503/jpn.220109
Heyes, C., & Catmur, C. (2022). What happened to mirror neurons? Perspectives on Psychological Science, 17(1), 153–168. https://doi.org/10.1177/1745691621990638
Howes, O. D., & Kapur, S. (2009a). The dopamine hypothesis of schizophrenia: Version III–the final common pathway. Schizophrenia Bulletin (3rd version), 35(3), 549–562. https://doi.org/10.1093/schbul/sbp006
Howes, O. D., Montgomery, A. J., Asselin, M. C., Murray, R. M., Valli, I., Tabraham, P., Bramon-Bosch, E., Valmaggia, L., Johns, L., Broome, M., McGuire, P. K., & Grasby, P. M. (2009b). Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Archives of General Psychiatry, 66(1), 13–20. https://doi:10.1001/archgenpsychiatry.2008.514. https://doi.org/10.1001/archgenpsychiatry.2008.514
Hunnicutt, B. J., Jongbloets, B. C., Birdsong, W. T., Gertz, K. J., Zhong, H., & Mao, T. (2016). A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife, 5. https://doi.org/10.7554/eLife.19103
Isomura, Y., Takekawa, T., Harukuni, R., Handa, T., Aizawa, H., Takada, M., & Fukai, T. (2013). Reward-modulated motor information in identified striatum neurons. Journal of Neuroscience, 33(25), 10209–10220. https://doi.org/10.1523/JNEUROSCI.0381-13.2013
Kapur, S. (2003). Psychosis as a state of aberrant salience: A framework linking biology, phenomenology, and pharmacology in schizophrenia. American Journal of Psychiatry, 160(1), 13–23. https://doi.org/10.1176/appi.ajp.160.1.13
Katagiri, N., Pantelis, C., Nemoto, T., Tsujino, N., Saito, J., Hori, M., Yamaguchi, T., Funatogawa, T., & Mizuno, M. (2019). Longitudinal changes in striatum and sub-threshold positive symptoms in individuals with an “at risk mental state” (ARMS). Psychiatry Research. Neuroimaging, 285, 25–30. https://doi.org/10.1016/j.pscychresns.2019.01.008
Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 76(5), 1574–1581. https://doi.org/10.1002/mrm.26054
Kelly, S., Jahanshad, N., Zalesky, A., Kochunov, P., Agartz, I., Alloza, C., Andreassen, O. A., Arango, C., Banaj, N., Bouix, S., Bousman, C. A., Brouwer, R. M., Bruggemann, J., Bustillo, J., Cahn, W., Calhoun, V., Cannon, D., Carr, V., Catts, S., . . . Donohoe, G. (2018). Widespread white matter microstructural differences in schizophrenia across 4322 individuals: Results from the ENIGMA Schizophrenia DTI Working Group. Molecular Psychiatry, 23(5), 1261–1269. https://doi.org/10.1038/mp.2017.170
Keshavan, M. S., Collin, G., Guimond, S., Kelly, S., Prasad, K. M., & Lizano, P. (2020). Neuroimaging in schizophrenia. Neuroimaging Clinics of North America, 30(1), 73–83. https://doi.org/10.1016/j.nic.2019.09.007
Kobayashi, H., Nozaki, S., & Mizuno, M. (2007). Reliability of the structured interview for prodromal syndromes (Japanese version) (SIPS-J) [in Japanese]. Jpn. Bull. Soc. Psychiat, 15, 168–174.
Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. Trends in Cognitive Sciences, 11(6), 229–235. https://doi.org/10.1016/j.tics.2007.04.005
Leube, D., Whitney, C., & Kircher, T. (2008). The neural correlates of ego-disturbances (passivity phenomena) and formal thought disorder in schizophrenia. European Archives of Psychiatry and Clinical Neuroscience, 258 Suppl. 5, 22–27. https://doi.org/10.1007/s00406-008-5017-z
Leucht, S., Samara, M., Heres, S., Patel, M. X., Furukawa, T., Cipriani, A., Geddes, J., & Davis, J. M. (2015). Dose equivalents for second-generation antipsychotic drugs: The classical mean dose method. Schizophrenia Bulletin, 41(6), 1397–1402. https://doi.org/10.1093/schbul/sbv037
Martin, J. H. (2012). Neuroanatomy text and atlas, McGraw Hill Medical (4th ed). McGraw-Hill Professional.
Maximov, I. I., Alnaes, D., & Westlye, L. T. (2019). Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Human Brain Mapping, 40(14), 4146–4162. https://doi.org/10.1002/hbm.24691
McGorry, P. D., Hartmann, J. A., Spooner, R., & Nelson, B. (2018). Beyond the “at risk mental state” concept: Transitioning to transdiagnostic psychiatry. World Psychiatry, 17(2), 133–142. https://doi.org/10.1002/wps.20514
Mehta, U. M., Thirthalli, J., Aneelraj, D., Jadhav, P., Gangadhar, B. N., & Keshavan, M. S. (2014). Mirror neuron dysfunction in schizophrenia and its functional implications: A systematic review. Schizophrenia Research, 160(1–3), 9–19. https://doi.org/10.1016/j.schres.2014.10.040
Meyer-Lindenberg, A., Miletich, R. S., Kohn, P. D., Esposito, G., Carson, R. E., Quarantelli, M., Weinberger, D. R., & Berman, K. F. (2002). Reduced prefrontal activity predicts exaggerated striatal dopaminergic function in schizophrenia. Nature Neuroscience, 5(3), 267–271. https://doi.org/10.1038/nn804
Miller, T. J., McGlashan, T. H., Rosen, J. L., Cadenhead, K., Cannon, T., Ventura, J., McFarlane, W., Perkins, D. O., Pearlson, G. D., & Woods, S. W. (2003). Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: Predictive validity, interrater reliability, and training to reliability. Schizophrenia Bulletin, 29(4), 703–715. https://doi.org/10.1093/oxfordjournals.schbul.a007040
Mishara, A., Bonoldi, I., Allen, P., Rutigliano, G., Perez, J., Fusar-Poli, P., & McGuire, P. (2016). Neurobiological models of self-disorders in early schizophrenia. Schizophrenia Bulletin, 42(4), 874–880. https://doi.org/10.1093/schbul/sbv123
Northoff, G. (2002). What catatonia can tell us about “top-down modulation”: A neuropsychiatric hypothesis. Behavioral and Brain Sciences, 25(5), 555–577; discussion 578–604. https://doi.org/10.1017/s0140525x02000109
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9(1), 97–113. https://doi.org/10.1016/0028-3932(71)90067-4
Pankow, A., Knobel, A., Voss, M., & Heinz, A. (2012). Neurobiological correlates of delusion: Beyond the salience attribution hypothesis. Neuropsychobiology, 66(1), 33–43. https://doi.org/10.1159/000337132
Pantelis, C., Barnes, T. R., Nelson, H. E., Tanner, S., Weatherley, L., Owen, A. M., & Robbins, T. W. (1997). Frontal-striatal cognitive deficits in patients with chronic schizophrenia. Brain, 120(10), 1823–1843. https://doi.org/10.1093/brain/120.10.1823
Peters, S. K., Dunlop, K., & Downar, J. (2016). Cortico-striatal-thalamic loop circuits of the salience network: A central pathway in psychiatric disease and treatment. Frontiers in Systems Neuroscience, 10, 104. https://doi.org/10.3389/fnsys.2016.00104
Potvin, S., Gamache, L., & Lungu, O. (2019). A functional neuroimaging meta-analysis of self-related processing in schizophrenia. Frontiers in Neurology, 10, 990. https://doi.org/10.3389/fneur.2019.00990
Pridmore, S., Brüne, M., Ahmadi, J., & Dale, J. (2008). Echopraxia in schizophrenia: Possible mechanisms. Australian and New Zealand Journal of Psychiatry, 42(7), 565–571. https://doi.org/10.1080/00048670802119747
Schwehm, A., Robinson, D. G., Gallego, J. A., Karlsgodt, K. H., Ikuta, T., Peters, B. D., Malhotra, A. K., & Szeszko, P. R. (2016). Age and sex effects on white matter tracts in psychosis from adolescence through middle adulthood. Neuropsychopharmacology, 41(10), 2473–2480. https://doi.org/10.1038/npp.2016.47
Shepherd, G. M. (2013). Corticostriatal connectivity and its role in disease. Nature Reviews. Neuroscience, 14(4), 278–291. https://doi.org/10.1038/nrn3469
Shin, S., Jung, W. H., McCutcheon, R., Veronese, M., Beck, K., Lee, J. S., Lee, Y. S., Howes, O. D., Kim, E., & Kwon, J. S. (2022). The relationship between frontostriatal connectivity and striatal dopamine function in schizophrenia: An 18F-DOPA PET and diffusion tensor imaging study in treatment responsive and resistant patients. Psychiatry Investigation, 19(7), 570–579. https://doi.org/10.30773/pi.2022.0033
Steullet, P. (2020). Thalamus-related anomalies as candidate mechanism-based biomarkers for psychosis. Schizophrenia Research, 226, 147–157. https://doi.org/10.1016/j.schres.2019.05.027
Takahashi, S., Kitamura, T., & Okano, T. (2010). Structured Clinical Interview for DSM-IV-TR axis I disorders (2nd ed) (in Japanese). Nihon Hyoronsya. Tokyo.
Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., & Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. Neuroimage, 142, 394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016
Veraart, J., Sijbers, J., Sunaert, S., Leemans, A., & Jeurissen, B. (2013). Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls. Neuroimage, 81, 335–346. https://doi.org/10.1016/j.neuroimage.2013.05.028
Walther, S., Stegmayer, K., Wilson, J. E., & Heckers, S. (2019). Structure and neural mechanisms of catatonia. Lancet Psychiatry, 6(7), 610–619. https://doi.org/10.1016/S2215-0366(18)30474-7
Wasserthal, J., Maier-Hein, K. H., Neher, P. F., Northoff, G., Kubera, K. M., Fritze, S., Harneit, A., Geiger, L. S., Tost, H., Wolf, R. C., & Hirjak, D. (2020). Multiparametric mapping of white matter microstructure in catatonia. Neuropsychopharmacology, 45(10), 1750–1757. https://doi.org/10.1038/s41386-020-0691-2
Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018). TractSeg – Fast and accurate white matter tract segmentation. Neuroimage, 183, 239–253. https://doi.org/10.1016/j.neuroimage.2018.07.070
Waszczuk, K., Rek-Owodziń, K., Tyburski, E., Mak, M., Misiak, B., & Samochowiec, J. (2021). Disturbances in white matter integrity in the ultra-high-risk psychosis state-a systematic review. Journal of Clinical Medicine, 10(11), 2515. https://doi.org/10.3390/jcm10112515
Xu, X., Luo, S., Wen, X., Wang, X., Yin, J., Luo, X., He, B., Liang, C., Xiong, S., Zhu, D., Fu, J., Lv, D., Dai, Z., Lin, J., Li, Y., Lin, Z., Chen, W., Luo, Z., Wang, Y., & Ma, G. (2021). Genetic contribution of synapse-associated protein 97 to orbitofrontal-striatal-thalamic circuitry connectivity changes in first-episode schizophrenia. Frontiers in Psychiatry, 12, 691007. https://doi.org/10.3389/fpsyt.2021.691007
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011

Table 1. Demographic data of the Control-W and APS-W groups

	Control-W (SD)		APS-W (SD)		p
Participants (male/female)	n = 38 （25/13）		n = 36 （14/22）		0.04^*
Age (years)	25.79	4.74	23.42	4.49	0.03^*
FA value of Left ST-PREM × 10²	31.36	1.22	31.00	1.58	0.28
FA value of Right ST-PREM × 10²	32.33	1.54	31.65	1.44	0.05
FA value of Left T-PREM × 10²	34.54	1.10	34.07	1.43	0.11
FA value of Right T-PREM × 10²	35.83	1.49	34.58	1.50	0.001^*
Total score of positive symptoms	-	-	13.91	3.55	-
Total score of negative symptoms	-	-	14.78	6.01	-
Total score of disorganized symptoms	-	-	4.97	3.28	-
Total score of general symptoms	-	-	8.58	4.00	-

Control-W: healthy controls, whole group; APS-W: individuals with attenuated psychosis syndrome, whole group; SD: standard deviation; FA: fractional anisotropy; ST-PREM: striatum-premotor cortex; T-PREM: thalamus-premotor cortex. * p < 0.05

Table 2. Results of the logistic regression analysis between the Control-W and APS-W groups

	B	ES	Wald	df	p	Odds ratio	95% CI		VIF
	B	ES	Wald	df	p	Odds ratio	Min	Max
Age (years)	0.11	0.07	2.63	1.00	0.11	1.11	0.98	1.27	1.17
Sex	-0.41	0.57	0.51	1.00	0.48	0.67	0.22	2.04	1.25
FA value of Left ST-PREM × 10²	-0.32	0.28	1.30	1.00	0.25	0.73	0.42	1.26	2.10
FA value of Right ST-PREM × 10²	0.09	0.29	0.09	1.00	0.76	1.09	0.62	1.92	2.21
FA value of Left T-PREM × 10²	0.26	0.33	0.64	1.00	0.42	1.30	0.68	2.49	2.27
FA value of Right T-PREM × 10²	0.56	0.24	5.47	1.00	0.02^*	1.76	1.10	2.82	1.79

Control-W: healthy controls, whole group; APS-W: individuals with attenuated psychosis syndrome, whole group; FA: fractional anisotropy; CI: confidence intervals; VIF: variance inflation factor; ST-PREM: striatum-premotor cortex; T-PREM: thalamus-premotor cortex; B, unstandardized regression coefficient; ES, effect size; df, degrees of freedom. * p < 0.05

Table 3. Demographic data of the Control-R and APS-R-N groups

	Control-R (SD)		APS-R-N (SD)		p
Participants (male/female)	n = 36 （23/13）		n = 19 （10/9）		0.56
Age (years)	25.97	4.80	24.58	5.06	0.32
FA value of Left ST-PREM × 10²	31.34	1.20	31.01	1.73	0.42
FA value of Right ST-PREM × 10²	32.36	1.53	31.53	1.58	0.06
FA value of Left T-PREM × 10²	34.58	1.07	34.05	1.51	0.14
FA value of Right T-PREM × 10²	35.83	1.53	34.28	1.64	0.001	^*
Total score of positive symptoms	-	-	14.39	4.15	-
Total score of negative symptoms	-	-	16.00	6.59	-
Total score of disorganized symptoms	-	-	5.37	3.29	-
Total score of general symptoms	-	-	8.63	4.00	-

Control-R: right-handed healthy controls; APS-R-N: right-handed and non-medicated individuals with attenuated psychosis syndrome; SD: standard deviation; FA: fractional anisotropy; ST-PREM: striatum-premotor cortex; T-PREM: thalamus-premotor cortex. * p < 0.05

Table 4. Results of the logistic regression analysis of the Control-R and APS-R-N groups

	B	ES	Wald	df	p	Odds ratio	95% CI		VIF
	B	ES	Wald	df	p	Odds ratio	Min	Max
Age (years)	0.09	0.08	1.25	1.00	0.26	1.09	0.94	1.27	1.13
Sex	0.38	0.72	0.28	1.00	0.59	1.46	0.36	5.94	1.17
FA value of Left ST-PREM × 10²	-0.52	0.36	2.06	1.00	0.15	0.60	0.30	1.21	2.14
FA value of Right ST-PREM × 10²	0.00	0.38	0.00	1.00	1.00	1.00	0.47	2.10	2.31
FA value of Left T-PREM × 10²	0.48	0.43	1.22	1.00	0.27	1.62	0.69	3.78	2.32
FA value of Right T-PREM × 10²	0.80	0.31	6.38	1.00	0.01^*	2.22	1.20	4.11	1.98

Control-R: right-handed healthy controls; APS-R-N: right-handed and non-medicated individuals with attenuated psychosis syndrome; CI: confidence interval; VIF: variance inflation factor; FA: fractional anisotropy; ST-PREM: striatum-premotor cortex; T-PREM: thalamus-premotor cortex. * p < 0.05

No competing interests reported.

Supplementarymaterial.docx

Download PDF

Journal Publication

published 28 Aug, 2024

Read the published version in Brain Imaging and Behavior →

Editorial decision: Revision requested
19 Feb, 2024
Reviews received at journal
06 Feb, 2024
Reviewers agreed at journal
16 Jan, 2024
Reviewers invited by journal
14 Jan, 2024
Editor assigned by journal
14 Jan, 2024
Submission checks completed at journal
15 Dec, 2023
First submitted to journal
15 Dec, 2023

You are reading this latest preprint version

Investigating changes in the premotor cortex-derived frontal-striatal-thalamic subcircuit in attenuated psychosis syndrome

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1. Participants

2.2. Image acquisition

2.3. Image processing

2.4. Statistical analyses

3. Results

4. Discussion

4. 1. FA value changes in the frontal-striatal-thalamic circuit originate from the premotor cortex in APS

4.2. Reduction of the FA value of the T-PREM in APS

4.3. Pathophysiology of aberrant frontal-striatal-thalamic circuit

4.4. Limitations

5. Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1