Neural Plasticity

Neural Plasticity / 2020 / Article
Special Issue

Neural Plasticity and Neuroimaging in Suicide and Self-harm

View this Special Issue

Research Article | Open Access

Volume 2020 |Article ID 8859388 | https://doi.org/10.1155/2020/8859388

Ting Sun, Pengfei Zhao, Xiaowei Jiang, Yifang Zhou, Chao Li, Linna Jia, Yanqing Tang, "Distinct Associations of Cognitive Impairments and Reduced Gray Matter Volumes in Remitted Patients with Schizophrenia and Bipolar Disorder", Neural Plasticity, vol. 2020, Article ID 8859388, 9 pages, 2020. https://doi.org/10.1155/2020/8859388

Distinct Associations of Cognitive Impairments and Reduced Gray Matter Volumes in Remitted Patients with Schizophrenia and Bipolar Disorder

Academic Editor: Zhifen Liu
Received11 Sep 2020
Revised04 Nov 2020
Accepted30 Nov 2020
Published11 Dec 2020

Abstract

Background. Cognitive impairments are documented in schizophrenia (SZ) and bipolar disorder (BD) and may be related to gray matter volumes (GMVs). Thus, this study is aimed at exploring whether the association between cognitive impairments and GMV alterations is similar in patients with SZ and BD and understanding the underlying neurobiological mechanisms. Methods. A total of 137 adult subjects (46 with SZ, 35 with BD, and 56 age-, sex-, and education-matched healthy controls (HC)) completed the MATRICS Consensus Cognitive Battery (MCCB) and structural magnetic resonance imaging scanning. We performed group comparisons of the cognitive impairments, the GMV alterations, and the association between them. Results. Compared with HC, the patients with SZ and BD showed shared deficits in 4 cognitive domains (i.e., processing speed, working memory, problem solving, and social cognition) and the composite. SZ and BD had commonly decreased GMVs, mainly in the insula, superior temporal pole, amygdala, anterior cingulate, and frontal cortices (superior, middle, opercular inferior, and orbital frontal gyrus). No correlation between MCCB scores and GMVs was detected in SZ. However, for BD, working memory was relevant to the right hemisphere (i.e., right insula, amygdala, superior temporal pole, and medial and dorsolateral superior frontal gyrus). Limitations. The major limitations were that not all patients were the first-episode status and no medication. Conclusions. The association was mainly limited to the BD group. Thus, the underlying pathophysiology of the cognitive deficits, in terms of GMV alterations, may be diverse between two disorders.

1. Introduction

Cognitive impairments are the characteristic of schizophrenia (SZ) [1], covering almost all main domains. Although not as severe as those with SZ [24], patients with bipolar disorder (BD) also suffer significantly and share considerable overlaps with SZ in several cognitive domains, especially processing speed, verbal learning, and working memory [5, 6]. Impairments persist even in the absence of affective and/or psychotic symptoms [68], thereby seriously affecting sociooccupational ability and causing these clinically stable people (remitted SZ and BD) to remain unable of having normal or relatively normal social life [1, 6, 7, 9].

A series of studies, such as neuroinflammation [10, 11] and neurotrophic factor [12, 13], has been conducted on the impaired cognitive function of SZ and BD, but the underlying neurobiological mechanism is still unclear. Neuroimaging techniques, applied universally in the study of neuropsychiatric disorders, infer alterations of brain structure may have an impact on cognitive function [14, 15]. Voxel-based morphometry is a useful method in investigating the whole-brain structural alterations [16].

Many findings on the altered gray matter volumes (GMVs) of subjects with SZ and BD have been reported. Although the results of these reports have slight differences, similar alterations were observed in patients with SZ and BD. For example, one study reported changed GMVs in multiple frontal-temporal cortices of the patients with SZ across two cultural backgrounds (Germany and Japan) [17]. Meanwhile, other authors used meta-analyses to summarize GMV alterations in BD and also informed the regions located in frontal-temporal cortices [18]. These common brain structural alterations were supported by the findings of other researchers [19, 20]. Other similarly altered GMVs in patients with SZ and BD, such as cingulate and insula, were also documented [1922].

These GMV alterations reported above are associated with cognitive impairments in subjects with SZ and BD. For instance, small frontal GMVs are associated with low premorbid intelligence quotient in patients with SZ and BD [15, 23]. However, studies on the association are limited thus far, and differences were observed in the findings, which were mainly concentrated on the following aspects: (1) the same impaired domain is associated with different GMV reductions in two disorders, such as social cognition, which is linked to the medial prefrontal cortex in SZ [24], while it is connected to the right middle cingulate gyrus in BD [25]; and (2) the two fields do not correlate, as the results of a cross-sectional study in subjects with SZ, which revealed that metacognition ability is independent of GMV alterations [26], and as the findings in those with BD, which indicated that cognitive deficits and GMVs have no association [27, 28].

These contradictory findings should be further studied to advance the understanding of altered brain structure that is linked to cognitive deficits. Considering the effect of the mood state and/or psychotic symptoms on GMVs [29], we focused on remitted patients with SZ and BD. We supposed that shared cognitive deficits and common GMV alterations in subjects with SZ and BD had similar associations. Thus, this study is aimed at determining similarities between the two patient groups in the severity of cognitive deficits, the extent of GMV alterations, and the correlation between cognitive impairments and GMV changes and subsequently at understanding the underlying neurobiological mechanisms of cognitive impairments in psychiatric disorders.

2. Materials and Methods

2.1. Participants

The study was conducted in a single site and recruited 137 individuals (age range, 18–50 years old): 46 with SZ, 35 with BD, and 56 healthy controls (HC). After a detailed description of the present study, all participants provided written informed consent as approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University. All participants were recruited from the inpatient and outpatient services at the Shenyang Mental Health Center and the Department of Psychiatry, First Affiliated Hospital of China Medical University, Shenyang, China. HC was recruited from the surrounding community via advertisement. The presence or absence of Axis I psychiatric diagnoses in participants was determined by two trained psychiatrists via the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Axis I Disorders. All patients met the DSM-IV diagnostic criteria for BD or SZ without any other Axis I disorders. HC had no current or lifetime, personal or familial history of DSM-IV Axis I disorders. Exclusion criteria for all participants included the following: (1) substance/alcohol abuse or dependence, (2) any concomitant major medical disorder, (3) any neurological illness, (4) a history of head trauma with loss of consciousness for ≥5 min, (5) any magnetic resonance imaging (MRI) contraindications, and (6) suboptimal imaging data quality.

Symptom severity was assessed by the Hamilton Depression Rating Scale (HAMD-17) [30], Young Mania Rating Scale (YMRS) [31], and Brief Psychiatric Rating Scale (BPRS) [32]. The clinically stable criteria for patients included the following: (1) for SZ: ; and (2) for BD: and .

2.2. Cognitive Assessment

The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) is a reliable tool in assessing cognitive function from multidomains, which was introduced to evaluate and promote cognition in SZ and validated subsequently in BD [3336]. The MCCB contains 10 tasks across 7 cognitive domains, including (a) processing speed (Trail Making Test A, Symbol Coding, and Category Fluency), (b) verbal learning (Hopkins Verbal Learning Test-Revised), (c) working memory (Spatial Span, Letter Number Span), (d) visual learning (Brief Visuospatial Memory Test-Revised), (e) reasoning, problem solving (The Mazes), (f) attention-vigilance (Continuous Performance Test-Identical Pairs), and (g) social cognition (Mayer–Salovey–Caruso Emotional Intelligence Test). A total of 10 subtest scores and a composite score are included in this instrument. All subjects were tested cognition on the same day as the MRI scan.

2.3. MRI Acquisition

MRI scans were performed on a GE Signa HD 3.0-T scanner (General Electric, Milwaukee, USA) at the Image Institute of the First Affiliated Hospital of China Medical University, Shenyang, China. T1-weighted, high-resolution, and 3D image data were collected using a 3D fast spoiled gradient-echo sequence (, , , , , , , no gap, ). A standard head coil was applied to transmit and receive the nuclear magnetic resonance signal, while earplugs and foam pads were used to reduce noise and head motion. During scanning, subjects were informed to keep their eyes closed but warned not to fall asleep.

2.4. Data Processing

As suggested by the forerunners [37], we used the voxel-based morphometry (VBM8) toolbox (http://dbm.neuro.uni-jena.de/vbm8/) to process the structural MRI data, which were incorporated into the Statistical Parametric Mapping (SPM8) software. The VBM8 processing steps included bias correction, tissue segmentation, and spatial normalization (Montreal Neurological Institute space, resampled to 1.5 mm3 isotropic voxels) by using Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) [38], modulation process (nonlinear deformations), and smoothing (Gaussian kernel with 8 mm full width at half-maximum).

2.5. Statistical Analysis

Three groups’ (SZ, BD, and HC) analyses of the demographic and clinical data were performed in the SPSS 20.0 software (SPSS Inc., Chicago, Illinois) using one-way analysis of variance (ANOVA), independent-samples -test, or the Kruskal-Wallis test for continuous variables, and the Chi-square test for categorical variables. The MCCB scores among three groups were also compared in SPSS, using a one-way analysis of covariance (ANCOVA) with diagnosis as an independent factor, and gender and age as covariates. The GMVs were analyzed in SPM8 and Data Processing Assistant for Resting-State fMRI (DPABI, 2.3, Advanced edition), and ANCOVA was also used with diagnosis as an independent factor, and gender and age as covariates. To determine the significant brain regions statistically, which were identified by the Anatomical Automatic Labeling (AAL) template, we set for each voxel and for multiple comparisons (Gaussian random field (GRF) correction). GMVs were extracted from these significantly different regions and compared between each pair (, Bonferroni correction). Then, the partial correlation analysis was employed to analyze the relationship between abnormal MCCB scores and extracted significantly GMV values with gender and age as controlled factors, and the significance level at (false discovery rate (FDR) correction).

3. Results

3.1. Demographic and Clinical Analyses

Among the SZ, BD, and HC groups, the differences in age, gender, and education were not significant, and all participants were right-handed. For scale scores, significant group effects were found in HAMD-17 () and BPRS (), but not YMRS among the three groups. The duration of the illness and the proportion of the medication use were not significantly different between the SZ and BD groups, but the type of drug was different. The SZ group used more antipsychotic drugs (), while the BD group utilized more mood stabilizers (). The first-episode status was different between the two patient groups (). More analyses about the first-episode status and medication are listed in the supplemental file (Tables S1–S6). Details about demographic and clinical data are presented in Table 1.


CharacteristicGroup; or no. (%) valuePost hoc analysis
HC ()SZ ()BD ()

Age, yeara0.9660.383
Male sexb21 (37.5%)13 (28.3%)10 (28.6%)1.2600.533
Education, yeara2.8210.063
Handedness, rightb56 (100%)46 (100%)35 (100%)
First episode, yesb27 (58.7%)10 (28.6%)7.2690.007SZ>BD
Duration (month)c () ()1.0710.304
Medication, yesb42 (91.3%)29 (82.9%)1.3110.252
Antipsychoticb38 (86.4%)17 (58.6%)7.2420.007SZ>BD
Mood stabilizerb7 (15.9%)17 (58.6%)14.450<0.001BD>SZ
Antidepressantb11 (25.0%)6 (20.7%)0.1820.670
HAMD-17, total scored13.5370.001SZ>HC ()
BD>HC ()
SZ vs. BD ()
YMRS, total scored3.2740.195
BPRS, total scored24.500<0.001SZ>HC ()
BD>HC ()
SZ vs. BD ()

BD, bipolar disorder; BPRS, Brief Psychiatric Rating Scale; , one-way ANOVA; , Kruskal-Wallis test; HAMD-17, Hamilton Depression Scale; HAMA, Hamilton Anxiety Scale; HC, healthy control; SZ, schizophrenia; SD, standard deviation; , independent-samples -test; YMRS, Young Mania Rating Scale; , Chi-square test; aOne-way ANOVA; bChi-square test; cindependent-samples -test; dKruskal-Wallis test. Significant at ; post hoc analysis is the Bonferroni correction.
3.2. Cognitive Assessment Results

ANCOVA showed significant differences in cognitive function among the three groups. First, the composite score of the patients with SZ and BD was lower than that of the HC subjects, but SZ manifested worse than BD. For MCCB subtests, post hoc analyses revealed that compared with HC, the SZ group showed impairments in all 10 cognitive tasks, while the deficits of the BD group were detected in 6 tasks, including Trail Making Test A (TMT-A), Symbol Coding, Spatial Span, Letter Number Span, Mazes, and Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT). Finally, the distinction between patients with SZ and BD in the Hopkins Verbal Learning Test (HVLT-R), Letter Number Span, Mazes, Brief Visuospatial Memory Test-Revised (BVMT-R), and Continuous Performance Test-Identical Pairs (CPT-IP) was significant (, Bonferroni correction; Figure 1).

3.3. Differences in GMV Groups

Significant group effects were detected in 10 clusters across 17 brain regions. Compared with HC, SZ and BD groups had decreased GMVs in the bilateral insula, bilateral temporal pole-superior temporal gyrus (TPOsup), limbic system (right amygdala and left anterior cingulate and paracingulate gyri (ACG)), as well as several regions of the frontal cortices (bilateral dorsolateral superior frontal gyrus (SFGdor), right medial superior frontal gyrus (SFGmed), left middle frontal gyrus (MFG), left opercular inferior frontal gyrus (IFGoperc), left orbital middle frontal gyrus (ORBmid), left orbital superior frontal gyrus (ORBsup), and bilateral rectus (REC)). Besides, there were 2 regions (i.e., left superior temporal gyrus and right supramarginal gyrus) reduced only in the SZ group (, Bonferroni correction; Table 2 and Figure 2).


ClusterBrain regionsVoxelsPeak MNI coordinatePost hoc analysis

AR-dorsolateral superior frontal gyrus16382449.528.511.629<0.001SZ<HC ()
BD < HC (P = 0.022)
SZ vs BD (p = 0.336)
BL-orbital middle frontal gyrus
L-middle frontal gyrus
L-orbital superior frontal gyrus
1444-28.560-2.8412.008<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
CL-rectus
R-rectus
1420-7.536-2111.090<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
DR-insula
R-temporal pole-superior temporal gyrus
R-amygdala
131934.57.5-19.513.476<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
EL-superior temporal gyrus943-61.5-58.51515.155<0.001SZ<HC ()
BD vs. HC ()
SZ<BD ()
FL-insula906-337.519.512.144<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
GL-dorsolateral superior frontal gyrus
L-middle frontal gyrus
L-anterior cingulate and paracingulate gyri
856-19.558.525.515.475<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
HL-insula
L-opercular inferior frontal gyrus
L-temporal pole-superior temporal gyrus
811-4816.51.514.456<0.001SZ < HC (P < 0.001)
BD < HC (P = 0.004)
SZ vs BD (p = 0.421)
IR-medial superior frontal gyrus797360-1.510.622<0.001SZ<HC ()
BD<HC ()
SZ vs. BD ()
JR-supramarginal gyrus56564.5-16.528.511.030<0.001SZ<HC ()
BD vs. HC ()
SZ<BD ()

GRF, Gaussian random field corrections; MNI, Montreal Neurological Institute; L, left; R, right; , coordinates of peak voxel. Significant at . Post hoc analysis is the Bonferroni correction.
3.4. Correlation between GMVs and MCCB

No correlation was observed between MCCB scores and GMVs in subjects with SZ anywhere across 17 altered brain regions. Nevertheless, a series of links were detected in the BD group, concentrating on the cognitive subtest across Spatial Span and Letter Number Span, which comprised working memory. Specifically, the scores of Spatial Span were relevant to the right SFGdor, and the scores of Letter Number Span were affected by the right insula, amygdala, TPOsup, and SFGmed. (, FDR correction; Figure 3).

4. Discussions

This study focused on probands with remitted SZ and BD, from cognitive impairments, GMV alterations, and the correlation between them, in which substantial similarities and differences were observed.

Considering the composite cognition, all patients with SZ and BD had deficits, and the performance of SZ was poor, which was consistent with the findings of prior studies on cognitive deficits [4, 39], wherein SZ has a worse composite score than BD. In terms of every cognitive subtest of MCCB, the SZ group showed impairments in overall 7 cognitive domains, whereas the BD group presented cognitive impairments only in 4 domains, that is, processing speed, working memory, problem solving, and social cognition, just like the findings of several cross-sectional studies [6, 40, 41]. However, some studies have found other impaired cognitive domains in patients with BD, such as deficits in visual and verbal learning found by Van Rheenen and Rossell [34]. Another study that selected BD patients during the onset found no problem solving or social cognitive abnormalities [35]. These conflicting results may be due to the emotional states of patients which were different from our study. Additionally, the BD group performed better than the SZ group in verbal learning, working memory, problem solving, visual learning, and attention, which was similar to the result of another study that concentrated on the verbal episodic memory of SZ and BD [42]. According to the findings above, the cognitive impairments of patients with SZ were more severe than those of patients with BD, broad consent with previous studies [36, 43].

Regardless of probands with SZ or BD, the alterations of GMVs in every brain region which was discovered differences from HC were reduced. This result was supported by other authors. For example, several authors documented the entire cortex volume reductions in patients with SZ who had cognitive impairments [44], and others reported that cognitively impaired patients with SZ and BD exhibited small total brain volumes [45]. Relative to HC, the common brain structural changes in both patient groups were mainly concentrated on 4 areas, including insular, temporal cortex (bilateral TPOsup), limbic system (right amygdala and left ACG), and frontal cortices (bilateral SFGdor, right SFGmed, left MFG, left IFGoperc, left ORBmid, left ORBsup, and bilateral REC), which was consistent with the results of a review that summarized the findings of GMV comparisons between SZ and BD and pointed overlapping reductions in the insula and ACG [46], and following the results of a matched control study that indicated small GMVs within frontal and temporal regions in both SZ and BD [47]. This result also agreed with the findings of other studies [21, 25, 29]. Besides, as early studies detected, our study also reported that GMV damages in patients with SZ covered more areas than those with BD, that is, primarily left STG and right SMG [46, 48].

The results of the correlation analysis showed that only the BD group had associations between cognitive impairments and GMV alterations. The decreased working memory of BD was related to reduced GMVs of the right hemisphere, containing the right insula, amygdala, TPOsup, SFGmed, and SFGdor. However, the study on the association between cognitive deficits and GMV damages in patients with BD was limited. There was a study on the gray matter density of pediatric patients with BD, in which the reductions of the left orbitofrontal cortex are associated with working memory [49]. The differences from us may be due to the age range of the participants, because the gray matter of the child is still in the developmental stage. As for other cognitive impaired domains of BD, they were independent of GMV alterations, including processing speed and social cognition, against previous surveys.

However, the probands with remitted SZ, who had more cognitive impairments and GMV alterations, were undetected any association between MCCB subtest scores and extracted values from changed brain regions. This result was different from those of prior studies. For example, one study in a Chinese Han population with SZ reported impaired working memory was correlated with GMV reductions and fractional anisotropy decrease in prefrontal and superior temporal area [50], but they applied a different method—multimodal fusion, to measure brain abnormity. Another study informed the link between hippocampal subregion volumes and cognitive performance in visual, verbal, and working memory [51], whereas we found no alterations around hippocampal. Because we aimed to identify neuroimaging substrates of cognitive impairments in psychiatric disorders, other nonaltered regions were not performed a correlation analysis, which may also be associated with cognition but not related to the diseases. Meanwhile, the findings of a Japanese research suggested that the anterior cingulate and medial frontal cortices volumes affect working memory in SZ [52]. Divergence may be considered since the patients included in the study were disease-onset, whereas the mood state and/or psychotic symptoms have an effect on GMVs [29]. Another important reason for the differences from others was that we used partial correlation analysis with gender and age as controlled factors, so that the results were net and little.

Whether the neurobiological mechanism behind SZ and BD cognitive impairment is consistent has been controversial. In recent years, some researchers propose a continuum between SZ and BD [53], so that the mechanism of the two diseases should be the same. However, our results supported the traditional view that the two diseases are independent of each other. Some studies have explored the mechanism by functional magnetic resonance imaging or white matter integrity [52, 54], and the results are also divergent. There cannot be a conclusion on this matter whether cognitive impairment is caused by a single lesion or multiple lesions, yet in terms of our results, the patterns of SZ and BD were different.

Overall, this is the first study that focused on probands with remitted SZ and BD from the neurobiological mechanism behind cognitive impairment, using MCCB to assess cognitive function and GMVs to measure brain structural alterations. We eliminated the effects of the mood state and/or psychotic symptoms by strictly limiting the state of the disease. Our results added meaningful evidence for the study of cognitive impairment mechanisms in psychiatric illnesses.

5. Limitations

The major limitations of the study were the effects of the first-episode status and medication on cognition and GMV alterations. After analyzing these factors in the two patient groups, we made some findings. The first-episode status and the use of antipsychotic affected the working memory of both patient groups. The effect of mood stabilizer was only on the GMV alterations in the SZ group. Details were listed in the supplemental file (Tables S1–S6). Hence, additional large-scale surveys with strict limitations are needed.

6. Conclusions

SZ and BD groups had shared cognitive impairments and GMV alterations, but the SZ group was more severe than the BD group in both fields. The association between the two fields was mainly limited to the BD group. Consequently, the underlying pathophysiology of cognitive deficits, at least brain structure, may be diverse between two disorders.

Data Availability

The data that support the findings of this study are available upon reasonable request by contact with the corresponding author, Yanqing Tang.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (grant nos. 2018YFC1311604 and 2016YFC1306900 to Yanqing Tang), National Science Fund for Distinguished Young Scholars (grant no. 81725005 to Fei Wang), Liaoning Education Foundation (Pandeng Scholar to Fei Wang), Innovation Team Support Plan of Higher Education of Liaoning Province (grant no. LT2017007 to Fei Wang), Major Special Construction Plan of China Medical University (grant nos. 3110117059 and 3110118055 to Fei Wang), Joint Fund of National Natural Science Foundation of China (grant no. U1808204 to Feng Wu), and Natural Science Foundation of Liaoning Province (grant no. 2019-MS-05 to Feng Wu). The authors thank all participants in the study and investigators involved in conducting the study.

Supplementary Materials

Table S1: comparison of MCCB scores and cluster values between SZ patients in the first episode and SZ patients not in the first episode. Table S2: comparison of MCCB scores and cluster values between SZ patients with antipsychotic and SZ patients without antipsychotic. Table S3: comparison of MCCB scores and cluster values between SZ patients with mood stabilizer and SZ patients without mood stabilizer. Table S4: comparison of MCCB scores and cluster values between BD patients in the first episode and BD patients not in the first episode. Table S5: comparison of MCCB scores and cluster values between BD patients with antipsychotic and BD patients without antipsychotic. Table S6: comparison of MCCB scores and cluster values between BD patients with mood stabilizer and BD patients without mood stabilizer. (Supplementary Materials)

References

  1. R. S. Kahn and R. S. Keefe, “Schizophrenia is a cognitive Illness,” JAMA Psychiatry, vol. 70, no. 10, pp. 1107–1112, 2013. View at: Publisher Site | Google Scholar
  2. L. Krabbendam, B. Arts, J. van Os, and A. Aleman, “Cognitive functioning in patients with schizophrenia and bipolar disorder: a quantitative review,” Schizophrenia Research, vol. 80, no. 2-3, pp. 137–149, 2005. View at: Publisher Site | Google Scholar
  3. P. A. Vöhringer, S. Barroilhet, A. Amerio et al., “Cognitive impairment in bipolar disorder and schizophrenia: a systematic review,” Frontiers in Psychiatry, vol. 4, p. 87, 2013. View at: Google Scholar
  4. C.-K. Chen, C.-Y. Lee, Y. Lee et al., “Could schizoaffective disorder, schizophrenia and bipolar I disorder be distinguishable using cognitive profiles?” Psychiatry Research, vol. 266, pp. 79–84, 2018. View at: Publisher Site | Google Scholar
  5. D. Kim, J. Kim, T. Koo, H. Yun, and S. Won, “Shared and distinct neurocognitive endophenotypes of schizophrenia and psychotic bipolar disorder,” Clinical Psychopharmacology and Neuroscience, vol. 13, no. 1, pp. 94–102, 2015. View at: Publisher Site | Google Scholar
  6. J. H. Jensen, U. Knorr, M. Vinberg, L. V. Kessing, and K. W. Miskowiak, “Discrete neurocognitive subgroups in fully or partially remitted bipolar disorder: associations with functional abilities,” Journal of Affective Disorders, vol. 205, pp. 378–386, 2016. View at: Publisher Site | Google Scholar
  7. A. P. Wingo, P. D. Harvey, and R. J. Baldessarini, “Neurocognitive impairment in bipolar disorder patients: functional implications,” Bipolar Disorders, vol. 11, no. 2, pp. 113–125, 2009. View at: Publisher Site | Google Scholar
  8. P.-Y. Lin, P.-W. Wang, C.-S. Chen, and C.-F. Yen, “Neurocognitive function in clinically stable individuals with long-term bipolar I disorder: comparisons with schizophrenia patients and controls,” The Kaohsiung Journal of Medical Sciences, vol. 33, no. 5, pp. 260–265, 2017. View at: Publisher Site | Google Scholar
  9. C. A. Depp, B. T. Mausbach, A. L. Harmell et al., “Meta-analysis of the association between cognitive abilities and everyday functioning in bipolar disorder,” Bipolar Disorders, vol. 14, no. 3, pp. 217–226, 2012. View at: Publisher Site | Google Scholar
  10. Y. Nakagawa and K. Chiba, “Involvement of neuroinflammation during brain development in social cognitive deficits in autism spectrum disorder and schizophrenia,” The Journal of Pharmacology and Experimental Therapeutics, vol. 358, no. 3, pp. 504–515, 2016. View at: Publisher Site | Google Scholar
  11. S. Rolstad, J. Jakobsson, C. Sellgren et al., “CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment,” European Neuropsychopharmacology, vol. 25, no. 8, pp. 1091–1098, 2015. View at: Publisher Site | Google Scholar
  12. E. Mora, M. J. Portella, G. Piñol-Ripoll et al., “High BDNF serum levels are associated to good cognitive functioning in bipolar disorder,” European Psychiatry, vol. 60, pp. 97–107, 2019. View at: Publisher Site | Google Scholar
  13. E. Bora, “Peripheral inflammatory and neurotrophic biomarkers of cognitive impairment in schizophrenia: a meta-analysis,” Psychological Medicine, vol. 49, no. 12, pp. 1971–1979, 2019. View at: Publisher Site | Google Scholar
  14. M. Kubota, N. E. M. van Haren, S. V. Haijma et al., “Association of IQ changes and progressive brain changes in patients with schizophrenia,” JAMA Psychiatry, vol. 72, no. 8, pp. 803–812, 2015. View at: Publisher Site | Google Scholar
  15. E. Antonova, V. Kumari, R. Morris et al., “The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study,” Biological Psychiatry, vol. 58, no. 6, pp. 457–467, 2005. View at: Publisher Site | Google Scholar
  16. M. Kubicki, M. E. Shenton, D. F. Salisbury et al., “Voxel-based morphometric analysis of gray matter in first episode schizophrenia,” NeuroImage, vol. 17, no. 4, pp. 1711–1719, 2002. View at: Publisher Site | Google Scholar
  17. K. Koelkebeck, U. Dannlowski, P. Ohrmann et al., “Gray matter volume reductions in patients with schizophrenia: a replication study across two cultural backgrounds,” Psychiatry research. Neuroimaging, vol. 292, pp. 32–40, 2019. View at: Publisher Site | Google Scholar
  18. X. Lu, Y. Zhong, Z. Ma et al., “Structural imaging biomarkers for bipolar disorder: meta-analyses of whole-brain voxel-based morphometry studies,” Depression and Anxiety, vol. 36, no. 4, pp. 353–364, 2018. View at: Google Scholar
  19. D. E. Job, H. C. Whalley, S. McConnell, M. Glabus, E. C. Johnstone, and S. M. Lawrie, “Structural gray matter differences between first-episode schizophrenics and normal controls using voxel-based morphometry,” NeuroImage, vol. 17, no. 2, pp. 880–889, 2002. View at: Publisher Site | Google Scholar
  20. S. Selvaraj, D. Arnone, D. Job et al., “Grey matter differences in bipolar disorder: a meta-analysis of voxel-based morphometry studies,” Bipolar Disorders, vol. 14, no. 2, pp. 135–145, 2012. View at: Publisher Site | Google Scholar
  21. A. M. Shepherd, K. R. Laurens, S. L. Matheson, V. J. Carr, and M. J. Green, “Systematic meta-review and quality assessment of the structural brain alterations in schizophrenia,” Neuroscience and Biobehavioral Reviews, vol. 36, no. 4, pp. 1342–1356, 2012. View at: Publisher Site | Google Scholar
  22. X. Wang, F. Tian, S. Wang et al., “Gray matter bases of psychotic features in adult bipolar disorder: a systematic review and voxel-based meta-analysis of neuroimaging studies,” Human Brain Mapping, vol. 39, no. 12, pp. 4707–4723, 2018. View at: Publisher Site | Google Scholar
  23. L. Gutiérrez-Galve, S. Bruno, C. A. M. Wheeler-Kingshott, M. Summers, L. Cipolotti, and M. A. Ron, “IQ and the fronto-temporal cortex in bipolar disorder,” Journal of the International Neuropsychological Society : JINS, vol. 18, no. 2, pp. 370–374, 2012. View at: Publisher Site | Google Scholar
  24. M. Yamada, K. Hirao, C. Namiki et al., “Social cognition and frontal lobe pathology in schizophrenia: a voxel-based morphometric study,” NeuroImage, vol. 35, no. 1, pp. 292–298, 2007. View at: Publisher Site | Google Scholar
  25. L. N. Maila de Castro, M. R. Albuquerque, L. Malloy-Diniz et al., “A voxel-based morphometry study of gray matter correlates of facial emotion recognition in bipolar disorder,” Psychiatry Research, vol. 233, no. 2, pp. 158–164, 2015. View at: Publisher Site | Google Scholar
  26. G. Davies, C. L. Rae, S. N. Garfinkel et al., “Impairment of perceptual metacognitive accuracy and reduced prefrontal grey matter volume in first-episode psychosis,” Cognitive Neuropsychiatry, vol. 23, no. 3, pp. 165–179, 2018. View at: Publisher Site | Google Scholar
  27. C. Delaloye, F. de Bilbao, G. Moy et al., “Neuroanatomical and neuropsychological features of euthymic patients with bipolar disorder,” The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, vol. 17, no. 12, pp. 1012–1021, 2009. View at: Publisher Site | Google Scholar
  28. S. Alonso-Lana, J. M. Goikolea, C. M. Bonnin et al., “Structural and functional brain correlates of cognitive impairment in euthymic patients with bipolar disorder,” PLoS One, vol. 11, no. 7, article e0158867, 2016. View at: Publisher Site | Google Scholar
  29. X. Wang, Q. Luo, F. Tian et al., “Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: a voxel-based meta-analysis,” Journal of psychiatry & neuroscience, vol. 44, no. 2, pp. 89–101, 2019. View at: Publisher Site | Google Scholar
  30. M. Hamilton, “A rating scale for depression,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 23, no. 1, pp. 56–62, 1960. View at: Publisher Site | Google Scholar
  31. R. C. Young, J. T. Biggs, V. E. Ziegler, and D. A. Meyer, “A rating scale for mania: reliability, validity and sensitivity,” The British Journal of Psychiatry, vol. 133, no. 5, pp. 429–435, 1978. View at: Publisher Site | Google Scholar
  32. P. Bech, J. K. Larsen, and J. Andersen, “The BPRS: psychometric developments,” Psychopharmacology Bulletin, vol. 24, no. 1, pp. 118–121, 1988. View at: Google Scholar
  33. Q. Bo, Z. Mao, X. Li, Z. Wang, C. Wang, and X. Ma, “Use of the MATRICS consensus cognitive battery (MCCB) to evaluate cognitive deficits in bipolar disorder: a systematic review and meta-analysis,” PLoS One, vol. 12, no. 4, article e0176212, 2017. View at: Publisher Site | Google Scholar
  34. T. E. Van Rheenen and S. L. Rossell, “An empirical evaluation of the MATRICS consensus cognitive battery in bipolar disorder,” Bipolar Disorders, vol. 16, no. 3, pp. 318–325, 2014. View at: Publisher Site | Google Scholar
  35. K. E. Burdick, T. E. Goldberg, B. A. Cornblatt et al., “The MATRICS consensus cognitive battery in patients with bipolar I disorder,” Neuropsychopharmacology, vol. 36, no. 8, pp. 1587–1592, 2011. View at: Publisher Site | Google Scholar
  36. S. H. Sperry, L. K. O’Connor, D. Öngür, B. M. Cohen, M. S. Keshavan, and K. E. Lewandowski, “Measuring cognition in bipolar disorder with psychosis using the MATRICS consensus cognitive battery,” Journal of the International Neuropsychological Society, vol. 21, no. 6, pp. 468–472, 2015. View at: Publisher Site | Google Scholar
  37. J. Ashburner and K. J. Friston, “Voxel-based morphometry--the methods,” NeuroImage, vol. 11, no. 6, pp. 805–821, 2000. View at: Publisher Site | Google Scholar
  38. J. Ashburner, “A fast diffeomorphic image registration algorithm,” NeuroImage, vol. 38, no. 1, pp. 95–113, 2007. View at: Publisher Site | Google Scholar
  39. E. Jiménez-López, A. I. Aparicio, E. M. Sánchez-Morla, R. Rodriguez-Jimenez, E. Vieta, and J. L. Santos, “Neurocognition in patients with psychotic and non-psychotic bipolar I disorder. A comparative study with individuals with schizophrenia,” Journal of affective disorders, vol. 222, pp. 169–176, 2017. View at: Publisher Site | Google Scholar
  40. J. M. Gold, D. M. Barch, L. M. Feuerstahler et al., “Working memory impairment across psychotic disorders,” Schizophrenia Bulletin, vol. 45, no. 4, pp. 804–812, 2019. View at: Publisher Site | Google Scholar
  41. N. Ishisaka, S. Shimano, T. Miura et al., “Neurocognitive profile of euthymic Japanese patients with bipolar disorder,” Psychiatry and Clinical Neurosciences, vol. 71, no. 6, pp. 373–382, 2017. View at: Publisher Site | Google Scholar
  42. L. S. Czepielewski, R. Massuda, P. Goi et al., “Verbal episodic memory along the course of schizophrenia and bipolar disorder: a new perspective,” European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology, vol. 25, no. 2, pp. 169–175, 2015. View at: Publisher Site | Google Scholar
  43. J. Sheffield, N. Karcher, and D. Barch, “Cognitive deficits in psychotic disorders: a lifespan perspective,” Neuropsychology Review, vol. 28, no. 4, pp. 509–533, 2018. View at: Publisher Site | Google Scholar
  44. T. E. Van Rheenen, V. Cropley, A. Zalesky et al., “Widespread volumetric reductions in schizophrenia and schizoaffective patients displaying compromised cognitive abilities,” Schizophrenia Bulletin, vol. 44, no. 3, pp. 560–574, 2018. View at: Publisher Site | Google Scholar
  45. N. Woodward and S. Heckers, “Brain structure in neuropsychologically defined subgroups of schizophrenia and psychotic bipolar disorder,” Schizophrenia Bulletin, vol. 41, no. 6, pp. 1349–1359, 2015. View at: Publisher Site | Google Scholar
  46. E. Maggioni, M. Bellani, A. C. Altamura, and P. Brambilla, “Neuroanatomical voxel-based profile of schizophrenia and bipolar disorder,” Epidemiology and Psychiatric Sciences, vol. 25, no. 4, pp. 312–316, 2016. View at: Publisher Site | Google Scholar
  47. G. G. Brown, J.-S. Lee, I. A. Strigo, M. P. Caligiuri, M. J. Meloy, and J. Lohr, “Voxel-based morphometry of patients with schizophrenia or bipolar I disorder: a matched control study,” Psychiatry Research, vol. 194, no. 2, pp. 149–156, 2011. View at: Publisher Site | Google Scholar
  48. I. Nenadic, R. Maitra, K. Langbein et al., “Brain structure in schizophrenia vs. psychotic bipolar I disorder: a VBM study,” Schizophrenia Research, vol. 165, no. 2-3, pp. 212–219, 2015. View at: Publisher Site | Google Scholar
  49. A. James, M. Hough, S. James et al., “Structural brain and neuropsychometric changes associated with pediatric bipolar disorder with psychosis,” Bipolar Disorders, vol. 13, no. 1, pp. 16–27, 2011. View at: Publisher Site | Google Scholar
  50. S. Liu, H. Wang, M. Song et al., “Linked 4-way multimodal brain differences in schizophrenia in a large Chinese Han population,” Schizophrenia Bulletin, vol. 45, no. 2, pp. 436–449, 2019. View at: Publisher Site | Google Scholar
  51. T. Vargas, D. J. Dean, K. J. Osborne et al., “Hippocampal subregions across the psychosis spectrum,” Schizophrenia Bulletin, vol. 44, no. 5, pp. 1091–1099, 2018. View at: Publisher Site | Google Scholar
  52. S. Hidese, M. Ota, J. Matsuo et al., “Association between the scores of the Japanese version of the brief assessment of cognition in schizophrenia and whole-brain structure in patients with chronic schizophrenia: a voxel-based morphometry and diffusion tensor imaging study,” Psychiatry and Clinical Neurosciences, vol. 71, no. 12, pp. 826–835, 2017. View at: Publisher Site | Google Scholar
  53. M. S. Keshavan, D. W. Morris, J. A. Sweeney et al., “A dimensional approach to the psychosis spectrum between bipolar disorder and schizophrenia: the schizo-bipolar scale,” Schizophrenia Research, vol. 133, no. 1-3, pp. 250–254, 2011. View at: Publisher Site | Google Scholar
  54. A. M. Jimenez, P. Riedel, J. Lee, E. A. Reavis, and M. F. Green, “Linking resting-state networks and social cognition in schizophrenia and bipolar disorder,” Human Brain Mapping, vol. 40, no. 16, pp. 4703–4715, 2019. View at: Publisher Site | Google Scholar

Copyright © 2020 Ting Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views453
Downloads422
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.