Edited by: Wenbin Guo, Central South University, China
Reviewed by: Jun Chen, Shanghai Mental Health Center (SMHC), China; Mingrui Xia, Beijing Normal University, China
*Correspondence: Gang Zhu,
This article was submitted to Neuroimaging and Stimulation, a section of the journal Frontiers in Psychiatry
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Recently, magnetic resonance imaging (MRI) technology has been widely used to quantitatively analyze brain structure, morphology, and functional activities, as well as to clarify the neuropathological and neurobiological mechanisms of schizophrenia. However, although there have been many relevant results and conclusions, there has been no systematic assessment of this field.
To analyze important areas of research utilizing MRI in studies of schizophrenia and explore major trends and the knowledge structure using bibliometric analysis.
Literature related to MRI studies of schizophrenia published in PubMed between January 1, 2004 and December 31, 2018 were retrieved in 5-year increments. The extracted major Medical Subject Headings (MeSH) terms/MeSH subheadings were analyzed quantitatively. Bi-clu-stering analysis, social network analysis (SNA), and strategic diagrams were employed to analyze the word matrix and co-occurrence matrix of high-frequency MeSH terms.
For the periods of 2004 to 2008, 2009 to 2013, and 2014 to 2018, the number of relevant retrieved publications were 916, 1,344, and 1,512 respectively, showing an overall growth trend. 26, 34, and 36 high-frequency major MeSH terms/MeSH subheadings were extracted in each period, respectively. In line with strategic diagrams, the main undeveloped theme clusters in 2004–2008 were effects of antipsychotics on brain structure and their curative efficacy. These themes were replaced in 2009–2013 by physiopathology mechanisms of schizophrenia, etiology of cognitive disorder, research on default mode network and schizophrenic psychology, and were partially replaced in 2014–2018 by studies of differences in the neurobiological basis for schizophrenia and other mental disorders. Based on SNA, nerve net/physiopathology and psychotic disorder/pathology were considered the emerging hotspots of research in 2009–2013 and 2014–2018.
MRI studies on schizophrenia were relatively diverse, but the theme clusters derived from each period may reflect the publication trends to some extent. Bibliometric research over a 15-year period may be helpful in depicting the overall scope of research interest and may generate novel ideas for researchers initiating new projects.
Schizophrenia, as a common and devastating mental disorder, has been characterized by abnormal social behavior and the failure to understand reality, accompanied by emotional disorder or substance abuse, and even suicide (approximately 5%) (
At present, clinical diagnosis of schizophrenia is mainly based on patients’ abnormal behavior. Clinical symptoms are described with quantitative dimensions, such as the quantitative evaluation of symptoms with the Positive and Negative Syndrome Scale (
Bibliometrics, as a branch of library and information science, originated in the early 20th century. It is a comprehensive application of mathematics and statistics to conduct quantitative analysis and description of various characteristics of published literatures, so as to provide an easy method to evaluate research status and predict the developmental trends (
Literature involved in this study were retrieved and downloaded from the PubMed database(National Center for Biotechnology Information, U.S. National Library of Medicine, Rockville Pike, Bethesda MD, USA). Medical Subject Headings (MeSH) which are characterized by accuracy (accurately revealing the subject of the literature) and specificity can be used to index and catalog literatures in PubMed. In this study, the retrieval model was set as [“Magnetic Resonance Imaging”(Mesh) OR “Diffusion Tensor Imaging”(Mesh) OR “Diffusion Tensor Imaging”(Title/Abstract) AND “Schizophrenia”(Mesh)] with the filter restriction of literature type as “journal article” and language as “English”. In addition, in order to dynamically analyze the changes in hotspots, theme trends and knowledge structure of related studies of MRI and schizophrenia, the publication scope was divided into three periods (January 1, 2004 to December 31, 2008, January 1, 2009 to December 31, 2013, and January 1, 2014 to December 31, 2018). Finally, 916, 1,344, and 1,512 related literatures were retrieved in each period, respectively. Additionally, two researchers are required to carry out the primary retrieval and literature screening based on reviewing titles, abstracts, as well as full text in some cases independently.
Bibliographic information, including publication dates, countries, titles, authors, journal categories, major MeSH terms, MeSH subheading terms, abstracts, and other related characteristics of these literatures were accurately extracted and properly kept in XML format. The principle of h-index (
Bi-clustering analysis, also known as two-way clustering, was first proposed by Hartigan in 1972 (
The two-dimensional matrix visualization, as a colorful interactive matrix, is composed of horizontal rows of high-frequency major MeSH terms/MeSH subheadings and vertical columns of PubMed unique identifiers (PMID) of these retrieved literatures, which are showed on the left and the top of the matrix, respectively. Mountain visualization attempts to describe the relationship between clusters from a three-dimensional perspective, and we can estimate the relative similarity between peaks by estimating the distance between them. The volume and height of each mountain is proportional to the number of high-frequency MeSH terms contained in a cluster and their similarity within the cluster, respectively. The greater the similarity within a cluster, the steeper the mountain. In addition, the colors of peaks include red, yellow, light blue, and dark blue. Red represents a lower standard deviation of the internal similarity in the cluster, while blue denotes a higher standard deviation. In addition, the closer the color is to a single color, the smaller is the deviation between the internal MeSH terms of various clusters and the greater is their similarity.
Furthermore, according to calculate statistical indices, including descriptive (literatures that represents this class of characteristics) and discriminating (literatures that distinguishes it from other clusters) of each high-frequency major MeSH terms/MeSH subheadings to the clusters, trace back to the source literatures by identifying the PMID that contributes the most to the formation of each cluster as the significant representative literatures. These extracted representative literatures can be used to summarize and interpret content of the theme cluster.
The analysis method of strategic diagram was first proposed by Law et al. in 1988 and aimed to describe the complex internal structure and developmental trend of hotspots in a research field on the basis of the co-occurrence matrix and bi-cluster analysis (
The strategic diagram can be divided into four quadrants moving counterclockwise (
SNA, as an increasingly applied and rapidly developing method in variety of fields (e.g., psychology, sociology, mathematics, statistics, and others), emphasizes the connectivity and interdependence of elements in a group (
Given that betweenness centrality, as a mediating role, is more applicable to describe the decisive effect within the whole network, we chose it to scale the node sizes. Calculation of the related statistical indices and drawing of the network diagrams were completed using Ucinet 6.0 software (Analytic Technologies Co., Nicholasville, Kentucky, USA), while visualization of the network structure was demonstrated using NetDraw 2.084 software (
In this study, 916, 1,344, and 1,512 literatures were retrieved from the three periods of 2004 to 2008, 2009 to 2013, and 2014 to 2018, respectively, which were then subjected to comparative analysis using the statistical indices of authors, source of countries, and journals. The annual total number of MRI studies on schizophrenia presented an overall growth trend (
The number of publications of MRI studies on schizophrenia in PubMed from 2004 to 2018.
Temporal distribution of publications of MRI studies on schizophrenia in PubMed from 2004 to 2018.
Period | Rank | Country | Top journal | Author | |||
---|---|---|---|---|---|---|---|
Name | Publications, n (%) | Title | Publication, n (%) | Name | Number of papers | ||
2004- 2008 | 1 | United States | 427(44.11%) | 162(16.71%) | Shenton ME | 35 | |
2 | England | 178(18.39%) | 86(8.88%) | McCarley RW | 34 | ||
3 | Netherlands | 178(18.39%) | 77(7.95%) | Lawrie SM | 30 | ||
4 | Ireland | 96(9.92%) | 73(7.53%) | Johnstone EC | 29 | ||
5 | Germany | 42(4.34%) | 67(6.91%) | Keshavan MS | 26 | ||
Total | 921(95.15%) | 465(47.98%) | |||||
2009- 2013 | 1 | United States | 574(39.70%) | 242(16.69%) | Calhoun VD | 58 | |
2 | Netherlands | 285(19.71%) | 132(9.10%) | Shenton ME | 41 | ||
3 | England | 273(18.88%) | 114(7.86%) | Keshavan MS | 40 | ||
4 | Ireland | 144(9.96%) | 96(6.62%) | Kubicki M | 37 | ||
5 | Germany | 70(4.84%) | 61(4.20%) | Kahn RS | 35 | ||
Total | 1346(93.09%) | 645(44.47%) | |||||
2014- 2018 | 1 | United States | 573(37.26%) | 222(14.37%) | Calhoun VD | 77 | |
2 | Netherlands | 425(27.63%) | 125(8.09%) | Pearlson GD | 36 | ||
3 | England | 311(20.22%) | 81(5.24%) | Andreassen OA | 36 | ||
4 | Ireland | 74(4.81%) | 63(4.08%) | Agartz I | 35 | ||
5 | Germany | 45(2.93%) | 62(4.01%) | Guo W | 34 | ||
Total | 1428(92.85%) | 553(35.79%) |
From the searched literature, 26, 34, and 36 high-frequency major MeSH terms/MeSH subheadings were extracted in each period, respectively, and their cumulative frequency percentages were 49.0459, 53.8805, and 52.1285% of the total, and thus could be considered as the research hotspots of MRI studies on schizophrenia in the past three 5-year time periods (
Distribution of the high-frequency major MeSH terms/MeSH subheadings of MRI studies on schizophrenia in PubMed from 2004 to 2018.
Period | Threshold value of high- and low-frequency of MeSH terms | Number of high-frequency of MeSH terms | Frequency | Cumulative frequency | |
---|---|---|---|---|---|
Min | Max | ||||
2004-2008 | 26 | 26 | 26 | 327 | 49.0459% |
2009-2013 | 33 | 34 | 33 | 663 | 53.8805% |
2014-2018 | 36 | 36 | 36 | 605 | 52.1285% |
The high-frequency MeSH terms in each period can be seen in
According to bi-clustering analysis, 26, 34, and 36 high-frequency major MeSH terms/MeSH subheadings in each period were evenly divided into three clusters (
Bi-clustering analysis of 26 high-frequency MeSH terms/MeSH subheadings and literatures of MRI studies on schizophrenia in 2004 to 2008.
Bi-clustering analysis of 34 high-frequency major MeSH terms/MeSH subheadings and literatures of MRI studies on schizophrenia in 2009 to 2013.
Bi-clustering analysis of 36 high-frequency major MeSH terms/MeSH subheadings and literatures of MRI studies on schizophrenia in 2014 to 2018.
In this study, we plotted strategic diagrams so as to systematically compare similarities and differences of the theme clusters in the three periods and explore the developmental trends of MRI studies on schizophrenia. The number of high-frequency major MeSH terms/MeSH subheadings involved in each cluster can be reflected by the area of the nodes, that is, the greater the number, the larger the area (
Strategic diagrams for MRI studies on schizophrenia in three different periods.
Cluster interpretation of high-frequency major MeSH terms/MeSH subheadings of MRI studies on schizophrenia in PubMed from 2004 to 2018.
Period | Cluster | Content and interpretation of the cluster | Rank of MeSH terms | Representative literatures (PMID) |
---|---|---|---|---|
2004-2008 | Cluster 0 | Correlation between brain structural (e.g. cerebral cortex, prefrontal cortex) abnormalities and brain function changes to explore physiopathology mechanism of schizophrenia | 1, 3, 4, 5, 7, 8, 12, 17, 21, 23 | 17085018, 17913465, 16797186, 19042913 |
Cluster 1 | 1. Neurodevelopmental abnormalities in patients with schizophrenia | 2, 6, 9, 10, 11, 14, 16, 24 | 18793730, 17166743, 17689500, 16179202 | |
2. Analysis of regional abnormal structural features and influencing factors of the brain | ||||
Cluster 2 | 1. Analysis of metabolism, therapeutic efficacy of antipsychotic agents on brain structure | 13, 15, 18, 19, 20, 22, 25, 26 | 15201569, 17507880, 15520356, 16005383, 15809403 | |
2. Schizophrenia pathology (from the perspective of temporal lobe, prefrontal cortex and anatomy and histology of brain structure) | ||||
2009–2013 | Cluster 0 | Research on the physiopathology mechanism (from the perspective of frontal lobe, temporal lobe, prefrontal cortex, neural pathways) of schizophrenia and the etiology of cognitive disorder and other complications | 6, 10, 18, 22, 24, 26, 27, 28, 31 | 19097861, 20452574, 21868203, 19683896, 22105156 |
Cluster 1 | 1. Research on brain structure and function (e.g. hippocampus, nerve fibers, cerebral cortex) and differentiate schizophrenia from bipolar disorder based on MRI | 1, 3, 8, 9, 11, 13, 16, 20, 23, 33, 34 | 24004694, 22832855, 20573561, 21878411, 19042913, 22945617 | |
2. MRI (including DTI) usages, image interpretations | ||||
Cluster 2 | 1. Research on default mode network in schizophrenia patients | 2, 4, 5, 7, 12, 14, 15, 17, 19, 21, 25, 29, 30, 32 | 21095105, 21147518, 19931396, 21277171 | |
2. Schizophrenic psychology (association between anatomical and functional cerebral deficits with related clinical symptoms) | ||||
2014–2018 | Cluster 0 | Research on brain structural and functional abnormality (e.g. prefrontal cortex, nerve net) of patients with schizophrenia, as well as physiopathology mechanism of the disease | 1, 4, 12, 14, 15, 18, 19, 23, 27, 31, 32, 34 | 24306091, 26123450, 28338738, 29527474, 26908926, 28207073 |
Cluster 1 | 1. Research on the difference of neurobiological basis between schizophrenia and other mental diseases | 2, 6, 10, 13, 16, 20, 21, 22, 24, 26, 29, 33, 35 | 28347393, 25904725, 25089761, 29257977, 25829144, 25968549 | |
2. Effects of antipsychotics on brain structural changes | ||||
Cluster 2 | 1. Effects of genetic load on brain function of schizophrenia patients | 3, 5, 7, 8, 9, 11, 17, 25, 28, 30, 36 | 27479923, 27375133, 27829096, 29935206, 29247760 | |
2. Identification of schizophrenia biomarkers |
“Rank of MeSH terms” represents the number of high-frequency major MeSH terms/MeSH subheadings in each period as shown in
In the first period of 2004–2008, Cluster 0 and Cluster 1 were located in Quadrant I. Cluster 0 represents the correlation between brain structural abnormalities and brain function changes to explore physiopathology mechanism of schizophrenia, while Cluster 1 represented neurodevelopmental abnormalities in patients with schizophrenia and analysis of regional abnormal structural features and influencing factors in the brain. These two clusters were developed and in the core status with adequate centrality and high density. Cluster 2 in Quadrant III represented analysis of metabolism, therapeutic efficacy of antipsychotic agents on brain structure and schizophrenia pathology, which had not matured and were in the beginning stages of research in this field.
Compared with the results of 2004–2008, research on brain structure and function in the 2009–2013 period was still located in Quadrant I and was regarded as a mature and developed research area. However, in contrast to the similar theme cluster contents in the previous period, research in the 2009–2013 period was not only focused on the study of brain structure and function in patients with schizophrenia, but also paid more attention to the differential diagnosis of patients with bipolar disorder or other mental disorders. Furthermore, research on the physiopathology mechanisms of schizophrenia and the etiology of cognitive disorder and other complications, the structure and function of the brain default mode network, as well as schizophrenic psychology were newly developed themes.
In the third period of 2014–2018, theme clusters in Quadrant III, including research on differences in the neurobiological basis between schizophrenia and other mental diseases, were similar to the developed and mature theme contents in the first and second periods, which were identified as peripheral and undeveloped themes in the most recent 5 years. In addition, new emerging theme clusters researching on brain structural and functional abnormality of patients with schizophrenia, as well as physiopathology mechanism of the disease were located in Quadrant II, indicating they were developed but still peripheral research areas. Other emerging theme clusters, including effects of genetic load on brain function of schizophrenia patients, as well as identification of schizophrenia biomarkers, were new main undeveloped themes situated in Quadrant IV, which represented central and undeveloped research topics.
To summarize, these three strategic diagrams clearly revealed the current situation and development tendency of each theme cluster of MRI studies on schizophrenia during three different periods.
In this study, statistical indices of degree, betweenness and closeness centrality were applied to describe the knowledge structure of SNA networks in three different periods (
Descriptive statistics for centrality measures of MRI studies on schizophrenia from 2004 to 2018.
Period | Density | Degree | Closeness | Betweenness | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Max | Min |
|
Network centralization | Max | Min |
|
Network centralization | Max | Min |
|
Network centralization | ||
2004–2008 | 8.145±15.599 | 674.00 | 41.00 | 203.69 ± 184.44 | 17.88% | 100.00 | 62.50 | 79.90 ± 10.10 | 42.69% | 8.42 | 0.48 | 3.39 ± 2.38 | 1.74% |
2009–2013 | 8.080 ± 17.782 | 1311.00 | 73.00 | 266.65 ± 273.88 | 15.80% | 100.00 | 66.00 | 80.05 ± 9.82 | 41.76% | 10.61 | 0.61 | 4.41 ± 2.95 | 1.21% |
2014-2018 | 7.062 ± 16.209 | 1204.00 | 48.00 | 247.17 ± 241.62 | 13.47% | 97.22 | 64.82 | 81.18 ± 8.76 | 33.49% | 9.38 | 0.89 | 4.31 ± 2.33 | 0.88% |
Descriptive statistics for centrality measure about MRI studies on schizophrenia from 2004 to 2018.
Period | Rank of MeSH terms | High-frequency MeSH terms/MeSH subheadings | Centrality | Rank of MeSH terms | High-frequency MeSH terms/MeSH subheadings | Centrality | ||||
---|---|---|---|---|---|---|---|---|---|---|
Degree | Betweenness | Closeness | Degree | Betweenness | Closeness | |||||
2004–2008 | 3 | Magnetic Resonance Imaging | 674.000 | 5.320 | 89.286 | 15 | Antipsychotic Agents/therapeutic use | 99.000 | 3.174 | 80.645 |
1 | Schizophrenia/physiopathology | 663.000 | 8.417 | 100.000 | 16 | Cerebral Cortex/pathology | 93.000 | 2.587 | 75.758 | |
4 | Schizophrenia/diagnosis | 461.000 | 8.417 | 100.000 | 14 | Brain Mapping | 91.000 | 1.650 | 71.429 | |
5 | Schizophrenic Psychology | 429.000 | 5.313 | 92.593 | 21 | Diffusion Magnetic Resonance Imaging | 86.000 | 1.927 | 71.429 | |
2 | Schizophrenia/pathology | 415.000 | 7.593 | 96.154 | 20 | Brain/anatomy & histology | 81.000 | 1.653 | 73.529 | |
7 | Image Processing, Computer-Assisted | 389.000 | 1.736 | 78.125 | 24 | Image Interpretation, Computer-Assisted/methods | 72.000 | 0.482 | 62.500 | |
6 | Brain/pathology | 299.000 | 3.682 | 80.645 | 25 | Temporal Lobe/pathology | 69.000 | 2.238 | 73.529 | |
8 | Brain/physiopathology | 275.000 | 5.543 | 86.207 | 11 | Schizophrenia/complications | 69.000 | 1.530 | 71.429 | |
9 | Schizophrenia/genetics | 178.000 | 7.441 | 96.154 | 23 | Cerebral Cortex/physiopathology | 67.000 | 1.454 | 71.429 | |
17 | Imaging, Three-Dimensional | 162.000 | 1.246 | 73.529 | 19 | Magnetic Resonance Imaging/statistics & numerical data | 65.000 | 1.313 | 71.429 | |
12 | Prefrontal Cortex/physiopathology | 144.000 | 3.388 | 80.645 | 26 | Prefrontal Cortex/pathology | 63.000 | 1.480 | 75.758 | |
10 | Magnetic Resonance Imaging/methods | 135.000 | 4.448 | 83.333 | 22 | Schizophrenia/epidemiology | 55.000 | 2.312 | 73.529 | |
13 | Schizophrenia/drug therapy | 121.000 | 2.806 | 80.645 | 18 | Schizophrenia/metabolism | 41.000 | 0.851 | 67.568 | |
2009– 2013 | 1 | Schizophrenia/pathology | 1322.000 | 10.606 | 100.000 | 16 | Psychotic Disorders/pathology | 140.000 | 4.133 | 80.488 |
2 | Schizophrenia/physiopathology | 909.000 | 9.658 | 97.059 | 27 | Neural Pathways/pathology | 127.000 | 4.210 | 80.488 | |
4 | Schizophrenic Psychology | 754.000 | 10.606 | 100.000 | 22 | Temporal Lobe/pathology | 114.000 | 3.411 | 78.571 | |
3 | Brain/pathology | 700.000 | 7.333 | 91.667 | 25 | Neural Pathways/physiopathology | 114.000 | 2.137 | 71.739 | |
5 | Brain/physiopathology | 488.000 | 5.934 | 84.615 | 23 | Bipolar Disorder/pathology | 114.000 | 1.953 | 71.739 | |
7 | Magnetic Resonance Imaging | 447.000 | 8.232 | 91.667 | 28 | Cognition Disorders/etiology | 113.000 | 2.103 | 71.739 | |
6 | Brain Mapping | 397.000 | 8.011 | 91.667 | 21 | Cerebral Cortex/physiopathology | 111.000 | 2.063 | 71.739 | |
10 | Schizophrenia/complications | 320.000 | 6.084 | 84.615 | 26 | Brain/blood supply | 111.000 | 0.794 | 67.347 | |
9 | Magnetic Resonance Imaging/methods | 302.000 | 6.204 | 84.615 | 20 | Hippocampus/pathology | 104.000 | 3.953 | 78.571 | |
8 | Schizophrenia/genetics | 281.000 | 9.526 | 97.059 | 29 | Emotions/physiology | 102.000 | 1.975 | 71.739 | |
12 | Schizophrenia/diagnosis | 246.000 | 7.139 | 89.189 | 34 | Image Interpretation, Computer-Assisted/methods | 100.000 | 1.012 | 67.347 | |
11 | Cerebral Cortex/pathology | 244.000 | 4.293 | 82.500 | 30 | Brain Mapping/methods | 98.000 | 2.873 | 75.000 | |
19 | Image Processing, Computer-Assisted | 204.000 | 3.886 | 78.571 | 24 | Prefrontal Cortex/pathology | 94.000 | 0.607 | 66.000 | |
13 | Nerve Fibers, Myelinated/pathology | 200.000 | 5.405 | 84.615 | 15 | Schizophrenia/drug therapy | 91.000 | 2.404 | 75.000 | |
17 | Nerve Net/physiopathology | 172.000 | 1.468 | 70.213 | 31 | Frontal Lobe/pathology | 86.000 | 3.229 | 76.744 | |
14 | Prefrontal Cortex/physiopathology | 155.000 | 2.383 | 71.739 | 33 | Diffusion Tensor Imaging/methods | 85.000 | 0.917 | 68.750 | |
18 | Memory, Short-Term/physiology | 148.000 | 4.309 | 78.571 | 32 | Antipsychotic Agents/therapeutic use | 73.000 | 1.154 | 70.213 | |
2014- 2018 | 1 | Schizophrenia/physiopathology | 1240.000 | 7.924 | 97.222 | 13 | Schizophrenia/complications | 166.000 | 7.320 | 89.744 |
2 | Schizophrenia/pathology | 945.000 | 8.434 | 97.222 | 21 | Cerebral Cortex/pathology | 155.000 | 4.272 | 79.545 | |
4 | Brain/physiopathology | 577.000 | 3.869 | 83.333 | 16 | Schizophrenia/drug therapy | 154.000 | 3.862 | 81.395 | |
3 | Schizophrenia/diagnostic imaging | 514.000 | 7.642 | 94.595 | 24 | Bipolar Disorder/pathology | 131.000 | 0.889 | 64.815 | |
5 | Schizophrenic Psychology | 466.000 | 7.722 | 94.595 | 23 | Brain Mapping/methods | 129.000 | 4.990 | 83.333 | |
6 | Brain/pathology | 407.000 | 5.881 | 85.366 | 17 | Schizophrenia/diagnosis | 126.000 | 5.120 | 83.333 | |
7 | Magnetic Resonance Imaging/methods | 360.000 | 9.382 | 97.222 | 34 | Bipolar Disorder/physiopathology | 117.000 | 1.391 | 70.000 | |
9 | Brain/diagnostic imaging | 279.000 | 2.225 | 76.087 | 27 | Memory, Short-Term/physiology | 113.000 | 1.535 | 71.429 | |
10 | White Matter/pathology | 262.000 | 5.741 | 87.500 | 25 | Brain Mapping | 111.000 | 4.194 | 79.545 | |
14 | Nerve Net/physiopathology | 240.000 | 4.592 | 83.333 | 30 | Psychotic Disorders/diagnostic imaging | 111.000 | 3.946 | 81.395 | |
15 | Psychotic Disorders/physiopathology | 237.000 | 5.823 | 89.744 | 28 | White Matter/diagnostic imaging | 108.000 | 2.059 | 72.917 | |
8 | Schizophrenia/genetics | 213.000 | 7.621 | 92.105 | 29 | Diffusion Tensor Imaging/methods | 107.000 | 4.112 | 74.468 | |
11 | Magnetic Resonance Imaging | 205.000 | 7.086 | 89.744 | 26 | Antipsychotic Agents/therapeutic use | 101.000 | 2.463 | 72.917 | |
12 | Prefrontal Cortex/physiopathology | 198.000 | 2.618 | 76.087 | 32 | Neural Pathways/physiopathology | 99.000 | 1.663 | 72.917 | |
19 | Connectome/methods | 194.000 | 3.040 | 79.545 | 31 | Emotions/physiology | 93.000 | 2.463 | 76.087 | |
18 | Cerebral Cortex/physiopathology | 182.000 | 2.482 | 77.778 | 35 | Prefrontal Cortex/pathology | 88.000 | 3.268 | 76.087 | |
22 | Psychotic Disorders/pathology | 176.000 | 2.386 | 72.917 | 33 | Hippocampus/pathology | 78.000 | 1.446 | 70.000 | |
20 | Gray Matter/pathology | 168.000 | 4.280 | 79.545 | 36 | Schizophrenia/metabolism | 48.000 | 1.257 | 68.627 |
“Rank of MeSH terms” represents the number of high-frequency major MeSH terms/MeSH subheadings in each period as shown in
SNA for high-frequency major MeSH terms/MeSH subheadings applied to MRI studies on schizophrenia.
In the period of 2004–2008, eight major MeSH terms/MeSH subheadings were shown to have a high degree centrality (greater than the mean value of 203.69,
Compared with the SNA of the first period, another four new major MeSH terms/MeSH subheadings (
In the SNA of 2014–2018, five new major MeSH terms/MeSH subheadings were added to the nodes (
The human brain is a complex, efficient, and organic system with nearly 100 billion neurons, but during its process of development, a range of genetic, infectious, stress, family, and social environment factors can cause structural and functional abnormalities (
In the first period (2004–2008), theme Cluster 0 and Cluster 1 were located in Quadrant I, representing well-developed and mature topics. From the literature analysis, we found that previous research clarified the relationship between brain structural changes and functional abnormalities and analyzed the structural deficit in the corticothalamic system (
Another theme (Cluster 2) located in Quadrant I. indicated that research on the analysis of metabolism, therapeutic efficacy of antipsychotic agents on brain structure and schizophrenia pathology was immature and needed further study. Previous reports had suggested that after controlling for confounding factors such as sex and age, antipsychotics could alleviate or treat clinical symptoms by altering brain structure (
During the second period of 2009–2013, partial theme contents of Cluster 1 (research on brain structure and function) were still located in Quadrant I, indicating that researchers kept on paying close attention to the related research topic. With the exception of in-depth studies on nerve fibers, hippocampus, and cerebral cortex (
In the third period of 2014–2018, Cluster 1, whose theme contents involve research on differences in the neurobiological basis of schizophrenia and other mental diseases and include studies of the effects of antipsychotics on brain structural changes, was located in Quadrant III. The theme of differential diagnosis of schizophrenia and other mental disorder (e.g., bipolar disorder, schizoaffective disorder), which was originally in Quadrant Iin the second period (2009–2013), is now situated in Quadrant III during the third period (2014-2018). Based on MRI techniques, researchers have studied the similarities and differences in brain structure among patients with schizophrenia and bipolar disorder, such as the two major psychotic disorder have a shared white-matter dysconnectivity in callosal, paralimbic, and fronto-occipital regions (
In addition, as mentioned above, reports about the use of antipsychotics to prevent the progression of brain damage are as yet inconsistent. Numerous studies have found that changes in brain imaging occurred at an early stage in schizophrenia, while structural and functional abnormalities in brain regions caused by grey and white matter volume reductions and anomalous connections, became more prominent with the progression of the condition. But Emsley et al. argued that the brain volume reductions are not directly associated with antipsychotic treatment during the first year of medication, and decrease in brain volume may have more to do with neurotoxicity of drugs than with the therapeutic effect (
Finally, we can also conclude that the MeSH terms Schizophrenia/physiopathology, Schizophrenia/pathology, and Schizophrenia Psychology have higher or the highest values of betweenness centrality, implying that they have the largest number of direct connections with other nodes, as well as being situated at the core position within the networks. In other words, research on the pathological and physio-pathological mechanisms of schizophrenia, as well as analysis of abnormal psychological and behavioral symptoms from the perspective of brain imaging, are significant and potentially important academic issues in this research field. In addition, the new emerging hotspots in these three periods should be viewed as a guide to finding new directions for research.
The main finding of this study was that MRI studies on schizophrenia have been an issue of general concern, but the progress (e.g. pathogenesis of schizophrenia) is relatively slow in recent years. Further research is necessary to investigate the undeveloped, immature, and emerging hotspots and theme clusters mentioned above so as to provide medical staff, scientific researchers, and frontline educators with new directions in the field of MRI studies on schizophrenia.
There are several limitations to consider in the current study. To better present results, years were selected as the time unit to divide time periods and complete statistics. However, the complete data of 2019 has not yet been obtained, so the literature published in this year was not included. In addition, this study only included journals articles, so some research hotspots might have been omitted due to the exclusion of conference papers, reviews, and other types of literature. In addition, there are certain limitations in the scope of the PubMed database collection and in the indexing of MeSH terms, so that literature retrieval based on MeSH terms may miss some publications. Furthermore, this study selected the index of major MeSH terms/MeSH subheadings to extract high-frequency MeSH terms, and we cannot completely exclude the possibility that other low-frequency MeSH terms may become hotspots of research in the future. Finally, the data source only selects the published literatures, which may lead to publication bias.
GZ designed and corrected the paper. LD wrote the paper.
This work was supported by grants from the Major Project of the Department of Science & Technology of Liaoning Province (2019JH8/10300019).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.