Gcu Module 3 Pcn-805 Chronological Review of Literature
Introduction
Over the decades, enormous basic and clinical study efforts have led to many advances in the understanding of pain mechanism, and researchers have expanded their knowledge on the complex and multidimensional characteristics of pain.1–three In the early investigation of the brain mechanism of hurting, efforts have been made to discover a unmarried brain area responsible for pain perception, as in the other sensory modality of vision or hearing. However, it turned out that pain is multidimensional experience emerging from the integrated activity of the brain and there is no single region such as "primary pain cortex".
Numerous neuroimaging studies accept demonstrated that multiple brain regions are involved in various pain conditions. While several brain regions such equally the thalamus, insula, and ACC accept been consistently reported to be activated during acute nociception regardless of the blazon of baneful stimuli, encephalon activity patterns for chronic pain are rather heterogeneous inside and across dissimilar chronic pain weather condition. Yet, studies from both acute and chronic hurting have highlighted the emotional and cognitive aspects in pain perception regardless of the pain types.4–10 Furthermore, accumulated evidence has indicated interactions between mental disorders and astute/chronic hurting.11–15 Recently, a new perspective was suggested which states that pain perception is associated with the negative moods (eg, anxiety and depression) as a continuum of aversive behavioral learning.16
There are hundreds of thousands of accumulated articles well-nigh pain so far. Many researchers take reviewed the activity of diverse brain regions involved in diverse pain conditions to sympathize the brain mechanisms of pain perception. Notwithstanding, it is practically limited for the researchers to manually investigate a vast number of papers and draw quantitative results efficiently. Information technology also might exist possible to obtain biased results according to the researcher'due south background knowledge or inquiry interests. Recently, the literature mining arroyo has been actively practical in various biomedical fields to efficiently extract scientific knowledge from the accumulated data.17–25 Literature mining converts unstructured textual information into structured data to excerpt meaningful numeric information and discover patterns.26,27 The advantage of literature mining is that it can quickly analyze vast quantities of documents and mine the latent knowledge such equally the implicit relationships between the words past computing quantitative metrics, eg, the frequency of occurrence and co-occurrence between words.
In this written report, we aimed to quantitatively investigate how the neuroscience field adult over time in terms of its concept on how hurting is represented in the brain and compare the inquiry trends of pain with those of mental disorders through literature mining of accumulated published articles. First, the bibliographic information of 137,525 pain-relevant abstracts was retrieved from PubMed and and then preprocessed. The brain regions were automatically recognized from the abstracts. Later, nosotros performed frequency and co-occurrence analyses to identify the temporal pattern of the occurrences of pain-related encephalon regions. Relative frequency patterns of pain-related brain regions were compared with those of mental disorders-related brain regions. Evolving occurrence patterns of the pain-related brain regions were investigated through the network analyses and the state-space model (Figure 1). Furthermore, time to come trends in the hurting written report were suggested based on the evolving patterns of the pain-related brain network.
 | Figure 1 Overview of construction and analysis of the evolving patterns of the pain-related brain network using literature mining. |
Materials And Methods
Datasets
Bibliographic Information Retrieval
We downloaded the abstracts and publication year information of articles from PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) using custom Python scripts and the Biopython Entrez module (http://biopython.org/DIST/docs/api/Bio.Entrez-module.html). "Pain[majr]" was used as the query to search manufactures whose MeSH Major Topics were classified equally pain in PubMed. Only the abstracts published until 2015 were retrieved because the MeSH Major Topics of the papers published after 2016 were not fully classified in PubMed at the time of analysis (on May 14th, 2018). We also retrieved bibliographic data of the articles related to the representative mental disorders: schizophrenia, depression, feet disorders, bipolar disorder, and post-traumatic stress disorders.
Compiling A Dictionary Of The Encephalon Regions
It is a challenge to recognize the nomenclature of brain regions from the unstructured texts considering they are not-standardized. To compile a lexicon of the brain regions, we downloaded the ontology of encephalon regions from the Brede database (http://neuro.imm.dtu.dk/wiki/Brede_Database/WOROI), which includes 586 macroscopic brain regions in humans. To consider synonyms, we used the Neuroscience Information Framework Standard Ontology (NIFSTD; https://bioportal.bioontology.org/ontologies/NIFSTD).
Recognition Of The Brain Regions In The Abstracts
Nosotros used Python's regular expression module to preprocess the abstracts and automatically recognize brain regions of the compiled dictionary in the abstracts using a rule-based approach. The rule was used to recognize the brain region term with spaces before and afterward it in the abstract. With this dominion, the occurrences of brain regions were computed in each abstract, and the occurrence matrix was constructed (Figure 2). Get-go, the queries consisted of the preferred characterization (the primary term of the brain region in NIFSTD) and its synonyms. The binary occurrence matrix was synthetic by recognizing all queries from the whole retrieved abstracts. As shown in Figure 2, each row represents the abstract; each column represents the brain region; and the element represents whether the given queries appear in the given abstract (value 1) or not (value 0).
 | Figure 2 Workflow of recognition of brain regions in the abstracts and construction of the occurrence matrix. Brain regions from the Brede database were listed. The query for the recognition consisted of synonyms of each brain region. The occurrence matrix was constructed past recognizing all queries in the abstracts. Each row represents the abstract; each column represents the brain region; and the element represents whether the given queries appear in the given abstract (value 1) or not (value 0). |
Determination Of The Pain-Related Brain Regions
To decide the pain-related brain regions, we computed the occurrence frequencies of the regions by summing columns in the occurrence matrix. We selected the encephalon regions that appeared more than 100 times as pain-related brain regions. Then, a curation process was followed to sort out duplicated terms and remove the terms that are likewise broad (eg, brain, cerebral cortex, and white matter) or non-localizable (eg, cerebrospinal fluid).
Analysis Of The Relative Frequency Of Pain-Related Brain Regions
To place significant patterns of pain-related brain regions in each year, we computed the relative frequencies of the hurting-related brain regions as occurrence frequency divided by the number of abstracts including pain-related brain regions in the corresponding year.
In guild to cluster the brain regions according to the changing patterns of the relative frequency over fourth dimension, the offset derivatives of the relative frequency were calculated for encephalon regions and clustered into three groups by applying K-means clustering.
Co-Occurrence Matrix Construction And Network Analysis
In literature mining, words are considered correlated when they are collocated in a corpus. To investigate the change of region-to-region associations, we conducted co-occurrence analysis to quantify co-occurrences of the brain regions in the abstracts.
Showtime, the temporal patterns of co-occurrences between brain groups were identified. Pain-related brain regions were assigned to the following six groups: cortex region (CTX), limbic area (Limbic), diencephalon (DIEN), basal ganglia (BG), brain stem (BS), and cerebellum (Cb). The grouping co-occurrence frequencies were calculated in every yr of publication and were divided by the number of pairs constituting the group, thereby minimizing the bias due to the unlike number of regions between the groups. Considering that the full number of studies increases over the years, we normalized the group co-occurrence frequencies once again to the total co-occurrence frequencies of each twelvemonth.
We generated the annual co-occurrence matrices; their rows and columns betoken the pain-related brain regions, and each element contains the number of abstracts in which a pair of brain regions co-occur. To investigate the overall co-occurrence patterns of pain-related brain regions, we grouped annual co-occurrence matrices into the following four stages: phase ane (before 1986), stage 2 (1986–1995), phase iii (1996–2005), and phase four (2006–2015). Then, the elements of four co-occurrence matrices were divided by the total co-occurrence frequencies of the corresponding stage to identify the relatively important relationship of hurting-related encephalon regions at each phase.
After, hurting-related brain networks were synthetic to investigate the topology of the interconnected encephalon regions at each stage. Each network consisted of nodes (pain-related brain regions) and edges between them (co-occurrences). For the tractability of analysis, the edges of the networks were binarized (1, connected; 0, not continued) every bit follows. First, different co-occurrence thresholds were called in different networks to set the aforementioned edge density betwixt networks. Second, we eliminated the edges with co-occurrences of less than 5 to avoid detecting spurious relationships. Networks were visualized and analyzed using Cytoscape 3.5.1(http://www.cytoscape.org/).28
Implementation Of The State-Space Model
To compare the occurrence patterns of the brain regions in pain and mental disorders, the state-space model was implemented by applying the principal component analysis (PCA), a linear dimensionality reduction method. PCA finds a reduced set of new variables through a linear combination of initial variables while preserving the information every bit much equally possible (ie, maximizing the total variance of the original data).29 In other words, these reduced set of variables chosen principal components (PCs) stand for the management that covers a maximal corporeality of variance in the loftier-dimensional space. The obtained low-dimensional representation of the data is composed of a much smaller set of variables and thus can be easily visualized.
For the pain- or mental disorders-related encephalon regions that appeared more than 100 times in the abstracts of each topic, we assigned the regions into ten groups: PFC, frontal cortex (Frontal), parietal cortex (Parietal), temporal cortex (Temporal), Limbic, DIEN, BG, BS, Cb, and corpus callosum (Corpus). A relative frequency matrix, consisting of 41 rows (year of publication) and 10 columns (brain region groups), was obtained from each disorder. The data were scaled into unit length vectors by dividing them by the norm of each column to focus on the vector direction rather than the magnitude. After concatenating the matrices, PCA was applied to projection data resided in 10-dimensional space to three-dimensional space composed of PC1, PC2, and PC3. These projected information form the low-dimensional representation of each disorder which effectively summarizes the original data while minimizing the loss of data. In our study, these representations indicate the evolving trajectory of the relative frequency of the pain- and mental disorder-related brain regions so that we could intuitively capture the trend of how the occurrence patterns of brain regions take inverse over fourth dimension. Nosotros plotted the information points to the two- and three-dimensional space after smoothing with the Gaussian kernel (sigma = 2) to depict the trends effectively. Furthermore, we investigated the PCA coefficients of each brain region in given PCs to identify the contribution of each region in the moving direction of the trajectory.
Results
Different Occurrence Patterns Of Brain Regions Over Time
We identified the number of abstracts over time, along with the trends of the occurrence of the encephalon regions. A full of 137,525 hurting-related abstracts published until 2015 were institute in PubMed, and the number of abstracts substantially increased over time (Effigy 3A). We constructed a dictionary of brain regions consisting of 586 encephalon regions from the Brede database and their synonyms obtained from NIFSTD. As shown in Effigy 3B, the number of abstracts including encephalon regions also grew over time. However, different patterns were observed for the number of abstracts containing each brain region. For example, the number of abstracts containing PFC was few in the early years but increased from 1995, and the number of abstracts containing midbrain and striatum did not show steadily increasing patterns.
 | Figure 3 Changes in the number of pain-related abstracts over time. (A) The growth number of published pain-related abstracts retrieved from PubMed. (B) The total number of increasing abstracts containing 586 encephalon regions from the compiled dictionary (black line); examples of different patterns of the number of abstracts containing single brain region with dissimilar colors (dark-green line, thalamus; blue line, midbrain; ruby line, PFC; gray line, Striatum). Abridgement: PFC, prefrontal cortex. |
Identifying Clusters Of Pain-Related Brain Regions Co-ordinate To The Changing Patterns Over Fourth dimension
Nosotros defined 22 pain-related brain regions as those appeared more than 100 times in the retrieved abstracts (Table one). To investigate the chronological changes in the relative importance of pain-related brain regions, we calculated the kickoff derivatives of the relative frequency for encephalon regions and amassed them into three groups by applying Thou-means clustering. As shown in Figure 4, the three clustered groups represent a pattern in which the occurrence frequency is generally increased, relatively consequent, or generally decreased with time. And then, nosotros named each group as rising, consistent, and falling. PFC, anterior insula, dorsolateral PFC (DLPFC), central amygdaloid nucleus (CeA), hippocampus, motor cortex, Cb, Insula, ACC, somatosensory cortex, and amygdala were grouped into the rising cluster; midbrain, hypothalamus, periaqueductal gray (PAG), BS, and thalamus were grouped into the falling cluster; the rest were grouped into the consistent cluster.
 | Tabular array 1 Pain-Related Brain Regions And Their Occurrences In Pain-Related Abstracts |
 | Figure iv The relative frequency changes of hurting-related encephalon regions over time. (A) Relative frequency changes of 22 pain-related brain regions over time were represented every bit a color matrix. Color bar (upper right) indicates the relative frequency values (the number of abstracts containing each encephalon region divided past the total number of abstracts in each year). Note that brain regions were amassed by Thou-means clustering co-ordinate to the design of changes in relative frequencies (grouped into 3 clusters: ascension, falling, and consistent). (B) Relative frequencies of hurting-related encephalon regions were plotted against time in years. Each number label of the line corresponds to the label in the left matrix. A sliding window was used to shine the values of matrix and graph (window size = 10). |
Change In Co-Occurrence Patterns Between Hurting-Related Brain Regions
We next attempted to effigy out the changing blueprint of the inter-regional relationship through the co-occurrence analysis because it has been demonstrated in recent years that pain is an integrated activity betwixt multiple brain regions. Indeed, we plant that the number of abstracts with more than 2 brain regions increased over fourth dimension, while the number of abstracts with simply one brain region decreased (Figure 5).
 | Figure v The relative number of pain-related abstracts containing different numbers of pain-related brain regions. Scatterplots with regression lines show that more pain-related brain regions co-occur in the abstracts with time. Each bespeak with a different color represents the relative number of abstracts containing the indicated number of hurting-related encephalon regions in the year. The number of abstracts was normalized by the number of those containing at least one brain region for computing relative number of abstracts. |
Nosotros quantified relative co-occurrence changes between the groups of pain-related brain regions and plant that eight pairs of groups exhibited increasing or decreasing patterns (Figure 6). Pain-related brain regions were assigned to six groups as follows: (1) CTX group: PFC, DLPFC, motor cortex, frontal lobe, and somatosensory area; (2) Limbic grouping: ACC, insula, amygdala, hippocampus, CeA, anterior insula, and posterior insula; (3) DIEN group: thalamus and hypothalamus; four) BG group: striatum and Nac; five) BS grouping: brain stem, PAG, medulla oblongata, midbrain, and LC; six) Cb group: cerebellum. Limbic and CTX tended to increasingly co-occur with other groups (DIEN-Limbic, Limbic-Limbic, DIEN-CTX, Limbic-CTX, and CTX-CTX), while BS tended to co-occur less often with other groups (BS-BS and DIEN-BS) over time. Notably, although cerebellum showed depression levels of relative frequency in general, it showed increasing patterns both in relative frequency and co-occurrence analysis (Figures 4 and vi).
 | Figure 6 Relative co-occurrence changes of hurting-related brain region groups over time. Relative co-occurrence changes were plotted against published years. The sliding window method was used to smooth the graph (window size = 10). Abbreviations: CTX, cortex region; DIEN, diencephalon; Limbic, limbic area; BG, basal ganglia; BS, brain stem; Cb, Cerebellum. |
Structure Of Fourth dimension-Evolving Hurting-Related Brain Networks
To investigate the topology of interconnected pain-related encephalon regions, we constructed the chronological weighted co-occurrence matrices and binarized pain-related brain networks over iv stages (stage ane, before 1986; stage ii, 1986–1995; phase 3, 1996–2005; and stage iv, 2006–2015) (Figure 7). The binarized networks were constructed with an edge density of 0.09 for stages 2–four. In phase 1, the regions of BS group (medulla oblongata, PAG, and brain stalk) and DIEN grouping (thalamus) co-occurred prevalently while other pain-related brain regions neither frequently occurred nor formed edges in the network. In phase 2, limbic areas such as the hippocampus and amygdala showed not only a significantly increased relative frequency just also an increased clan with other regions including the hypothalamus, striatum, and PAG. Although the relative frequency was low, the somatosensory expanse was connected with the thalamus. In stage three, the network became extensive equally new connections between ACC, insula, thalamus, PFC, and somatosensory area emerged. The regions of the Limbic and CTX groups showed consistently high relative frequencies and tended to appear oft with other encephalon regions in stages 3 and four. The brain stalk still appeared at a high relative frequency, simply the occurrences of other areas in the BS group (medulla oblongata, PAG, midbrain, and LC) in phase iv decreased compared with those in other stages.
 | Figure vii Chronological weighted co-occurrence matrices (left) and brain networks related to pain (right). Weighted networks were converted into binary networks by applying thresholds for tractability of analyses (varying thresholds to set the border density abiding across networks: edge density = 0.09). In the visualized network (right), a node represents the pain-related brain region, and an border between ii nodes represents co-occurrence in abstracts. The node size is proportional to the relative frequency. |
To evaluate important regions on the network topology, we analyzed the caste (the number of edges) of nodes in the networks. The subnetworks of hub regions (divers equally the nodes with degrees ≥ 3) and not-hub regions continued to the hubs were represented for every stage (Effigy 8). PAG and thalamus had multiple edges in every stage. Midbrain, and medulla oblongata frequently co-occurred with other regions in stages 1 and ii, but not in stages 3 and 4. Co-occurrences of the encephalon stalk gradually decreased, and it was somewhen excluded from the hub regions in phase 4. On the other hand, ACC, insula, PFC, amygdala, and the somatosensory expanse that belonged to the rising cluster were found as the oft co-occurred regions in stages three and 4. Interestingly, we constitute that most of the regions of ascension cluster (somatosensory area, ACC, insula, amygdala, motor cortex, hippocampus, and PFC) except for the motor cortex, posterior insula, and DLPFC formed edges with other brain regions in at to the lowest degree one subnetwork.
 | Effigy 8 Subnetwork of hub encephalon regions (caste ≥ 3) from the pain-related brain networks in each phase. Nodes with blue labels represent hub encephalon regions; nodes with green labels represent non-hub brain regions. The node size is proportional to the degree, and the degree of the hub nodes is indicated in parentheses. |
Hub nodes tended to class edges between themselves but were occasionally continued past edges with non-hub nodes. We attempted to seek potentially important regions in the subnetwork past focusing on non-hub nodes in terms of network topology. Information technology was establish that most not-hubs in a certain stage appeared as hubs in other stages, implying that the regions of the subnetworks are more often than not homogeneous. However, we plant that the cerebellum that belonged to subnetworks every bit non-hubs in stage iii and 4 had never been a hub in all stages. This suggests a possibility of the cerebellum as a potential hub in the future.
Comparison Of Irresolute Patterns Of Encephalon Regions Between Pain And Mental Disorders In The State-Space
We found that the importance of the brain areas concerning the emotional and cognitive aspects of pain has increased over time. The country-space model was and then implemented for more than accurate assay to compare the relative frequency patterns of brain regions in pain and mental disorders. By visualizing the trajectory of pain-related and mental disorder-related brain regions in the low-dimensional space, nosotros could intuitively capture the occurrence patterns of the encephalon regions in hurting and each mental disorder.
Of the mental disorders considered (schizophrenia, depression, anxiety disorders, bipolar disorder, and post-traumatic stress disorders), only those with at least ten regions that appeared more than a hundred times past 2015 were included in the analysis: schizophrenia, anxiety disorder, and depression (Supplementary Table1). The ten-dimensional matrix equanimous of 41 years (row) and 10 brain region groups (columns) was reduced to a three-dimensional matrix (41 by 3) by applying PCA for hurting and mental disorders (Encounter methods for detail). Reduced data representing the changing patterns of the relative frequency of encephalon regions in pain and mental disorders were plotted in the ii- and 3-dimensional space composed of PC1 and PC2, and PC1, PC2, and PC3, respectively (Effigy ix). The three components (PC1, PC2, and PC3) explained 43%, 22%, and 9% variances in the dataset respectively. The relative frequency trajectory of encephalon regions related to hurting shifted closer to that of brain regions related to mental disorders over time, suggesting that encephalon regions emerging in the abstracts related to the mental disorders and hurting gradually became like. The trajectory of pain-related encephalon regions, in item, showed dramatic motion to a negative direction along the PC1 axis over time, thus approaching to the trajectory of mental disorder-related brain regions.
 | Figure 9 Land-space model of brain regions related to pain and mental disorders (schizophrenia, anxiety disorders, and depression). A 3-dimensional land-infinite model was implemented past applying PCA (left). The starting time two primary components were visualized in the two-dimensional space (right). The PC values of each disease were depicted as scatter plots of different colors. The gradient color changes stand for the dissimilar publication years of the abstracts equally described in the box. The scatter plots were smoothed using the Gaussian kernel (sigma = 2). |
To interpret the movement of trajectory along the PC1 axis, we investigated the coefficients of PC1. Using PCA coefficients, we could quantitatively investigate the influence of each encephalon region group on constructing the trajectory (Table 2). For case, BS and DIEN take large positive coefficients for PC1, which means these areas largely contribute to moving in a positive direction on the PC1 centrality. Therefore, the trajectory of pain-related brain regions shifting to a negative direction in the PC1 axis implies that portions of BS and DIEN in the relative frequency of hurting-related brain regions have decreased. In contrast, PFC, Limbic, and BG showed large negative coefficients for PC1, implying their increasing portions to the hurting-related brain regions over fourth dimension.
 | Table 2 Eigenvectors Of The Iii Principal Components |
Discussion
In this report, nosotros text-mined encephalon regions that were frequently mentioned in 137,525 pain-related abstracts in the PubMed database to quantitatively investigate how the neuroscientific field developed over time in terms of its concept on how pain is represented in the encephalon and compare the research trends of pain with those of mental disorders. We analyzed frequencies and co-occurrences of pain-related brain regions and compared the relative frequency patterns of pain-related brain regions with those of depression, anxiety disorders, and schizophrenia. Nosotros establish that the brain regions in the pain-related abstracts accept gradually extended, reflecting the changes of the perspective on pain from a simple modality of perception into a multidimensional experience. The relative frequency pattern of pain-related brain regions shifted closer to that of mental disorders-related brain regions in the country-space model.
The results of this report point that researchers have gradually come to focus on the emotional/cerebral aspects of pain rather than unproblematic pain perception and modulation. We found brain regions that are highly related to mental disorders (such as PFC, amygdala, insula, ACC, and hippocampus) have shown increased relative frequencies and co-occurrences over time.30 Similarly, there were mutual hurting-related encephalon regions that appeared more 100 times in the abstracts related to depression, anxiety disorders, and schizophrenia: PFC, DLPFC, frontal lobe, striatum, ACC, insula, hippocampus, and amygdala. These regions are known to exist associated with cognitive and emotional processing, and almost of them except frontal lobe and striatum belonged to the rise cluster on the relative frequency in our analyses.5,31,32 Recently, information technology has been suggested that hurting perception is related to negative moods (eg, feet and low) and is a continuum of aversive behavioral learning.sixteen The comorbidity of pain and mental disorders are mutual, and the correlation between pain and mental disorders has been widely investigated clinically.14,33–35 Especially, depression and anxiety disorders accept been demonstrated to be highly correlated with chronic pain based on the results of multi-population surveys.36
Meanwhile, there were brain areas that but appeared oftentimes in the hurting-related abstracts, and non in the mental disorders. The brain areas such equally PAG and motor cortex exclusively appeared at a high frequency in the pain-related abstracts. PAG is known to comprise the descending pathway of pain, which exerts influence upon a peak-down modulation of hurting sensation. It has been demonstrated that the descending pain modulatory circuit can facilitate as well as inhibit hurting and that dysfunction of this circuit may pb to the chronification of pain. Although PAG was clustered into a falling grouping in our study, information technology showed high levels of relative frequency in general, reflecting the importance of PAG in hurting studies.37–42
Interestingly, the advent patterns of the cerebellum and motor cortex were notable in pain-related abstracts. Motor cortex, which belongs to the rising cluster, showed a consistent increase in relative frequency from the tardily 1990s. While most of the regions in the rising cluster frequently co-occurred with other regions to form edges in the subnetwork of hub regions, the motor cortex formed no edges with other areas in the networks in the given threshold. The cerebellum did not show high levels of relative frequency in general; however, it formed edges with hub nodes (ACC and thalamus in stage 3; insula and thalamus in phase 4) in the hurting-related brain networks in the terminal two decades. This trend reflects the advanced understanding of pain in recent studies regarding the relationship betwixt motor control and pain.43–47 The emotional/cerebral encephalon regions that belonged to the rising cluster or were represented equally hubs in the networks accept been recognized as important in hurting studies in the last 2 decades. This information suggests that the motor cortex and cerebellum are besides likely to play a key role in hurting studies in the years to come.
Our results are supposed to be influenced not merely by the change in the perspective of the hurting machinery or the researchers' interests, simply likewise by the rapid developments in the encephalon imaging technologies such as positron emission tomography, computed tomography, and magnetic resonance imaging (MRI). In stage 3 (1996–2005), the configuration of the pain-related encephalon network changed abruptly from the earlier stages (Figure 7). These changes could partially be attributed to the fact that the portion of the man encephalon imaging studies in pain research has increased around that time. For instance, increase of cortical regions and subtract of brain stem regions in occurrence pattern could be partially explained by the emergence of functional MRI because information technology is difficult to obtain data from several complicated regions such as brain stem.48
It should be noted that although we draw the brain regions that appeared more than 100 times as hurting-related regions, pain-related brain regions are not pain-specific brain regions, ie, hurting coding regions. For instance, the insular cortex has been discovered to involve a wide range of functions in humans that encompasses sensory, emotional, and high-level cognition.eleven And there have been emerging studies showing that ACC is implicated both in cognitive and emotional processes.49
One limitation of the study should be noted. Because nosotros only dealt with the abstracts, non the unabridged manuscripts, the pain-related studies were included for assay without because pain types, subjects, and article types. There is a possibility of misinterpretation in the results which may effect from this unproblematic criterion. However, the macroscopic changes of pain-related brain regions, which were demonstrated by analyzing a vast number of abstracts, are apparently in line with our current understanding of the pain written report trends, supporting the validity of our approach.
In summary, we performed literature-mining analyses in the pain-related abstracts to investigate how the neuroscience field developed over time in terms of its concept on how pain is represented in the brain and compare the inquiry trends of pain with those of mental disorders, and the findings bespeak that the regions related to emotional/cerebral aspects of pain have go increasingly important. The relative frequency trajectory of pain-related brain regions has shifted closer to that of mental disorders-related brain regions. Furthermore, we besides conceptualize that the cerebellum and motor cortex will exist actively explored in the hurting study considering of their notable occurrence patterns. Nosotros expect that the literature mining arroyo used in this study tin exist applied to other study topics in the hereafter to provide macroscopic insights into the study trends.
Determination
Evolving patterns of the hurting-related encephalon network were examined by analyzing a vast number of abstracts that are impossible to be manually reviewed, and the results are in line with our current understanding of the pain report trends. Temporal changes of hurting-related encephalon regions in the abstracts signal that emotional/cognitive aspects of pain have been gradually emphasized. The relative frequencies and co-occurrences of encephalon regions related to the emotional/cognitive aspects of hurting tended to increment consistently. The state-space model showed that the relative frequency trajectory of the hurting-related brain regions shifted closer to that of mental disorders-related brain regions over time. Based on the notable occurrence patterns of the cerebellum and motor cortex, these motor-related areas are expected to exist actively explored by pain researchers in the future. We expect that the literature mining approach used in this report can be practical to other study topics in the future to provide macroscopic insights into the report trends.
Acknowledgments
This work was supported past the Gachon Academy research fund of 2016 (GCU-2016-0493).
Disclosure
The authors have declared that no competing interests exist in this piece of work.
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