| BackgroundThe categories of psychiatric disorders are currently grouped by a wide range of phenomenological diagnostic criteria.Researchers hope to achieve two goals by studying the biological mechanisms behind the complex clinical phenotypes of psychiatric disorders: First,understand the common mechanisms among different psychiatric disorders,and then integrate the classification system;Second,searching for precise classification of subtypes of heterogeneous psychiatric disorders such as Major Depressive Disorder(MDD),Bipolar Disorder(BD)and Schizophrenia(SZ).The study of the biological mechanism of psychiatric disorders often chooses their core symptoms or predominant clinical phenotypes as the starting point.Among a large number of symptoms,only the core symptoms carry certain degree of specificity.As a representative of the field,the National Institute of Mental Health has proposed Research Domain Criteria(RDoC),which emphasizing the effort to analyze multi-dimensional mechanism interpretations for the core clinical phenotypes of psychiatric disorders to support the current diagnostic systems.A similar view was expressed in Lancet’s 2016 discussion series on the diagnosis of psychiatric disorders: the study of heterogeneous psychiatric disorders such as MDD,BD and SZ,focusing on its core symptoms is helpful in enhancing evidence for disease diagnosis.In addition,core symptoms would still be critical to assist in the subdivision of disease subtypes.With the advancement of the above research,it was gradually realized that there was a pathway consisting of "genes,brains and clinical phenotypes" in psychiatric disorders.Imaging genetics(IG)had received attention as a way of integrating genetic information and neuroimaging.In genetic studies of psychiatric disorders,the corresponding imaging changes,often referred to as intermediate phenotypes(IP),were found in the genetically affected brain imaging mediator phenotypes that may be relevant to the classification of psychiatric disorders.It may help to achieve the two ultimate goals listed above.The concept of intermediary phenotype and the methodological integration of IG provide a more reliable way of thinking and analytical methods for the study of the mechanism of psychiatric disorders.However,there are still some difficulties in this integrated analysis including multiple modalities of clinical assessments,brain imaging and genetic information.At the same time,the method of the brain network revealing and aggregating a large amount of genetic information on the brain network still needs to be explored.More importantly,the stability and repeatability of the such research is critical.In our previous exploratory study looking for a potential IG intermediated phenotype,and we focused on patients with remitted geriatric depression(RGD).RGD patients displayed widespread cognitive disorders which were separated from mood disorders.Using a univariate analysis method based on prior knowledge,we tried to extract the brain network changes behind its cognitive impairments.Then,effort was made to explore the effects of brain-derived neurotrophic factor(BDNF)on these brain networks and evaluate whether they can serve as the potential IG intermediated phenotype.We found that this exploratory study helped revealing the brain network mechanism behind RGD cognitive impairment to some extent.However,due to the overlap of RGD cognitive impairment and other cognitive disorders in the geriatric population;relatively minor changes in the brain network;the single BDNF polymorphism showing minor effect on the brain network.These results were not entirely sufficient to establish a reliable IG intermediated phenotype.In order to further explore and answer the above issues,it’s critical to make further advance through improving clinical phenotypic specificity,enhancing the stability of brain network construction,and aggregate the impact of a large amount of genetic information on the brain network to fullfil the limitations of previous exploratory research.This research will:First,we moved the focus to more specific symptoms as anhedonia in MDD and stable network in brain gray matter.Using a priori knowledge-free,data-driven multivariateanalysis method,we aimed to explore the gray matter network alteration mechanism behind the MDD anhedonia and tried to overcome the limitations of the univariate analysis method based on prior knowledge.Later this method would be extended to multimodalities to assess repeatability;Second,in another group of MDD patients collected in different centers,the above results were projected.The stability and reproducibility of the anhedonia gray matter network were examined between the data of two different centers.At the same time,the genetic modality was included to observe the influence of large amount genetic information on the gray matter network and the relationship with the anhedonia.Part 1 Multivariate data driven analysis of gray matter network on anhedonia indepression 1.Source based morphometry(SBM)analysis of gray matter network behind anhedonia in depression.Objective: Through a preliminary study of the RGD cognitive impairment and brain network,we realized that in order to reveal a reliable intermediary phenotype,the core symptoms of mental disorders must be prioritized as an entry point to avoid overlap,and a more optimized brain network construction method is needed.In this study,we studied the network mechanism behind anhedonia through source-based morphometry(SBM)analysis.Methods: 41 patients with previous diagnosis of MDD,who had stable symptoms but had a complaint about anhedonia.They went through demographic information collection,a Temporal Experience of Pleasure Scale(TEPS)assessment,and a structural magnetic resonance imaging(sMRI)scan.After quality control,38 patients entered SBM,and extracted the load coefficients of all acquired brain networks,and correlated with the scores and total scores of the subtle subscales.Results: After correlation analysis with TEPS subscale scores and total scores,it was finally identified that three gray matter networks were significantly associated with anhedonia:1)anterior cingulate-frontal-orbitofrontal network and TEPS consummatory anhedonia(TEPS-CON)(Spearman correlation,r=0.372,P=0.021);2)cerebellar network and TEPS-CON(Spearman correlation,r=0.336,P=0.039);3)frontal-parietal network with TEPS total score(Spearman correlation,r =0.331,P =0.043).Conclusions: In this study,we found that the presence of MDD in the anterior cingulate-frontal-orbitofrontal network,cerebellar network,and frontal-parietal network are closely related to the symptoms of anhedonia.The more severe the gray matter damage of these networks,the more severe the symptoms of anhedonia.However,this study failed to reflect the subcortical regions that may be associated with anhedonia,and will explore the reproducibility of these results in further research.2.Parallel independent component analysis of anhedonia in depression.Objective: Through SBM analysis,we found that in the MDD,the anterior cingulate-frontal-orbitofrontal network,cerebellar network,and frontal-parietal network were closely related to the anhedonia.In this chapter,we extended themultivariate data-driven analysis method from brain imaging modality to brain imaging modality and clinical evaluation modality at same time.Parallel independent component analysis(pICA)with and without reference would be performed to re-extracts the gray matter network associated with anhedonia.Then we compared it with the single-modality SBM results to examine the repeatability of the results.Methods: The objects included were same as the first chapter.The TEPS scale was transformed into a series of sequences in the order of TEPS-anticipatory(TEPS-ANT)and TEPS-CON subscale meet the needs of pICA analysis.At the same time,and two reference systems were established based on TEPS-ANT and TEPS-CON.Results: 1)The non-reference pICA results showed the gray matter network continued to cover the frontal lobe(middle and inferior frontal gyrus),orbitofrontal cortex and part of parietal lobe.The 7th and 13 th questions of TEPS contributed less to this model,and the contribution of other items to this component was relatively average reflecting the overall situation of lack of pleasure.There was a significant positive correlation between the two modes(Mixed coefficient,r=0.6213,P= 0.00003),the significance of which can be corrected by multiple comparisons(0.05/25/3 = 0.00067).2)There was no significant difference between the two modes using the TEPS-ANT reference system.3)Using the TEPS-CON reference,the two modes of final convergence were almost identical to those of the non-reference pICA,and the two modalities were also positively correlated(Mixed coefficient,r=0.6269,P =0.000024).Conclusions: pICA still suggested the gray matter network associated with anhedonia is at the core of the frontal-orbitofrontal lobe.At the same time,the results were not affected by the TEPS reference frame,and the pICA analysis results of the TEPS-CON reference system were almost identical to those of the non-reference system,and the TEPS-ANT reference system results were not significant between the two modes.This result was also similar to the SBM analysis results(SBM only found the gray matter network was related to the TEPS total score and TEPS-CON,and hadnothing to do with TEPS-ANT).The stability of the pICA analysis was reflected,and most of the analysis results before SBM were repeated.Part 2 The projection and imaging genetics study of gray matter network behind anhedonia in depression3.The projection of gray matter network behind anhedonia in depressionObjective: In this chapter,we used a distinct set of MDD data from different center to conduct cross dataset multivariate data-driven analysis to investigate the stability and reproducibility.First,the relationship between the projection of the anterior cingulate-frontal-orbitofrontal network,the cerebellum network,and the frontal-parietal network on new data was studied.Then,pICA analysis was again performed in the new data to observe the changes in the network anhedonia.Methods: The MDD group was recruited from Mental Hospital of Henan Province.The normal control(NC)was derived from healthy volunteers and people in the working group.All personnel were clinically evaluated,assessed for anhedonia,sMRI scans and genetic information collection.After quality control,there were 71 MDD patients with demographic data,completing TEPS,and available gray matter images.Through the pinv,intersect and other matrix calculation functions,the gray matter network projection was obtained.The correlation analysis between the loading coefficients and TEPS were extracted.At the same time,the same parameters were used to perform the pICA analysis between the TEPS and the gray matter brain map again in this dataset.Results: 1)The projection of the anterior cingulate-frontal-orbitofrontal network,the cerebellum network,and the frontal-parietal network on new MDD dataset: the projection of the three gray matter networks was not significantly correlated with the anhedonia.However,it was associated with Hamilton’s 24 Depression Rating Scale24-Item(HAMD-24).The anterior cingulate-frontal-orbitofrontal network projection load factor was negatively correlated with the HAMD-24 score(Spearman correlation,r=-0.364,P=0.0018),and the cerebellar network projection was negatively correlated with the HAMD-24 score(Spearman correlation,r=-0.315,P=0.008).The parietal-frontal network projection load factor was not significantly correlated with HAMD-24,but it showed the trends of being significant(Spearman correlation,r=-0.217,P=0.071).2)Results of pICA analysis: In addition to the structure of frontal lobe,frontal lobe and cerebellum,the gray matter network also contains striatum(pallidum,putamen and caudate nucleus),and amygdala.In addition,the TEPS mode was very similar to the corresponding component in the first part of the pICA analysis,which showed that except for the 7th item other items contribute averagely to the component.There is a positive correlation between the two modes(Mixed coefficient,r=0.3724,P<0.00001),and the significance passed the correction threshold by multiple comparisons(0.05/25/3 = 0.00067).Conclusions: The anterior cingulate-frontal-orbitofrontal network,the cerebellum network,and the frontal-parietal network tended to reflect overall depression severity in new data.The frontal-orbitofrontal-cerebellum-striatum-amygdala network obtained by pICA analysis was repetitively amplified based on the first part of the study,and these structures were the core structures of anhedonia.The pICA analysis might better reflect the severity of anhedonia,and it was able to be replicated between different data.4.The imaging genetic study of gray matter network behind anhedonia in depressionObjective: Based on the findings in the third chapter,we would further examine the impact of genetic information on gray matter networks.We would attempt to studythrough the Ingenuity Pathway Analysis(IPA)and genetic association analysis techniques to establish appropriate genetic modality which could be analyzed by pICA.In addition,we aimed to compare the final constrained gray matter network affected by genetic modalities to suggest its overlap with the anhedonia network.Methods: The study object was the same as the third chapter.At the same time,the quality control of genetic data and image data,this study only included 45 MDD and 38 healthy controls.First,the most significant first five pathways were extracted by IPA analysis to investigate the SNP coverage in real data.If the coverage was low,the association analysis between groups would be performed to extract the most significant 100-200 SNPs to form the genetic modality;The pICA analysis was performed between the genetic modality and the gray matter brain map;whether the final convergence of the gray matter network overlaps with the previously discovered anhedonia gray matter network,and the potential impact of the genetic information on the network would be considered.Results: 1)IPA found CREB signaling in neurons,g-protein coupled receptor signaling pathway,opioid receptor signaling pathway,role of NFAT in cardiac hypertrophy,cardiac hypertrophy signaling showed the highest overlap with our dataset.However in the real data,the coverage of SNPs were too low to meet the needs of pICA;2)genetic association analysis finally extracted five genes including LAMA2,LA2G4 C,CACNA1C,KCNK10,PSEN2 who were most significant between groups,and a total of 132 SNPs formed the genetic modality;3)pICA analysis between genetic modality and gray matter map: the final convergent gray matter network includes anterior cingulate gyrus,orbitofrontal cortex,frontal lobes,cerebellum,striatum,globus pallidus,putamen and caudate nucleus,and thalamus.12 SNP as rs1015846 and etc,from CACNA1 C and LAMA2 contributed more to the genetic effects on the gray matter network.There was a positive correlation between the two modalities(Mixed coefficient,r=0.4910,P<0.00001),and the significance passed the correction threshold by multiple comparisons(0.05/25/8 = 0.00025).Conclusions: The gray matter network coupled with selected genetic information contained a large number of cortical and subcortical structures associated with anhedonia.The network formed by these structures repeatedly appeared in the above studies and had been verified by different multivariate data-driven research methods;The presence of CACNA1 C was included in both neuronal cell membrane penetration factor pathway and opioid receptor signaling pathway.These two pathways were closely related to anhedonia.From another perspective,this anterior cingulate-frontalorbitofrontal-striatum-cerebellum gray matter network was represented as an imaging genetic IP of MDD anhedonia. |
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