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Stoyanov D, Khorev V, Paunova R, Kandilarova S, Kurkin S, Calhoun VD. Group independent components underpin responses to items from a depression scale. Acta Neuropsychiatr 2024; 36:9-16. [PMID: 37088536 DOI: 10.1017/neu.2023.22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
OBJECTIVE The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales. METHODS In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. RESULTS The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC. CONCLUSION Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Rossitsa Paunova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS), The Georgia State University/Georgia Institute of Technology/Emory University, Atlanta, GA, USA
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de Sousa TR, Dt C, Novais F. Exploring the Hypothesis of a Schizophrenia and Bipolar Disorder Continuum: Biological, Genetic and Pharmacologic Data. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:161-171. [PMID: 34477537 DOI: 10.2174/1871527320666210902164235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/19/2021] [Accepted: 08/08/2021] [Indexed: 12/16/2022]
Abstract
Present time nosology has its roots in Kraepelin's demarcation of schizophrenia and bipolar disorder. However, accumulating evidence has shed light on several commonalities between the two disorders, and some authors have advocated for the consideration of a disease continuum. Here, we review previous genetic, biological and pharmacological findings that provide the basis for this conceptualization. There is a cross-disease heritability, and they share single-nucleotide polymorphisms in some common genes. EEG and imaging patterns have a number of similarities, namely reduced white matter integrity and abnormal connectivity. Dopamine, serotonin, GABA and glutamate systems have dysfunctional features, some of which are identical among the disorders. Finally, cellular calcium regulation and mitochondrial function are, also, impaired in the two.
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Affiliation(s)
- Teresa Reynolds de Sousa
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
| | - Correia Dt
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
| | - Filipa Novais
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
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3
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Yan R, Geng JT, Huang YH, Zou HW, Wang XM, Xia Y, Zhao S, Chen ZL, Zhou H, Chen Y, Yao ZJ, Shi JB, Lu Q. Aberrant functional connectivity in insular subregions in somatic depression: a resting-state fMRI study. BMC Psychiatry 2022; 22:146. [PMID: 35209866 PMCID: PMC8867834 DOI: 10.1186/s12888-022-03795-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/17/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Somatic depression (SD) is different from non-somatic depression (NSD), and insular subregions have been associated with somatic symptoms. However, the pattern of damage in the insular subregions in SD remains unclear. The aim of this study was to use functional connectivity (FC) analyses to explore the bilateral ventral anterior insula (vAI), bilateral dorsal anterior insula (dAI), and bilateral posterior insula (PI) brain circuits in SD patients. METHODS The study included 28 SD patients, 30 NSD patients, and 30 matched healthy control (HC) subjects. All participants underwent 3.0 T resting state functional magnetic resonance imaging. FC analyses were used to explore synchronization between insular subregions and the whole brain in the context of depression with somatic symptoms. Pearson correlation analyses were performed to assess relationships between FC values in brain regions showing significant differences and the total and factor scores on the 17-item Hamilton Rating Scale for Depression (HAMD17). RESULTS Compared with the NSD group, the SD group showed significantly decreased FC between the left vAI and the right rectus gyrus, right fusiform gyrus, and right angular gyrus; between the right vAI and the right middle cingulate cortex, right precuneus, and right superior frontal gyrus; between the left dAI and the left fusiform gyrus; and between the right dAI and the left postcentral gyrus. Relative to the NSD group, the SD group exhibited increased FC between the left dAI and the left fusiform gyrus. There were no differences in FC between bilateral PI and any brain regions among the SD, NSD, and HC groups. Within the SD group, FC values between the left vAI and right rectus gyrus were positively correlated with cognitive impairment scores on the HAMD17; FC values between the right vAI and right superior frontal gyrus were positively related to the total scores and cognitive impairment scores on the HAMD17 (p < 0.05, uncorrected). CONCLUSIONS Aberrant FC between the anterior insula and the frontal and limbic cortices may be one possible mechanism underlying SD.
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Affiliation(s)
- Rui Yan
- Nanjing Brain Hospital, Medical School, Nanjing University, 22 Hankou Road, Nanjing, 210093, China
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Ji Ting Geng
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Hong Huang
- Nanjing Brain Hospital, Medical School, Nanjing University, 22 Hankou Road, Nanjing, 210093, China
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Hao Wen Zou
- Nanjing Brain Hospital, Medical School, Nanjing University, 22 Hankou Road, Nanjing, 210093, China
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Xu Miao Wang
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Yi Xia
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Shuai Zhao
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Zhi Lu Chen
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Hongliang Zhou
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Yu Chen
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China
| | - Zhi Jian Yao
- Nanjing Brain Hospital, Medical School, Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China.
- School of Biological Sciences and Medical Engineering, Southeast University, No. 2 sipailou, Nanjing, 210096, China.
| | - Jia Bo Shi
- Department of psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, No. 2 sipailou, Nanjing, 210096, China.
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
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Stoyanov D. Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later. Front Psychiatry 2022; 13:999680. [PMID: 36304557 PMCID: PMC9595022 DOI: 10.3389/fpsyt.2022.999680] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022] Open
Abstract
Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at "bridging" the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology and Research Institute, Plovdiv Medical University, Plovdiv, Bulgaria
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5
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Stoyanov D, Aryutova K, Kandilarova S, Paunova R, Arabadzhiev Z, Todeva-Radneva A, Kostianev S, Borgwardt S. Diagnostic Task Specific Activations in Functional MRI and Aberrant Connectivity of Insula with Middle Frontal Gyrus Can Inform the Differential Diagnosis of Psychosis. Diagnostics (Basel) 2021; 11:95. [PMID: 33435624 PMCID: PMC7827259 DOI: 10.3390/diagnostics11010095] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia-SCH-and second group with a depressive episode in the context of major depressive disorder or bipolar disorder-DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences-resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group-the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Zlatoslav Arabadzhiev
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria; (K.A.); (S.K.); (R.P.); (Z.A.); (A.T.-R.)
| | - Stefan Kostianev
- Department of Pathophysiology, and Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Stefan Borgwardt
- Klinik für Psychiatrie und Psychotherapie, Universität zu Lübeck, 23538 Lübeck, Germany;
- Department of Psychiatry, University of Basel, 4001 Basel, Switzerland
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6
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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