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Keskin-Gokcelli D, Kizilates-Evin G, Eroglu-Koc S, Oguz K, Eraslan C, Kitis O, Gonul AS. The effect of emotional faces on reward-related probability learning in depressed patients. J Affect Disord 2024; 351:184-193. [PMID: 38286231 DOI: 10.1016/j.jad.2024.01.247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/30/2023] [Accepted: 01/26/2024] [Indexed: 01/31/2024]
Abstract
BACKGROUND Existing research indicates that individuals with Major Depressive Disorder (MDD) exhibit a bias toward salient negative stimuli. However, the impact of such biased stimuli on concurrent cognitive and affective processes in individuals with depression remains inadequately understood. This study aimed to investigate the effects of salient environmental stimuli, specifically emotional faces, on reward-associated processes in MDD. METHODS Thirty-three patients with recurrent MDD and thirty-two healthy controls (HC) matched for age, sex, and education were included in the study. We used a reward-related associative learning (RRAL) task primed with emotional (happy, sad, neutral) faces to investigate the effect of salient stimuli on reward-related learning and decision-making in functional magnetic resonance imaging (fMRI). Participants were instructed to ignore emotional faces during the task. The fMRI data were analyzed using a full-factorial general linear model (GLM) in Statistical Parametric Mapping (SPM12). RESULTS In depressed patients, cues primed with sad faces were associated with reduced amygdala activation. However, both HC and MDD group exhibited reduced ventral striatal activity while learning reward-related cues and receiving rewards. LIMITATIONS The patients'medication usage was not standardized. CONCLUSIONS This study underscores the functional alteration of the amygdala in response to cognitive tasks presented with negative emotionally salient stimuli in the environment of MDD patients. The observed alterations in amygdala activity suggest potential interconnected effects with other regions of the prefrontal cortex. Understanding the intricate neural connections and their disruptions in depression is crucial for unraveling the complex pathophysiology of the disorder.
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Affiliation(s)
- Duygu Keskin-Gokcelli
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital, RWTH Aachen, Aachen, Germany
| | - Gozde Kizilates-Evin
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, Istanbul, Turkey
| | - Seda Eroglu-Koc
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, Turkey
| | - Kaya Oguz
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Computer Engineering, Izmir University of Economics, Izmir, Turkey
| | - Cenk Eraslan
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Radiology, School of Medicine, Ege University, Izmir, Turkey
| | - Omer Kitis
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Radiology, School of Medicine, Ege University, Izmir, Turkey
| | - Ali Saffet Gonul
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; Department of Psychiatry and Behavioral Sciences, Mercer School of Medicine, Mercer University, Macon, GA, USA.
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Zhao Y, Chang C, Zhang J, Zhang Z. Genetic underpinnings of brain structural connectome for young adults. J Am Stat Assoc 2023; 118:1473-1487. [PMID: 37982009 PMCID: PMC10655950 DOI: 10.1080/01621459.2022.2156349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University
| | - Changgee Chang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Jingwen Zhang
- Department of Biostatistics, Boston University, Boston, MA
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill
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Lyu Z, Xia D, Zhang Y. Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition. J Comput Graph Stat 2023. [DOI: 10.1080/10618600.2022.2164289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Zhongyuan Lyu
- Department of Mathematics, Hong Kong University of Science and Technology
| | - Dong Xia
- Department of Mathematics, Hong Kong University of Science and Technology
| | - Yuan Zhang
- Department of Statistics, Ohio State University
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Dey P, Zhang Z, Dunson DB. Outlier detection for multi-network data. Bioinformatics 2022; 38:4011-4018. [PMID: 35762974 PMCID: PMC9890313 DOI: 10.1093/bioinformatics/btac431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/21/2022] [Accepted: 06/27/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically expressed as adjacency matrices, with each cell containing a summary of connectivity between a pair of brain regions. There is an emerging statistical literature describing methods for the analysis of such multi-network data in which nodes are common across networks but the edges vary. However, there has been essentially no consideration of the important problem of outlier detection. In particular, for certain subjects, the neuroimaging data are so poor quality that the network cannot be reliably reconstructed. For such subjects, the resulting adjacency matrix may be mostly zero or exhibit a bizarre pattern not consistent with a functioning brain. These outlying networks may serve as influential points, contaminating subsequent statistical analyses. We propose a simple Outlier DetectIon for Networks (ODIN) method relying on an influence measure under a hierarchical generalized linear model for the adjacency matrices. An efficient computational algorithm is described, and ODIN is illustrated through simulations and an application to data from the UK Biobank. RESULTS ODIN was successful in identifying moderate to extreme outliers. Removing such outliers can significantly change inferences in downstream applications. AVAILABILITY AND IMPLEMENTATION ODIN has been implemented in both Python and R and these implementations along with other code are publicly available at github.com/pritamdey/ODIN-python and github.com/pritamdey/ODIN-r, respectively. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pritam Dey
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
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Zhang J, Sun WW, Li L. Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies. J Comput Graph Stat 2022; 32:252-262. [PMID: 36970553 PMCID: PMC10035565 DOI: 10.1080/10618600.2022.2074434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 04/23/2022] [Indexed: 10/18/2022]
Abstract
Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the non-asymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies.
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Affiliation(s)
- Jingfei Zhang
- Department of Management Science, Miami Herbert Business School, University of Miami, Miami, FL, 33146
| | - Will Wei Sun
- Krannert School of Management, Purdue University, West Lafayette, IN, 47906
| | - Lexin Li
- Department of Biostatistics and Epidemiology, School of Public Health, University of California at Berkeley, Berkeley, CA, 94720
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Cai JF, Li J, Xia D. Generalized Low-rank plus Sparse Tensor Estimation by Fast Riemannian Optimization. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2063131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Jian-Feng Cai
- Department of Mathematics, Hong Kong University of Science and Technology
| | - Jingyang Li
- Department of Mathematics, Hong Kong University of Science and Technology
| | - Dong Xia
- Department of Mathematics, Hong Kong University of Science and Technology
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Young JG, Kirkley A, Newman MEJ. Clustering of heterogeneous populations of networks. Phys Rev E 2022; 105:014312. [PMID: 35193232 DOI: 10.1103/physreve.105.014312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
Statistical methods for reconstructing networks from repeated measurements typically assume that all measurements are generated from the same underlying network structure. This need not be the case, however. People's social networks might be different on weekdays and weekends, for instance. Brain networks may differ between healthy patients and those with dementia or other conditions. Here we describe a Bayesian analysis framework for such data that allows for the fact that network measurements may be reflective of multiple possible structures. We define a finite mixture model of the measurement process and derive a Gibbs sampling procedure that samples exactly from the full posterior distribution of model parameters. The end result is a clustering of the measured networks into groups with similar structure. We demonstrate the method on both real and synthetic network populations.
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Affiliation(s)
- Jean-Gabriel Young
- Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
| | - Alec Kirkley
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
- School of Data Science, City University of Hong Kong, 999077, Hong Kong
| | - M E J Newman
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA
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Macdonald PW, Levina E, Zhu J. Latent space models for multiplex networks with shared structure. Biometrika 2021. [DOI: 10.1093/biomet/asab058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Summary
Latent space models are frequently used for modelling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well-developed for more complex network structures, which are becoming increasingly common in practice. Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set. Multiplex networks can represent a network sample with shared node labels, a network evolving over time, or a network with multiple types of edges. The key feature of our model is that it learns from data how much of the network structure is shared between layers and pools information across layers as appropriate. We establish identifiability, develop a fitting procedure using convex optimization in combination with a nuclear norm penalty, and prove a guarantee of recovery for the latent positions as long as there is sufficient separation between the shared and the individual latent subspaces. We compare the model to competing methods in the literature on simulated networks and on a multiplex network describing the worldwide trade of agricultural products.
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Affiliation(s)
- P W Macdonald
- Department of Statistics, University of Michigan, 1085 South University, Ann Arbor, Michigan 48109-1107, U.S.A
| | - E Levina
- Department of Statistics, University of Michigan, 1085 South University, Ann Arbor, Michigan 48109-1107, U.S.A
| | - J Zhu
- Department of Statistics, University of Michigan, 1085 South University, Ann Arbor, Michigan 48109-1107, U.S.A
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Maugis PAG, Olhede SC, Priebe CE, Wolfe PJ. Testing for Equivalence of Network Distribution Using Subgraph Counts. J Comput Graph Stat 2020. [DOI: 10.1080/10618600.2020.1736085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- P.-A. G. Maugis
- Department of Statistical Science, University College London, and Pivitar, London, UK
| | - S. C. Olhede
- School of Basic Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - C. E. Priebe
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD
| | - P. J. Wolfe
- Department of Statistics, Purdue University, West Lafayette, IN
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