Psychology Commons^™

4d Dynamic Spatial Brain Networks At Rest Linked To Cognition Show Atypical Variability And Coupling In Schizophrenia, Krishna Pusuluri, Zening Fu, Robyn Miller, Godfrey Pearlson, Peter Kochunov, Theo G M Van Erp, Armin Iraji, Vince D Calhoun

Student and Faculty Publications

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with …

Brain-Age Prediction: Systematic Evaluation Of Site Effects, And Sample Age Range And Size, Yuetong Yu, Hao-Qi Cui, Shalaila S Haas, Faye New, Nicole Sanford, Kevin Yu, Denghuang Zhan, Guoyuan Yang, Jia-Hong Gao, Dongtao Wei, Jiang Qiu, Nerisa Banaj, Dorret I Boomsma, Alan Breier, Henry Brodaty, Randy L Buckner, Jan K Buitelaar, Dara M Cannon, Xavier Caseras, Vincent P Clark, Patricia J Conrod, Fabrice Crivello, Eveline A Crone, Udo Dannlowski, Christopher G Davey, Lieuwe De Haan, Greig I De Zubicaray, Annabella Di Giorgio, Lukas Fisch, Simon E Fisher, Barbara Franke, David C Glahn, Dominik Grotegerd, Oliver Gruber, Raquel E Gur, Ruben C Gur, Tim Hahn, Ben J Harrison, Sean Hatton, Ian B Hickie, Hilleke E Hulshoff Pol, Alec J Jamieson, Terry L Jernigan, Jiyang Jiang, Andrew J Kalnin, Sim Kang, Nicole A Kochan, Anna Kraus, Jim Lagopoulos, Luisa Lazaro, Brenna C Mcdonald, Colm Mcdonald, Katie L Mcmahon, Benson Mwangi, Fabrizio Piras, Raul Rodriguez-Cruces, Jessica Royer, Perminder S Sachdev, Theodore D Satterthwaite, Andrew J Saykin, Gunter Schumann, Pierluigi Sevaggi, Jordan W Smoller, Jair C Soares, Gianfranco Spalletta, Christian K Tamnes, Julian N Trollor, Dennis Van't Ent, Daniela Vecchio, Henrik Walter, Yang Wang, Bernd Weber, Wei Wen, Lara M Wierenga, Steven C R Williams, Mon-Ju Wu, Giovana B Zunta-Soares, Boris Bernhardt, Paul Thompson, Sophia Frangou, Ruiyang Ge

Student and Faculty Publications

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the …

Brain-Age Prediction: Systematic Evaluation Of Site Effects, And Sample Age Range And Size, Yuetong Yu, Hao-Qi Cui, Shalaila S Haas, Faye New, Nicole Sanford, Kevin Yu, Denghuang Zhan, Guoyuan Yang, Jia-Hong Gao, Dongtao Wei, Jiang Qiu, Nerisa Banaj, Dorret I Boomsma, Alan Breier, Henry Brodaty, Randy L Buckner, Jan K Buitelaar, Dara M Cannon, Xavier Caseras, Vincent P Clark, Patricia J Conrod, Fabrice Crivello, Eveline A Crone, Udo Dannlowski, Christopher G Davey, Lieuwe De Haan, Greig I De Zubicaray, Annabella Di Giorgio, Lukas Fisch, Simon E Fisher, Barbara Franke, David C Glahn, Dominik Grotegerd, Oliver Gruber, Raquel E Gur, Ruben C Gur, Tim Hahn, Ben J Harrison, Sean Hatton, Ian B Hickie, Hilleke E Hulshoff Pol, Alec J Jamieson, Terry L Jernigan, Jiyang Jiang, Andrew J Kalnin, Sim Kang, Nicole A Kochan, Anna Kraus, Jim Lagopoulos, Luisa Lazaro, Brenna C Mcdonald, Colm Mcdonald, Katie L Mcmahon, Benson Mwangi, Fabrizio Piras, Raul Rodriguez-Cruces, Jessica Royer, Perminder S Sachdev, Theodore D Satterthwaite, Andrew J Saykin, Gunter Schumann, Pierluigi Sevaggi, Jordan W Smoller, Jair C Soares, Gianfranco Spalletta, Christian K Tamnes, Julian N Trollor, Dennis Van't Ent, Daniela Vecchio, Henrik Walter, Yang Wang, Bernd Weber, Wei Wen, Lara M Wierenga, Steven C R Williams, Mon-Ju Wu, Giovana B Zunta-Soares, Boris Bernhardt, Paul Thompson, Sophia Frangou, Ruiyang Ge, Enigma‐Lifespan Working Group

Student and Faculty Publications

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the …

Cross-Cohort Replicable Resting-State Functional Connectivity In Predicting Symptoms And Cognition Of Schizophrenia, Chunzhi Zhao, Rongtao Jiang, Juan Bustillo, Peter Kochunov, Jessica A Turner, Chuang Liang, Zening Fu, Daoqiang Zhang, Shile Qi, Vince D Calhoun

Student and Faculty Publications

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ …

Reciprocal Relationships Between Stress And Depressive Symptoms: The Essential Role Of The Nucleus Accumbens, Yizhou Ma, Peter Kochunov, Mark D Kvarta, Tara Legates, Bhim M Adhikari, Joshua Chiappelli, Andrew Van Der Vaart, Eric L Goldwaser, Heather Bruce, Kathryn S Hatch, Si Gao, Shuo Chen, Ann Summerfelt, Thomas E Nichols, L Elliot Hong

Student and Faculty Publications

BACKGROUND: Stress and depression have a reciprocal relationship, but the neural underpinnings of this reciprocity are unclear. We investigated neuroimaging phenotypes that facilitate the reciprocity between stress and depressive symptoms.

METHODS: In total, 22 195 participants (52.0% females) from the population-based UK Biobank study completed two visits (initial visit: 2006-2010, age = 55.0 ± 7.5 [40-70] years; second visit: 2014-2019; age = 62.7 ± 7.5 [44-80] years). Structural equation modeling was used to examine the longitudinal relationship between self-report stressful life events (SLEs) and depressive symptoms. Cross-sectional data were used to examine the overlap between neuroimaging correlates of SLEs and …

Multi-Site Benchmark Classification Of Major Depressive Disorder Using Machine Learning On Cortical And Subcortical Measures, Vladimir Belov, Tracy Erwin-Grabner, Moji Aghajani, Andre Aleman, Alyssa R Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Robin Bülow, Christopher R K Ching, Colm G Connolly, Kathryn Cullen, Christopher G Davey, Danai Dima, Annemiek Dols, Jennifer W Evans, Cynthia H Y Fu, Ali Saffet Gonul, Ian H Gotlib, Hans J Grabe, Nynke Groenewold, J Paul Hamilton, Ben J Harrison, Tiffany C Ho, Benson Mwangi, Natalia Jaworska, Neda Jahanshad, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Meng Li, David E J Linden, Frank P Macmaster, David M A Mehler, Elisa Melloni, Bryon A Mueller, Amar Ojha, Mardien L Oudega, Brenda W J H Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Matthew D Sacchet, Philipp G Sämann, Anouk Schrantee, Kang Sim, Jair C Soares, Dan J Stein, Sophia I Thomopoulos, Aslihan Uyar-Demir, Nic J A Van Der Wee, Steven J A Van Der Werff, Henry Völzke, Sarah Whittle, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Tony T Yang, Carlos Zarate, Dick J Veltman, Lianne Schmaal, Paul M Thompson, Roberto Goya-Maldonado, Enigma Major Depressive Disorder Working Group

Student and Faculty Publications

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy …

Depression, Stress And Regional Cerebral Blood Flow, Joshua Chiappelli, Bhim M Adhikari, Mark D Kvarta, Heather A Bruce, Eric L Goldwaser, Yizhou Ma, Shuo Chen, Seth Ament, Alan R Shuldiner, Braxton D Mitchell, Peter Kochunov, Danny Jj Wang, L Elliot Hong

Student and Faculty Publications

Decreased cerebral blood flow (CBF) may be an important mechanism associated with depression. In this study we aimed to determine if the association of CBF and depression is dependent on current level of depression or the tendency to experience depression over time (trait depression), and if CBF is influenced by depression-related factors such as stressful life experiences and antidepressant medication use. CBF was measured in 254 participants from the Amish Connectome Project (age 18-76, 99 men and 154 women) using arterial spin labeling. All participants underwent assessment of symptoms of depression measured with the Beck Depression Inventory and Maryland Trait …

Brain Deficit Patterns Of Metabolic Illnesses Overlap With Those For Major Depressive Disorder: A New Metric Of Brain Metabolic Disease, Kathryn S Hatch, Si Gao, Yizhou Ma, Alessandro Russo, Neda Jahanshad, Paul M Thompson, Bhim M Adhikari, Heather Bruce, Andrew Van Der Vaart, Aristeidis Sotiras, Mark D Kvarta, Thomas E Nichols, Lianne Schmaal, L Elliot Hong, Peter Kochunov

Student and Faculty Publications

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure …

Functional Connectivity Signatures Of Nmdar Dysfunction In Schizophrenia-Integrating Findings From Imaging Genetics And Pharmaco-Fmri, Arnim J Gaebler, Nilüfer Fakour, Felix Stöhr, Jana Zweerings, Arezoo Taebi, Mariia Suslova, Juergen Dukart, Joerg F Hipp, Bhim M Adhikari, Peter Kochunov, Suresh D Muthukumaraswamy, Anna Forsyth, Thomas Eggermann, Florian Kraft, Ingo Kurth, Michael Paulzen, Gerhard Gründer, Frank Schneider, Klaus Mathiak

Student and Faculty Publications

Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A …

Psychotic Symptom, Mood, And Cognition-Associated Multimodal Mri Reveal Shared Links To The Salience Network Within The Psychosis Spectrum Disorders, Chuang Liang, Godfrey Pearlson, Juan Bustillo, Peter Kochunov, Jessica A Turner, Xuyun Wen, Rongtao Jiang, Zening Fu, Xiao Zhang, Kaicheng Li, Xijia Xu, Daoqiang Zhang, Shile Qi, Vince D Calhoun

Student and Faculty Publications

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability …

Association Between Brain Similarity To Severe Mental Illnesses And Comorbid Cerebral, Physical, And Cognitive Impairments, Yizhou Ma, Mark D Kvarta, Bhim M Adhikari, Joshua Chiappelli, Xiaoming Du, Andrew Van Der Vaart, Eric L Goldwaser, Heather Bruce, Kathryn S Hatch, Si Gao, Ann Summerfelt, Neda Jahanshad, Paul M Thompson, Thomas E Nichols, L Elliot Hong, Peter Kochunov

Student and Faculty Publications

Severe mental illnesses (SMIs) are often associated with compromised brain health, physical comorbidities, and cognitive deficits, but it is incompletely understood whether these comorbidities are intrinsic to SMI pathophysiology or secondary to having SMIs. We tested the hypothesis that cerebral, cardiometabolic, and cognitive impairments commonly observed in SMIs can be observed in non-psychiatric individuals with SMI-like brain patterns of deviation as seen on magnetic resonance imaging. 22,883 participants free of common neuropsychiatric conditions from the UK Biobank (age = 63.4 ± 7.5 years, range = 45–82 years, 50.9% female) were split into discovery and replication samples. The regional vulnerability index …

Cerebral Blood Flow And Cardiovascular Risk Effects On Resting Brain Regional Homogeneity, Bhim M Adhikari, L Elliot Hong, Zhiwei Zhao, Danny J J Wang, Paul M Thompson, Neda Jahanshad, Alyssa H Zhu, Stefan Holiga, Jessica A Turner, Theo G M Van Erp, Vince D Calhoun, Kathryn S Hatch, Heather Bruce, Stephanie M Hare, Joshua Chiappelli, Eric L Goldwaser, Mark D Kvarta, Yizhou Ma, Xiaoming Du, Thomas E Nichols, Alan R Shuldiner, Braxton D Mitchell, Juergen Dukart, Shuo Chen, Peter Kochunov

Student and Faculty Publications

Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy …

Multimodel Order Independent Component Analysis: A Data-Driven Method For Evaluating Brain Functional Network Connectivity Within And Between Multiple Spatial Scales, Xing Meng, Armin Iraji, Zening Fu, Peter Kochunov, Aysenil Belger, Judith Ford, Sara Mcewen, Daniel H Mathalon, Bryon A Mueller, Godfrey Pearlson, Steven G Potkin, Adrian Preda, Jessica Turner, Theo Van Erp, Jing Sui, Vince D Calhoun

Student and Faculty Publications

Background:

While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown.

Methods:

We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales …

Revisiting Sex Differences In The Acquisition And Extinction Of Threat Conditioning In Humans, Zhenfu Wen, Jamie Fried, Edward F Pace-Schott, Sara W Lazar, Mohammed R Milad

Student and Faculty Publications

Findings pertaining to sex differences in the acquisition and extinction of threat conditioning, a paradigm widely used to study emotional homeostasis, remain inconsistent, particularly in humans. This inconsistency is likely due to multiple factors, one of which is sample size. Here, we pooled functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) data from multiple studies in healthy humans to examine sex differences during threat conditioning, extinction learning, and extinction memory recall. We observed increased functional activation in males, relative to females, in multiple parietal and frontal (medial and lateral) cortical regions during acquisition of threat conditioning and extinction …

Physical Aggressiveness And Gray Matter Deficits In Ventromedial Prefrontal Cortex, David S. Chester, Donald R. Lynam, Richard Milich, C. Nathan Dewall

Psychology Faculty Publications

What causes individuals to hurt others? Since the famous case of Phineas Gage, lesions of the ventromedial prefrontal cortex (VMPFC) have been reliably linked to physically aggressive behavior. However, it is unclear whether naturally-occurring deficits in VMPFC, among normal individuals, might have widespread consequences for aggression. Using voxel based morphometry, we regressed gray matter density from the brains of 138 normal female and male adults onto their dispositional levels of physical aggression, verbal aggression, and sex, simultaneously. Physical, but not verbal, aggression was associated with reduced gray matter volume in the VMPFC and to a lesser extent, frontopolar cortex. Participants …

Neurophysiological Capacity In A Working Memory Task Differentiates Dependent From Nondependent Heavy Drinkers And Controls, Michael J. Wesley, Joshua A. Lile, Mark T. Fillmore, Linda J. Porrino

Behavioral Science Faculty Publications

Background—Determining the brain-behavior profiles that differentiate heavy drinkers who are and are not alcohol dependent will inform treatment efforts. Working memory is linked to substance use disorders and can serve as a representation of the demand placed on the neurophysiology associated with cognitive control.

Methods—Behavior and brain activity (via fMRI) were recorded during an N-Back working memory task in controls (CTRL), nondependent heavy drinkers (A-ND) and dependent heavy drinkers (A-D). Typical and novel step-wise analyses examined profiles of working memory load and increasing task demand, respectively.

Results—Performance was significantly decreased in A-D during high working memory load …