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Martin E, Chowdury A, Kopchick J, Thomas P, Khatib D, Rajan U, Zajac-Benitez C, Haddad L, Amirsadri A, Robison AJ, Thakkar KN, Stanley JA, Diwadkar VA. The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward. Front Psychiatry 2024; 15:1337882. [PMID: 39355381 PMCID: PMC11443173 DOI: 10.3389/fpsyt.2024.1337882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 08/16/2024] [Indexed: 10/03/2024] Open
Abstract
Introduction Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward. Methods fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure. Results From the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval. Discussion Our analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model's notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain's sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.
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Affiliation(s)
- Elizabeth Martin
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
- Department of Psychiatry, University of Texas Austin, Austin, TX, United States
| | - Asadur Chowdury
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, United States
| | - John Kopchick
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Patricia Thomas
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Dalal Khatib
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Usha Rajan
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Caroline Zajac-Benitez
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Luay Haddad
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Alireza Amirsadri
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Alfred J Robison
- Department of Physiology, Michigan State University, East Lansing, MI, United States
| | - Katherine N Thakkar
- Department of Psychology, Michigan State University, East Lansing, MI, United States
| | - Jeffrey A Stanley
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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Bai YX, Luo JX, Peng D, Sun JJ, Gao YF, Hao LX, Tong BG, He XM, Luo JY, Liang ZH, Yang F. Brain network functional connectivity changes in long illness duration chronic schizophrenia. Front Psychiatry 2024; 15:1423008. [PMID: 38962058 PMCID: PMC11221339 DOI: 10.3389/fpsyt.2024.1423008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/03/2024] [Indexed: 07/05/2024] Open
Abstract
Introduction Chronic schizophrenia has a course of 5 years or more and has a widespread abnormalities in brain functional connectivity. This study aimed to find characteristic functional and structural changes in a long illness duration chronic schizophrenia (10 years or more). Methods Thirty-six patients with a long illness duration chronic schizophrenia and 38 healthy controls were analyzed by independent component analysis of brain network functional connectivity. Correlation analysis with clinical duration was performed on six resting state networks: auditory network, default mode network, dorsal attention network, fronto-parietal network, somatomotor network, and visual network. Results The differences in the resting state network between the two groups revealed that patients exhibited enhanced inter-network connections between default mode network and multiple brain networks, while the inter-network connections between somatomotor network, default mode network and visual network were reduced. In patients, functional connectivity of Cuneus_L was negatively correlated with illness duration. Furthermore, receiver operating characteristic curve of functional connectivity showed that changes in Thalamus_L, Rectus_L, Frontal_Mid_R, and Cerebelum_9_L may indicate a longer illness duration chronic schizophrenia. Discussion In our study, we also confirmed that the course of disease is significantly associated with specific brain regions, and the changes in specific brain regions may indicate that chronic schizophrenia has a course of 10 years or more.
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Affiliation(s)
- Yin Xia Bai
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Jia Xin Luo
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Duo Peng
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Jing Jing Sun
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Yi Fang Gao
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Li Xia Hao
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - B. G. Tong
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Xue Mei He
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Jia Yu Luo
- Department of Rehabilitation, Genghis Khan Community Branch of Inner Mongolia People’s Hospital, Hohhot, China
| | - Zi Hong Liang
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Fan Yang
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
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Samona EA, Chowdury A, Kopchick J, Thomas P, Rajan U, Khatib D, Zajac-Benitez C, Amirsadri A, Haddad L, Stanley JA, Diwadkar VA. The importance of covert memory consolidation in schizophrenia: Dysfunctional network profiles of the hippocampus and the dorsolateral prefrontal cortex. Psychiatry Res Neuroimaging 2024; 340:111805. [PMID: 38447230 PMCID: PMC11188056 DOI: 10.1016/j.pscychresns.2024.111805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 01/24/2024] [Accepted: 02/20/2024] [Indexed: 03/08/2024]
Abstract
Altered brain network profiles in schizophrenia (SCZ) during memory consolidation are typically observed during task-active periods such as encoding or retrieval. However active processes are also sub served by covert periods of memory consolidation. These periods are active in that they allow memories to be recapitulated even in the absence of overt sensorimotor processing. It is plausible that regions central to memory formation like the dlPFC and the hippocampus, exert network signatures during covert periods. Are these signatures altered in patients? The question is clinically relevant because real world learning and memory is facilitated by covert processing, and may be impaired in schizophrenia. Here, we compared network signatures of the dlPFC and the hippocampus during covert periods of a learning and memory task. Because behavioral proficiency increased non-linearly, functional connectivity of the dlPFC and hippocampus [psychophysiological interaction (PPI)] was estimated for each of the Early (linear increases in performance) and Late (asymptotic performance) covert periods. During Early periods, we observed hypo-modulation by the hippocampus but hyper-modulation by dlPFC. Conversely, during Late periods, we observed hypo-modulation by both the dlPFC and the hippocampus. We stitch these results into a conceptual model of network deficits during covert periods of memory consolidation.
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Affiliation(s)
- Elias A Samona
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Asadur Chowdury
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - John Kopchick
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Patricia Thomas
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Usha Rajan
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Dalal Khatib
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Caroline Zajac-Benitez
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Alireza Amirsadri
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Luay Haddad
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Jeffrey A Stanley
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States.
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Muzik O, Diwadkar VA. Depth and hierarchies in the predictive brain: From reaction to action. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2023; 14:e1664. [PMID: 37518831 DOI: 10.1002/wcs.1664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 05/18/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.
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Affiliation(s)
- Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Children's Hospital of Michigan, Michigan, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, Michigan, USA
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Luo L, Li Q, Wang Y, He N, Wang Y, You W, Zhang Q, Long F, Chen L, Zhao Y, Yao L, Sweeney JA, Gong Q, Li F. Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia. Schizophr Bull 2023; 49:1387-1398. [PMID: 37030006 PMCID: PMC10483459 DOI: 10.1093/schbul/sbad042] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) and schizophrenia have distinct but also overlapping symptoms. Few studies have examined the shared and disorder-specific disturbances in dynamic brain function in the 2 disorders. STUDY DESIGN Resting-state functional magnetic resonance imaging data of 31 patients with OCD and 49 patients with schizophrenia, all untreated, and 45 healthy controls (HCs) were analyzed using spatial group independent component (IC) analysis. Time-varying degree centrality patterns across the whole brain were clustered into 3 reoccurring states, and state transition metrics were obtained. We further explored regional temporal variability of degree centrality for each IC across all time windows. STUDY RESULTS Patients with OCD and patients with schizophrenia both showed decreased occurrence of a state having the highest centrality in the sensorimotor and auditory networks. Additionally, patients with OCD and patients with schizophrenia both exhibited reduced dynamics of degree centrality in the superior frontal gyrus than controls, while dynamic degree centrality of the cerebellum was lower in patients with schizophrenia than with OCD and HCs. Altered dynamics of degree centrality nominally correlated with symptom severity in both patient groups. CONCLUSIONS Our study provides evidence of transdiagnostic and clinically relevant functional brain abnormalities across OCD and schizophrenia in neocortex, as well as functional dynamic alterations in the cerebellum specific to schizophrenia. These findings add to the recognition of overlap in neocortical alterations in the 2 disorders, and indicate that cerebellar alterations in schizophrenia may be specifically important in schizophrenia pathophysiology via impact on cerebellar thalamocortical circuitry.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Radiology, West China Second Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
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Hasan SM, Huq MS, Chowdury AZ, Baajour S, Kopchick J, Robison AJ, Thakkar KN, Haddad L, Amirsadri A, Thomas P, Khatib D, Rajan U, Stanley JA, Diwadkar VA. Learning without contingencies: A loss of synergy between memory and reward circuits in schizophrenia. Schizophr Res 2023; 258:21-35. [PMID: 37467677 PMCID: PMC10521382 DOI: 10.1016/j.schres.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 02/09/2023] [Accepted: 06/11/2023] [Indexed: 07/21/2023]
Abstract
Motivational deficits in schizophrenia may interact with foundational cognitive processes including learning and memory to induce impaired cognitive proficiency. If such a loss of synergy exists, it is likely to be underpinned by a loss of synchrony between the brains learning and reward sub-networks. Moreover, this loss should be observed even during tasks devoid of explicit reward contingencies given that such tasks are better models of real world performance than those with artificial contingencies. Here we applied undirected functional connectivity (uFC) analyses to fMRI data acquired while participants engaged in an associative learning task without contingencies or feedback. uFC was estimated and inter-group differences (between schizophrenia patients and controls, n = 54 total, n = 28 patients) were assessed within and between reward (VTA and NAcc) and learning/memory (Basal Ganglia, DPFC, Hippocampus, Parahippocampus, Occipital Lobe) sub-networks. The task paradigm itself alternated between Encoding, Consolidation, and Retrieval conditions, and uFC differences were quantified for each of the conditions. Significantly reduced uFC dominated the connectivity profiles of patients across all conditions. More pertinent to our motivations, these reductions were observed within and across classes of sub-networks (reward-related and learning/memory related). We suggest that disrupted functional connectivity between reward and learning sub-networks may drive many of the performance deficits that characterize schizophrenia. Thus, cognitive deficits in schizophrenia may in fact be underpinned by a loss of synergy between reward-sensitivity and cognitive processes.
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Affiliation(s)
- Sazid M Hasan
- Oakland University William Beaumont School of Medicine, USA
| | - Munajj S Huq
- Michigan State University, College of Osteopathic Medicine, USA
| | - Asadur Z Chowdury
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Shahira Baajour
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - John Kopchick
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - A J Robison
- Dept. of Physiology, Michigan State University, USA
| | | | - Luay Haddad
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Alireza Amirsadri
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Patricia Thomas
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Dalal Khatib
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Usha Rajan
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Jeffrey A Stanley
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Vaibhav A Diwadkar
- Dept. of Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, USA.
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Meram ED, Baajour S, Chowdury A, Kopchick J, Thomas P, Rajan U, Khatib D, Zajac-Benitez C, Haddad L, Amirsadri A, Stanley JA, Diwadkar VA. The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia. Netw Neurosci 2023; 7:184-212. [PMID: 37333998 PMCID: PMC10270714 DOI: 10.1162/netn_a_00278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/05/2022] [Indexed: 07/21/2023] Open
Abstract
There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.
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Affiliation(s)
- Emmanuel D. Meram
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Shahira Baajour
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - John Kopchick
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Patricia Thomas
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Usha Rajan
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Dalal Khatib
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Caroline Zajac-Benitez
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Luay Haddad
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Alireza Amirsadri
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jeffrey A. Stanley
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vaibhav A. Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
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Kody E, Diwadkar VA. Magnocellular and parvocellular contributions to brain network dysfunction during learning and memory: Implications for schizophrenia. J Psychiatr Res 2022; 156:520-531. [PMID: 36351307 DOI: 10.1016/j.jpsychires.2022.10.055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/07/2022]
Abstract
Memory deficits are core features of schizophrenia, and a central aim in biological psychiatry is to identify the etiology of these deficits. Scrutiny is naturally focused on the dorsolateral prefrontal cortex and the hippocampal cortices, given these structures' roles in memory and learning. The fronto-hippocampal framework is valuable but restrictive. Network-based underpinnings of learning and memory are substantially diverse and include interactions between hetero-modal and early sensory networks. Thus, a loss of fidelity in sensory information may impact memorial and cognitive processing in higher-order brain sub-networks, becoming a sensory source for learning and memory deficits. In this overview, we suggest that impairments in magno- and parvo-cellular visual pathways result in degraded inputs to core learning and memory networks. The ascending cascade of aberrant neural events significantly contributes to learning and memory deficits in schizophrenia. We outline the network bases of these effects, and suggest that any network perspectives of dysfunction in schizophrenia must assess the impact of impaired perceptual contributions. Finally, we speculate on how this framework enriches the space of biomarkers and expands intervention strategies to ameliorate this prototypical disconnection syndrome.
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Affiliation(s)
- Elizabeth Kody
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, USA.
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Zovetti N, Bellani M, Chowdury A, Alessandrini F, Zoccatelli G, Perlini C, Ricciardi GK, Marzi CA, Diwadkar VA, Brambilla P. Inefficient white matter activity in Schizophrenia evoked during intra and inter-hemispheric communication. Transl Psychiatry 2022; 12:449. [PMID: 36244980 PMCID: PMC9573867 DOI: 10.1038/s41398-022-02200-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/13/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Intensive cognitive tasks induce inefficient regional and network responses in schizophrenia (SCZ). fMRI-based studies have naturally focused on gray matter, but appropriately titrated visuo-motor integration tasks reliably activate inter- and intra-hemispheric white matter pathways. Such tasks can assess network inefficiency without demanding intensive cognitive effort. Here, we provide the first application of this framework to the study of white matter functional responses in SCZ. Event-related fMRI data were acquired from 28 patients (nine females, mean age 43.3, ±11.7) and 28 age- and gender-comparable controls (nine females, mean age 42.1 ± 10.1), using the Poffenberger paradigm, a rapid visual detection task used to induce intra- (ipsi-lateral visual and motor cortex) or inter-hemispheric (contra-lateral visual and motor cortex) transfer. fMRI data were pre- and post-processed to reliably isolate activations in white matter, using probabilistic tractography-based white matter tracts. For intra- and inter-hemispheric transfer conditions, SCZ evinced hyper-activations in longitudinal and transverse white matter tracts, with hyper-activation in sub-regions of the corpus callosum primarily observed during inter-hemispheric transfer. Evidence for the functional inefficiency of white matter was observed in conjunction with small (~50 ms) but significant increases in response times. Functional inefficiencies in SCZ are (1) observable in white matter, with the degree of inefficiency contextually related to task-conditions, and (2) are evoked by simple detection tasks without intense cognitive processing. These cumulative results while expanding our understanding of this dys-connection syndrome, also extend the search of biomarkers beyond the traditional realm of fMRI studies of gray matter.
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Affiliation(s)
- Niccolò Zovetti
- grid.5611.30000 0004 1763 1124Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
| | - Asadur Chowdury
- grid.254444.70000 0001 1456 7807Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI USA
| | - Franco Alessandrini
- grid.411475.20000 0004 1756 948XNeuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Giada Zoccatelli
- grid.411475.20000 0004 1756 948XNeuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Cinzia Perlini
- grid.5611.30000 0004 1763 1124Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy
| | - Giuseppe K. Ricciardi
- Pathology and Diagnostics, Section of Neuroradiology, Hospital Trust Verona, Verona, Italy
| | - Carlo A. Marzi
- grid.5611.30000 0004 1763 1124Physiology and Psychology Section, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy ,National Institute of Neuroscience, Verona, Italy
| | - Vaibhav A. Diwadkar
- grid.254444.70000 0001 1456 7807Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI USA
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy. .,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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10
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Raucher-Chéné D, Lavigne KM, Lepage M. Episodic Memory and Schizophrenia: From Characterization of Relational Memory Impairments to Neuroimaging Biomarkers. Curr Top Behav Neurosci 2022; 63:115-136. [PMID: 35902545 DOI: 10.1007/7854_2022_379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Episodic memory research in schizophrenia has a long history already which has clearly established significant impairments and strong associations with brain measures and functional outcome. The purpose of this chapter is not to make an exhaustive review of the recent literature but to highlight some relatively recent developments in the cognitive neuroscience field of episodic memory and schizophrenia. Hence, we present a contemporary view focusing specifically of relational memory which represents a form of episodic memory that refers to associations or binding among items or elements presented together. We describe the major tasks used and illustrate how their combination with brain imaging has: (1) favored the use of experimental memory tasks to isolate specific processes with specific neural correlates, (2) led to a distributed view of the neural correlates of memory impairments in schizophrenia where multiple regions are contributing, and (3) made possible the identification of fMRI biomarkers specific to episodic memory. We then briefly propose what we see as the next steps for memory research in schizophrenia so that the impact of this work can be maximized.
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Affiliation(s)
- Delphine Raucher-Chéné
- Cognition, Health, and Society Laboratory (EA 6291), University of Reims Champagne-Ardenne, Reims, France.,Academic Department of Psychiatry, University Hospital of Reims, EPSM Marne, Reims, France
| | - Katie M Lavigne
- Douglas Research Centre, Verdun, QC, Canada.,Department of Psychiatry, McGill University, Montréal, QC, Canada.,McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Martin Lepage
- Douglas Research Centre, Verdun, QC, Canada. .,Department of Psychiatry, McGill University, Montréal, QC, Canada.
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11
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Király B, Hangya B. Navigating the Statistical Minefield of Model Selection and Clustering in Neuroscience. eNeuro 2022; 9:ENEURO.0066-22.2022. [PMID: 35835556 PMCID: PMC9282170 DOI: 10.1523/eneuro.0066-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/16/2022] [Accepted: 06/22/2022] [Indexed: 11/21/2022] Open
Abstract
Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an additive and a multiplicative scaler describes the behavior of the neuron; even calculating an average implicitly assumes that the data were sampled from a distribution that has a finite first statistical moment: the mean. Model selection may be explicit, when the aim is to test whether one model provides a better description of the data than a competing one. As a special case, clustering algorithms identify groups with similar properties within the data. They are widely used from spike sorting to cell type identification to gene expression analysis. We discuss model selection and clustering techniques from a statistician's point of view, revealing the assumptions behind, and the logic that governs the various approaches. We also showcase important neuroscience applications and provide suggestions how neuroscientists could put model selection algorithms to best use as well as what mistakes should be avoided.
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Affiliation(s)
- Bálint Király
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, H-1083, Budapest, Hungary
- Department of Biological Physics, Eötvös Loránd University, H-1083, Budapest, Hungary
| | - Balázs Hangya
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, H-1083, Budapest, Hungary
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12
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Yuan J, Ji S, Luo L, Lv J, Liu T. Control energy assessment of spatial interactions among
macro‐scale
brain networks. Hum Brain Mapp 2022; 43:2181-2203. [PMID: 35072300 PMCID: PMC8996365 DOI: 10.1002/hbm.25780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/04/2021] [Accepted: 01/03/2022] [Indexed: 11/19/2022] Open
Abstract
Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.
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Affiliation(s)
- Jing Yuan
- College of Artificial Intelligence Nankai University Tianjin China
| | - Senquan Ji
- College of Artificial Intelligence Nankai University Tianjin China
| | - Liao Luo
- College of Artificial Intelligence Nankai University Tianjin China
| | - Jinglei Lv
- School of Biomedical Engineering The University of Sydney Sydney New South Wales Australia
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center The University of Georgia Athens Georgia USA
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13
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Yacou MA, Chowdury A, Easter P, Hanna GL, Rosenberg DR, Diwadkar VA. Sustained attention induces altered effective connectivity of the ascending thalamo-cortical relay in obsessive-compulsive disorder. Front Psychiatry 2022; 13:869106. [PMID: 36032258 PMCID: PMC9402224 DOI: 10.3389/fpsyt.2022.869106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Abnormal function of the thalamo-cortical relay is considered a hallmark of obsessive-compulsive disorder (OCD) and aberrant network interactions may underpin many of the clinical and cognitive symptoms that characterize the disorder. Several statistical approaches have been applied to in vivo fMRI data to support the general loss of thalamo-cortical connectivity in OCD. However, (a) few studies have assessed the contextual constraints under which abnormal network interactions arise or (b) have used methods of effective connectivity to understand abnormal network interactions. Effective connectivity is a particularly valuable method as it describes the putative causal influences that brain regions exert over each other, as opposed to the largely statistical consistencies captured in functional connectivity techniques. Here, using dynamic causal modeling (DCM), we evaluated how attention demand induced inter-group differences (HC ≠ OCD) in effective connectivity within a motivated thalamo-cortical network. Of interest was whether these effects were observed on the ascending thalamo-cortical relay, essential for the sensory innervation of the cortex. fMRI time series data from sixty-two participants (OCD, 30; HC, 32) collected using an established sustained attention task were submitted to a space of 162 competing models. Across the space, models distinguished between competing hypotheses of thalamo-cortical interactions. Bayesian model selection (BMS) identified marginally differing likely generative model architectures in OCD and HC groups. Bayesian model averaging (BMA), was used to weight connectivity parameter estimates across all models, with each parameter weighted by each model's posterior probability, thus providing more stable estimates of effective connectivity. Inferential statistical analyses of estimated parameters revealed two principal results: (1) Significantly reduced intrinsic connectivity of the V1 → SPC pathway in OCD, suggested connective weakness in the early constituents of the dorsal visual pathway; (2) More pertinent with the discovery possibilities afforded by DCM, sustained attention in OCD patients induced significantly reduced contextual modulation of the ascending relay from the thalamus to the prefrontal cortex. These results form an important complement to our understanding of the contextual bases of thalamo-cortical network deficits in OCD, emphasizing vulnerability of the ascending relay.
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Affiliation(s)
- Mario A Yacou
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Philip Easter
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Gregory L Hanna
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - David R Rosenberg
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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14
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Baajour SJ, Chowdury A, Thomas P, Rajan U, Khatib D, Zajac-Benitez C, Falco D, Haddad L, Amirsadri A, Bressler S, Stanley JA, Diwadkar VA. Disordered directional brain network interactions during learning dynamics in schizophrenia revealed by multivariate autoregressive models. Hum Brain Mapp 2020; 41:3594-3607. [PMID: 32436639 PMCID: PMC7416040 DOI: 10.1002/hbm.25032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 04/09/2020] [Accepted: 04/28/2020] [Indexed: 12/12/2022] Open
Abstract
Directional network interactions underpin normative brain function in key domains including associative learning. Schizophrenia (SCZ) is characterized by altered learning dynamics, yet dysfunctional directional functional connectivity (dFC) evoked during learning is rarely assessed. Here, nonlinear learning dynamics were induced using a paradigm alternating between conditions (Encoding and Retrieval). Evoked fMRI time series data were modeled using multivariate autoregressive (MVAR) models, to discover dysfunctional direction interactions between brain network constituents during learning stages (Early vs. Late), and conditions. A functionally derived subnetwork of coactivated (healthy controls [HC] ∩ SCZ] nodes was identified. MVAR models quantified directional interactions between pairs of nodes, and coefficients were evaluated for intergroup differences (HC ≠ SCZ). In exploratory analyses, we quantified statistical effects of neuroleptic dosage on performance and MVAR measures. During Early Encoding, SCZ showed reduced dFC within a frontal–hippocampal–fusiform network, though during Late Encoding reduced dFC was associated with pathways toward the dorsolateral prefrontal cortex (dlPFC). During Early Retrieval, SCZ showed increased dFC in pathways to and from the dorsal anterior cingulate cortex, though during Late Retrieval, patients showed increased dFC in pathways toward the dlPFC, but decreased dFC in pathways from the dlPFC. These discoveries constitute novel extensions of our understanding of task‐evoked dysconnection in schizophrenia and motivate understanding of the directional aspect of the dysconnection in schizophrenia. Disordered directionality should be investigated using computational psychiatric approaches that complement the MVAR method used in our work.
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Affiliation(s)
- Shahira J Baajour
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Patricia Thomas
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Usha Rajan
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Dalal Khatib
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Caroline Zajac-Benitez
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Dimitri Falco
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, USA
| | - Luay Haddad
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Alireza Amirsadri
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Steven Bressler
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, USA.,Department of Psychology, Florida Atlantic University, Boca Raton, Florida, USA
| | - Jeffery A Stanley
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan, USA
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