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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 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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Woodcock EA, Greenwald MK, Chen I, Feng D, Cohn JA, Lundahl LH. HIV chronicity as a predictor of hippocampal memory deficits in daily cannabis users living with HIV. DRUG AND ALCOHOL DEPENDENCE REPORTS 2023; 9:100189. [PMID: 37736522 PMCID: PMC10509297 DOI: 10.1016/j.dadr.2023.100189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/29/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
Background Antiretroviral medications have increased the lifespan of persons living with HIV (PLWH) thereby unmasking memory decline that may be attributed to chronological age, HIV symptomatology, HIV disease chronicity, and/or substance use (especially cannabis use which is common among PLWH). To date, few studies have attempted to disentangle these effects. In a sample of daily cannabis-using PLWH, we investigated whether hippocampal memory function, assessed via an object-location associative learning task, was associated with age, HIV chronicity and symptom severity, or substance use. Methods 48 PLWH (12.9 ± 9.6 years since HIV diagnosis), who were 44 years old on average (range: 24-64 years; 58 % male) and reported daily cannabis use (recent use confirmed by urinalysis) completed the study. We assessed each participant's demographics, substance use, medical history, current HIV symptoms, and hippocampal memory function via a well-validated object-location associative learning task. Results Multiple regression analyses found that living more years since HIV+ diagnosis predicted significantly worse associative learning total score (r=-0.40) and learning rate (r=-0.34) whereas chronological age, cannabis-use characteristics, and recent HIV symptom severity were not significantly related to hippocampal memory function. Conclusions In daily cannabis-using PLWH, HIV chronicity was related to worse hippocampal memory function independent from cannabis use, age, and HIV symptomatology. Object-location associative learning performance could serve as an 'early-warning' metric of cognitive decline among PLWH. Future research should examine longitudinal changes in associative learning proficiency and evaluate interventions to prevent hippocampal memory decline among PLWH. ClinicalTrials.gov: NCT01536899.
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Affiliation(s)
- Eric A. Woodcock
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI USA
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI USA
| | - Mark K. Greenwald
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI USA
| | - Irene Chen
- Wayne State University School of Medicine, Detroit, MI USA
| | - Danni Feng
- Wayne State University School of Medicine, Detroit, MI USA
| | - Jonathan A. Cohn
- Department of Internal Medicine, Wayne State University School of Medicine, Detroit, MI USA
| | - Leslie H. Lundahl
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI USA
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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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A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance. Mol Psychiatry 2022; 27:1184-1191. [PMID: 34642460 PMCID: PMC9001745 DOI: 10.1038/s41380-021-01331-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 09/17/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022]
Abstract
Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.
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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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Salch A, Regalski A, Abdallah H, Suryadevara R, Catanzaro MJ, Diwadkar VA. From mathematics to medicine: A practical primer on topological data analysis (TDA) and the development of related analytic tools for the functional discovery of latent structure in fMRI data. PLoS One 2021; 16:e0255859. [PMID: 34383838 PMCID: PMC8360597 DOI: 10.1371/journal.pone.0255859] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 07/23/2021] [Indexed: 11/19/2022] Open
Abstract
fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. “Structure” within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA (“persistent homology”) to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.
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Affiliation(s)
- Andrew Salch
- Department of Mathematics, Wayne State University, Detroit, Michigan, United States of America
- * E-mail: (AS); (AR); (HA)
| | - Adam Regalski
- Department of Mathematics, Wayne State University, Detroit, Michigan, United States of America
- * E-mail: (AS); (AR); (HA)
| | - Hassan Abdallah
- Department of Mathematics, Wayne State University, Detroit, Michigan, United States of America
- * E-mail: (AS); (AR); (HA)
| | - Raviteja Suryadevara
- Department of Mathematics, Wayne State University, Detroit, Michigan, United States of America
- Department of Psychiatry & Behavioral Neuroscience, Wayne State University, Detroit, Michigan, United States of America
| | - Michael J. Catanzaro
- Department of Mathematics, Iowa State University, Ames, Iowa, United States of America
| | - Vaibhav A. Diwadkar
- Department of Psychiatry & Behavioral Neuroscience, Wayne State University, Detroit, Michigan, United States of America
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8
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Ocular measures during associative learning predict recall accuracy. Int J Psychophysiol 2021; 166:103-115. [PMID: 34052234 DOI: 10.1016/j.ijpsycho.2021.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 05/19/2021] [Accepted: 05/25/2021] [Indexed: 11/20/2022]
Abstract
The ability to form associations between stimuli and commit those associations to memory is a cornerstone of human cognition. Dopamine and noradrenaline are critical neuromodulators implicated in a range of cognitive functions, including learning and memory. Eye blink rate (EBR) and pupil diameter have been shown to index dopaminergic and noradrenergic activity. Here, we examined how these ocular measures relate to accuracy in a paired-associate learning task where participants (N = 73) learned consistent object-location associations over eight trials consisting of pre-trial fixation, encoding, delay, and retrieval epochs. In order to examine how within-subject changes and between-subject changes in ocular metrics related to accuracy, we mean centered individual metric values on each trial based on within-person and across-subject means for each epoch. Within-participant variation in EBR was positively related to accuracy in both encoding and delay epochs: faster EBR within the individual predicted better retrieval. Differences in EBR across participants was negatively related to accuracy in the encoding epoch and in early trials of the pre-trial fixation: faster EBR, relative to other subjects, predicted poorer retrieval. Visual scanning behavior in pre-trial fixation and delay epochs was also positively related to accuracy in early trials: more scanning predicted better retrieval. We found no relationship between pupil diameter and accuracy. These results provide novel evidence supporting the utility of ocular metrics in illuminating cognitive and neurobiological mechanisms of paired-associate learning.
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9
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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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