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Huang S, De Brigard F, Cabeza R, Davis SW. Connectivity analyses for task-based fMRI. Phys Life Rev 2024; 49:139-156. [PMID: 38728902 PMCID: PMC11116041 DOI: 10.1016/j.plrev.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.
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Affiliation(s)
- Shenyang Huang
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States.
| | - Felipe De Brigard
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States
| | - Roberto Cabeza
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
| | - Simon W Davis
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States; Department of Neurology, Duke University School of Medicine, Durham, NC 27708, United States
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2
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Rangaprakash D, David O, Barry RL, Deshpande G. Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530359. [PMID: 37961471 PMCID: PMC10634675 DOI: 10.1101/2023.02.27.530359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
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3
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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4
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Islam MR, Islam SMR. Develop blood oxygen level dependent signal by metabolic/hemodynamic model using numerical methods. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
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5
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Avcu E, Newman O, Ahlfors SP, Gow DW. Neural evidence suggests phonological acceptability judgments reflect similarity, not constraint evaluation. Cognition 2023; 230:105322. [PMID: 36370613 PMCID: PMC9712273 DOI: 10.1016/j.cognition.2022.105322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 10/24/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022]
Abstract
Acceptability judgments are a primary source of evidence in formal linguistic research. Within the generative linguistic tradition, these judgments are attributed to evaluation of novel forms based on implicit knowledge of rules or constraints governing well-formedness. In the domain of phonological acceptability judgments, other factors including ease of articulation and similarity to known forms have been hypothesized to influence evaluation. We used data-driven neural techniques to identify the relative contributions of these factors. Granger causality analysis of magnetic resonance imaging (MRI)-constrained magnetoencephalography (MEG) and electroencephalography (EEG) data revealed patterns of interaction between brain regions that support explicit judgments of the phonological acceptability of spoken nonwords. Comparisons of data obtained with nonwords that varied in terms of onset consonant cluster attestation and acceptability revealed different cortical regions and effective connectivity patterns associated with phonological acceptability judgments. Attested forms produced stronger influences of brain regions implicated in lexical representation and sensorimotor simulation on acoustic-phonetic regions, whereas unattested forms produced stronger influence of phonological control mechanisms on acoustic-phonetic processing. Unacceptable forms produced widespread patterns of interaction consistent with attempted search or repair. Together, these results suggest that speakers' phonological acceptability judgments reflect lexical and sensorimotor factors.
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Affiliation(s)
- Enes Avcu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
| | - Olivia Newman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America; Department of Radiology, Harvard Medical School, Boston, MA, United States of America
| | - David W Gow
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America; Department of Psychology, Salem State University, Salem, MA, United States of America; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, United States of America
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6
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Goodman AM, Wheelock MD, Harnett NG, Davis ES, Mrug S, Deshpande G, Knight DC. Stress-Induced Changes in Effective Connectivity During Regulation of the Emotional Response to Threat. Brain Connect 2022; 12:629-638. [PMID: 34541896 PMCID: PMC9634990 DOI: 10.1089/brain.2021.0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Stress-related disruption of emotion regulation appears to involve the prefrontal cortex (PFC) and amygdala. However, the interactions between brain regions that mediate stress-induced changes in emotion regulation remain unclear. The present study builds upon prior work that assessed stress-induced changes in the neurobehavioral response to threat by investigating effective connectivity between these brain regions. Methods: Participants completed the Montreal Imaging Stress Task followed by a Pavlovian fear conditioning procedure during functional magnetic resonance imaging. Stress ratings and psychophysiological responses were used to assess stress reactivity. Effective connectivity during fear conditioning was identified using multivariate autoregressive modeling. Effective connectivity values were calculated during threat presentations that were either predictable (preceded by a warning cue) or unpredictable (no warning cue). Results: A neural hub within the dorsomedial PFC (dmPFC) showed greater effective connectivity to other PFC regions, inferior parietal lobule, insula, and amygdala during predictable than unpredictable threat. The dmPFC also showed greater connectivity to different dorsolateral PFC and amygdala regions during unpredictable than predictable threat. Stress ratings varied with connectivity differences from the dmPFC to the amygdala. Connectivity from dmPFC to amygdala was greater in general during unpredictable than predictable threat, however, this connectivity increased during predictable compared with unpredictable threat as stress reactivity increased. Conclusions: Our findings suggest that acute stress disrupts connectivity underlying top-down emotion regulation of the threat response. Furthermore, increased connectivity between the dmPFC and amygdala may play a critical role in stress-induced changes in the emotional response to threat. Impact statement The present study builds upon prior work that assessed stress-induced changes in the human neurobehavioral response to threat by demonstrating that increased top-down connectivity from the dorsomedial prefrontal cortex to the amygdala varies with individual differences in stress reactivity. These findings provide novel evidence in humans of stress-induced disruption of a specific top-down corticolimbic circuit during active emotion regulation processes, which may play a causal role in the long-term effects of chronic or excessive stress exposure.
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Affiliation(s)
- Adam M. Goodman
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Muriah D. Wheelock
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Nathaniel G. Harnett
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Elizabeth S. Davis
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Sylvie Mrug
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
- Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA
- Center for Neuroscience, Auburn University, Auburn, Alabama, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - David C. Knight
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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8
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Learning dynamic causal mechanisms from non-stationary data. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03843-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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9
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Seok D, Tadayonnejad R, Wong WW, O'Neill J, Cockburn J, Bari AA, O'Doherty JP, Feusner JD. Neurocircuit dynamics of arbitration between decision-making strategies across obsessive-compulsive and related disorders. Neuroimage Clin 2022; 35:103073. [PMID: 35689978 PMCID: PMC9192960 DOI: 10.1016/j.nicl.2022.103073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/11/2022] [Accepted: 05/31/2022] [Indexed: 11/20/2022]
Abstract
Obsessive-compulsive and related disorders (OCRD) include OCD and BDD. Neural differences in decision-making arbitration may underlie OCRD symptoms. Resting-state effective connectivity was used to assess arbitration circuitry. Greater left putamen inhibition via left ventrolateral prefrontal cortex in OCRD. Stronger left putamen inhibition was correlated with less severe symptoms.
Obsessions and compulsions are central components of obsessive–compulsive disorder (OCD) and obsessive–compulsive related disorders such as body dysmorphic disorder (BDD). Compulsive behaviours may result from an imbalance of habitual and goal-directed decision-making strategies. The relationship between these symptoms and the neural circuitry underlying habitual and goal-directed decision-making, and the arbitration between these strategies, remains unknown. This study examined resting state effective connectivity between nodes of these systems in two cohorts with obsessions and compulsions, each compared with their own corresponding healthy controls: OCD (nOCD = 43; nhealthy = 24) and BDD (nBDD = 21; nhealthy = 16). In individuals with OCD, the left ventrolateral prefrontal cortex, a node of the arbitration system, exhibited more inhibitory causal influence over the left posterolateral putamen, a node of the habitual system, compared with controls. Inhibitory causal influence in this connection showed a trend for a similar pattern in individuals with BDD compared with controls. Those with stronger negative connectivity had lower obsession and compulsion severity in both those with OCD and those with BDD. These relationships were not evident within the habitual or goal-directed circuits, nor were they associated with depressive or anxious symptomatology. These results suggest that abnormalities in the arbitration system may represent a shared neural phenotype across these two related disorders that is specific to obsessive–compulsive symptoms. In addition to nosological implications, these results identify potential targets for novel, circuit-specific treatments.
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Affiliation(s)
- Darsol Seok
- Division of Cognitive Neuroscience, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA
| | - Reza Tadayonnejad
- Division of Neuromodulation, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 1200 E. California Blvd., Code 228-77, Pasadena, CA 91125, USA
| | - Wan-Wa Wong
- Division of Cognitive Neuroscience, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA
| | - Joseph O'Neill
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA
| | - Jeff Cockburn
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 1200 E. California Blvd., Code 228-77, Pasadena, CA 91125, USA
| | - Ausaf A Bari
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - John P O'Doherty
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 1200 E. California Blvd., Code 228-77, Pasadena, CA 91125, USA; Computation & Neural Systems Program, California Institute of Technology, Pasadena, CA, 1200 East California Boulevard, Pasadena, CA 91125, USA
| | - Jamie D Feusner
- Division of Cognitive Neuroscience, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Temerty Faculty of Medicine, Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON M5T 1R8, Canada; Department of Women's and Children's Health, The Karolinska Institute, Tomtebodavägen 18A, 171 77 Solna, Sweden.
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10
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Hunter MD, Fatimah H, Bornovalova MA. Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data. PSYCHOMETRIKA 2022; 87:477-505. [PMID: 35064891 PMCID: PMC9177592 DOI: 10.1007/s11336-021-09827-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/22/2021] [Indexed: 06/14/2023]
Abstract
With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.
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Affiliation(s)
- Michael D Hunter
- Department of Human Development and Family Studies, Pennsylvania State University, 119 Health and Human Development Building, University Park, PA, 16802, USA
| | - Haya Fatimah
- Department of Psychology, University of South Florida, Tampa, FL, 33620, USA
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11
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Yan W, Palaniyappan L, Liddle PF, Rangaprakash D, Wei W, Deshpande G. Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity. Schizophr Bull 2022; 48:695-711. [PMID: 34951473 PMCID: PMC9077436 DOI: 10.1093/schbul/sbab140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.
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Affiliation(s)
- Wenjing Yan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Peter F Liddle
- Centre for Translational Neuroimaging, Division of Mental Health and Clinical Neuroscience, Institute of Mental Health, University of Nottingham, UK
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Wei
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL
- Alabama Advanced Imaging Consortium, Birmingham, AL
- Center for Neuroscience, Auburn University, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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12
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Costantini I, Deriche R, Deslauriers-Gauthier S. An Anisotropic 4D Filtering Approach to Recover Brain Activation From Paradigm-Free Functional MRI Data. FRONTIERS IN NEUROIMAGING 2022; 1:815423. [PMID: 37555185 PMCID: PMC10406250 DOI: 10.3389/fnimg.2022.815423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 02/11/2022] [Indexed: 08/10/2023]
Abstract
CONTEXT Functional Magnetic Resonance Imaging (fMRI) is a non-invasive imaging technique that provides an indirect view into brain activity via the blood oxygen level dependent (BOLD) response. In particular, resting-state fMRI poses challenges to the recovery of brain activity without prior knowledge on the experimental paradigm, as it is the case for task fMRI. Conventional methods to infer brain activity from the fMRI signals, for example, the general linear model (GLM), require the knowledge of the experimental paradigm to define regressors and estimate the contribution of each voxel's time course to the task. To overcome this limitation, approaches to deconvolve the BOLD response and recover the underlying neural activations without a priori information on the task have been proposed. State-of-the-art techniques, and in particular the total activation (TA), formulate the deconvolution as an optimization problem with decoupled spatial and temporal regularization and an optimization strategy that alternates between the constraints. APPROACH In this work, we propose a paradigm-free regularization algorithm named Anisotropic 4D-fMRI (A4D-fMRI) that is applied on the 4D fMRI image, acting simultaneously in the 3D space and 1D time dimensions. Based on the idea that large image variations should be preserved as they occur during brain activations, whereas small variations considered as noise should be removed, the A4D-fMRI applies an anisotropic regularization, thus recovering the location and the duration of brain activations. RESULTS Using the experimental paradigm as ground truth, the A4D-fMRI is validated on synthetic and real task-fMRI data from 51 subjects, and its performance is compared to the TA. Results show higher correlations of the recovered time courses with the ground truth compared to the TA and lower computational times. In addition, we show that the A4D-fMRI recovers activity that agrees with the GLM, without requiring or using any knowledge of the experimental paradigm.
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13
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Fisher ZF, Chow SM, Molenaar PCM, Fredrickson BL, Pipiras V, Gates KM. A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters. MULTIVARIATE BEHAVIORAL RESEARCH 2022; 57:134-152. [PMID: 33025834 PMCID: PMC8482377 DOI: 10.1080/00273171.2020.1815513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.
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Affiliation(s)
- Zachary F Fisher
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Sy-Miin Chow
- Human Development and Family Studies, Pennsylvania State University
| | | | - Barbara L Fredrickson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Vladas Pipiras
- Department of Statistics, University of North Carolina at Chapel Hill
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
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14
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Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain". Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110147. [PMID: 33096157 DOI: 10.1016/j.pnpbp.2020.110147] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/21/2020] [Accepted: 10/16/2020] [Indexed: 01/27/2023]
Abstract
Although accumulating neuroimaging studies have reported that social behavior deficits in children with autism spectrum disorders (ASD) are commonly attributed to the dysfunction of social brain regions underlying social cognition, the dynamic interaction within the social brain network and its association with social deficits remain unclear. Here, resting-state functional magnetic resonance imaging data obtained from Autism Brain Imaging Data Exchange (I and II) were analyzed in 105 children with ASD and 102 demographically matched typically developing controls (TDCs) (age range: 7-12 years old). Term-based meta-analysis combined the prior reference and anatomical labeling were used to define the regions of interests of the social brain network, and multivariate Granger causality analysis with blind deconvolution was employed to assess the effective connectivity within the social brain network in the ASD and TDC groups. Between-group comparison revealed significantly attenuated effective connectivity from the medial prefrontal cortex (mPFC) to the bilateral amygdala in children with the ASD group compared with TDC group. In addition, raw values of the effective connectivity from the mPFC to the bilateral amygdala were used to predict social deficits in ASD. Our findings indicate the impaired mPFC-amygdala pathway and its association with social deficits in children with ASD and provide a new perspective into the neuropathology of the developing autistic brain.
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15
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Abrevaya G, Dumas G, Aravkin AY, Zheng P, Gagnon-Audet JC, Kozloski J, Polosecki P, Lajoie G, Cox D, Dawson SP, Cecchi G, Rish I. Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput 2021; 33:2087-2127. [PMID: 34310676 DOI: 10.1162/neco_a_01401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 02/19/2021] [Indexed: 01/16/2023]
Abstract
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.
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Affiliation(s)
- Germán Abrevaya
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillaume Dumas
- Mila-Quebec Artificial Intelligence Institute, and CHU Sainte-Justine Research Center, Department of Psychiatry, Universitéde Montréal, Montreal H3A OE8, Canada
| | | | - Peng Zheng
- University of Washington, Seattle, WA 98195, U.S.A.
| | | | - James Kozloski
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Pablo Polosecki
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Guillaume Lajoie
- Mila-Quebec Artificial Intelligence Institute, Universitéde Montréal, Montreal H3A OE8, Canada
| | - David Cox
- MIT-IBM Watson AI Lab, Cambridge, MA 02139, U.S.A.
| | - Silvina Ponce Dawson
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillermo Cecchi
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Irina Rish
- Mila-Quebec Artificial Intelligence Institute, Université de Montréal, Montreal H3A OE8, Canada
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16
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Rangaprakash D, Tadayonnejad R, Deshpande G, O'Neill J, Feusner JD. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging Behav 2021; 15:1622-1640. [PMID: 32761566 PMCID: PMC7865013 DOI: 10.1007/s11682-020-00358-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.
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Affiliation(s)
- D Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School and Harvard-MIT Health Sciences and Technology, Cambridge, MA, 02129, USA
| | - Reza Tadayonnejad
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, 36849, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, 36849, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Auburn, AL, USA
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joseph O'Neill
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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17
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Di X, Zhang Z, Biswal BB. Understanding psychophysiological interaction and its relations to beta series correlation. Brain Imaging Behav 2021; 15:958-973. [PMID: 32710336 PMCID: PMC10666061 DOI: 10.1007/s11682-020-00304-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Psychophysiological interaction (PPI) was proposed 20 years ago for study of task modulated connectivity on functional MRI (fMRI) data. A few modifications have since been made, but there remain misunderstandings on the method, as well as on its relations to a similar method named beta series correlation (BSC). Here, we explain what PPI measures and its relations to BSC. We first clarify that the interpretation of a regressor in a general linear model depends on not only itself but also on how other effects are modeled. In terms of PPI, it always reflects differences in connectivity between conditions, when the physiological variable is included as a covariate. Secondly, when there are multiple conditions, we explain how PPI models calculated from direct contrast between conditions could generate identical results as contrasting separate PPIs of each condition (a.k.a. "generalized" PPI). Thirdly, we explicit the deconvolution process that is used for PPI calculation, and how is it related to the trial-by-trial modeling for BSC, and illustrate the relations between PPI and those based upon BSC. In particular, when context sensitive changes in effective connectivity are present, they manifest as changes in correlations of observed trial-by-trial activations or functional connectivity. Therefore, BSC and PPI can detect similar connectivity differences. Lastly, we report empirical analyses using PPI and BSC on fMRI data of an event-related stop signal task to illustrate our points.
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Affiliation(s)
- Xin Di
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Bharat B Biswal
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA.
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18
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Shoji I. Nonparametric filtering for stochastic nonlinear oscillations. Phys Rev E 2020; 102:052221. [PMID: 33327177 DOI: 10.1103/physreve.102.052221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/25/2020] [Indexed: 11/07/2022]
Abstract
This paper proposes a method of nonparametric filtering for stochastic nonlinear oscillations with particular interest in their derivative estimation. Based on a second-order ordinary differential equation, a stochastic oscillation is modeled by a two-variate stochastic differential equation without specifying the function form of the drift function, where the first variable is assumed to be observable but not the other. Given the discrete time series with observation error, the proposed method enables us to estimate the values of the drift function and its derivatives including those of the unobservable variable. According to the results of numerical experiments to compare estimation accuracy with a parametric method, the proposed method shows better performance in the estimation of nonlinear models.
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Affiliation(s)
- Isao Shoji
- Tokyo University of Science, Fujimi, Chiyoda, Tokyo 102-0071, Japan
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19
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Bielczyk NZ, Llera A, Buitelaar JK, Glennon JC, Beckmann CF. Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions. Netw Neurosci 2019; 3:1009-1037. [PMID: 31637336 PMCID: PMC6779268 DOI: 10.1162/netn_a_00099] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 06/03/2019] [Indexed: 12/20/2022] Open
Abstract
Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
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20
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Caballero-Gaudes C, Moia S, Panwar P, Bandettini PA, Gonzalez-Castillo J. A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. Neuroimage 2019; 202:116081. [PMID: 31419613 DOI: 10.1016/j.neuroimage.2019.116081] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/01/2019] [Accepted: 08/06/2019] [Indexed: 10/26/2022] Open
Abstract
This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (ΔR2⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ΔR2⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ΔR2⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.
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Affiliation(s)
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Puja Panwar
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA; Functional MRI Core, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
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21
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Wu H, Lu M, Zeng Y. State Estimation of Hemodynamic Model for fMRI Under Confounds: SSM Method. IEEE J Biomed Health Inform 2019; 24:804-814. [PMID: 31095502 DOI: 10.1109/jbhi.2019.2917093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introduces a state-space model with confounds, from a conventional hemodynamic model. In this model, a successive state estimation method requires a state value vector, an error covariance, an innovation covariance, and a cross covariance to be re-derived. Thus, a confounds square-root cubature Kalman smoothing (CSCKS) algorithm is proposed in this paper. We use a Balloon-Windkessel model to generate simulation data and add confounds signals to evaluate the performance of the proposed algorithm. The experiment results show that when the signal-to-interference ratio is less than 21 dB, the CSCKS proposed in this paper reduced estimation error to 16%, whereas the traditional algorithm reduced it to only 73%.
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22
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McCormick M, Reyna VF, Ball K, Katz JS, Deshpande G. Neural Underpinnings of Financial Decision Bias in Older Adults: Putative Theoretical Models and a Way to Reconcile Them. Front Neurosci 2019; 13:184. [PMID: 30930732 PMCID: PMC6427068 DOI: 10.3389/fnins.2019.00184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/15/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
- Michael McCormick
- Department of Psychology, Auburn University, Auburn, AL, United States
| | - Valerie F. Reyna
- Human Neuroscience Institute, Cornell University, Ithaca, NY, United States
- Department of Human Development, Cornell University, Ithaca, NY, United States
- Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, United States
- Magnetic Resonance Imaging Facility, Cornell University, Ithaca, NY, United States
| | - Karlene Ball
- Center for Research on Applied Gerontology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jeffrey S. Katz
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
| | - Gopikrishna Deshpande
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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23
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Cole MW, Ito T, Schultz D, Mill R, Chen R, Cocuzza C. Task activations produce spurious but systematic inflation of task functional connectivity estimates. Neuroimage 2018; 189:1-18. [PMID: 30597260 DOI: 10.1016/j.neuroimage.2018.12.054] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 12/12/2018] [Accepted: 12/26/2018] [Indexed: 01/21/2023] Open
Abstract
Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance - moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of task-evoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference "likely active or interacting during the task", rather than the intended inference "likely interacting during the task". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.
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Affiliation(s)
- Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA
| | - Douglas Schultz
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Ravi Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Richard Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA
| | - Carrisa Cocuzza
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA
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24
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Wheelock MD, Rangaprakash D, Harnett NG, Wood KH, Orem TR, Mrug S, Granger DA, Deshpande G, Knight DC. Psychosocial stress reactivity is associated with decreased whole-brain network efficiency and increased amygdala centrality. Behav Neurosci 2018; 132:561-572. [PMID: 30359065 PMCID: PMC6242743 DOI: 10.1037/bne0000276] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Cognitive and emotional functions are supported by the coordinated activity of a distributed network of brain regions. This coordinated activity may be disrupted by psychosocial stress, resulting in the dysfunction of cognitive and emotional processes. Graph theory is a mathematical approach to assess coordinated brain activity that can estimate the efficiency of information flow and determine the centrality of brain regions within a larger distributed neural network. However, limited research has applied graph-theory techniques to the study of stress. Advancing our understanding of the impact stress has on global brain networks may provide new insight into factors that influence individual differences in stress susceptibility. Therefore, the present study examined the brain connectivity of participants that completed the Montreal Imaging Stress Task (Goodman et al., 2016; Wheelock et al., 2016). Salivary cortisol, heart rate, skin conductance response, and self-reported stress served as indices of stress, and trait anxiety served as an index of participant's disposition toward negative affectivity. Psychosocial stress was associated with a decrease in the efficiency of the flow of information within the brain. Further, the centrality of brain regions that mediate emotion regulation processes (i.e., hippocampus, ventral prefrontal cortex, and cingulate cortex) decreased during stress exposure. Interestingly, individual differences in cortisol reactivity were negatively correlated with the efficiency of information flow within this network, whereas cortisol reactivity was positively correlated with the centrality of the amygdala within the network. These findings suggest that stress reduces the efficiency of information transfer and leaves the function of brain regions that regulate the stress response vulnerable to disruption. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
| | - Desphande Rangaprakash
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, AL, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Ca, USA
| | | | - Kimberly H. Wood
- Department of Psychology, University of Alabama at Birmingham, AL, USA
| | - Tyler R. Orem
- Department of Psychology, University of Alabama at Birmingham, AL, USA
| | - Sylvie Mrug
- Department of Psychology, University of Alabama at Birmingham, AL, USA
| | - Douglas A. Granger
- Institute for Interdisciplinary Salivary Bioscience Research & Center for the Neurobiology of Learning and Memory University of California, Irvine
- Johns Hopkins University School of Nursing, Johns Hopkins University Bloomberg School of Public Health, and Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, AL, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Ca, USA
- Department of Psychology, Auburn University, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, AL, USA
| | - David C. Knight
- Department of Psychology, University of Alabama at Birmingham, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at Birmingham, Birmingham, AL, USA
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25
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Rao NP, Deshpande G, Gangadhar KB, Arasappa R, Varambally S, Venkatasubramanian G, Ganagadhar BN. Directional brain networks underlying OM chanting. Asian J Psychiatr 2018; 37:20-25. [PMID: 30099280 DOI: 10.1016/j.ajp.2018.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 08/01/2018] [Accepted: 08/01/2018] [Indexed: 02/05/2023]
Abstract
OM chanting is an ancient technique of Indian meditation. OM chanting is associated with an experience of relaxation, changes in autonomic balance and deactivation of limbic brain regions. While functional localization is important, how brain regions interact with each other has been shown to underlie various brain functions. Therefore, in this study, we tested the hypothesis that there is reduced communication between deactivated regions during OM chanting. In order to do so, we employed multivariate autoregressive model (MVAR) based Granger causality to obtain directional connectivity between deactivated regions. fMRI scans of 12 right handed healthy volunteers (9 Men) from a previously published study was used in which participants performed OM chanting and a control condition in a block design. We found that outputs from insula, anterior cingulate and orbitofrontal cortices were significantly reduced in OM condition. Of interest is the reduction of outputs from these regions to limbic area amygdala. Modulation of brain regions involved in emotion processing and implicated in major depressive disorder (MDD) raises a potential possibility of OM chanting in the treatment of MDD.
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Affiliation(s)
- Naren P Rao
- National Institute of Mental Health and Neurosciences, Bangalore, India.
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | | | - Rashmi Arasappa
- National Institute of Mental Health and Neurosciences, Bangalore, India
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Fan X, Gaspard N, Legros B, Lucchetti F, Ercek R, Nonclercq A. Seizure evolution can be characterized as path through synaptic gain space of a neural mass model. Eur J Neurosci 2018; 48:3097-3112. [PMID: 30194874 DOI: 10.1111/ejn.14142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/08/2018] [Accepted: 08/29/2018] [Indexed: 11/30/2022]
Abstract
Physiologically based models could facilitate better understanding of mechanisms underlying epileptic seizures. In this paper, we attempt to reveal the dynamic evolution of intracranial EEG activity during epileptic seizures based on synaptic gain identification procedure of a neural mass model. The distribution of average excitatory, slow and fast inhibitory synaptic gain in the parameter space and their temporal evolution, i.e., the path through the model parameter space, were analyzed in thirty seizures from ten temporal lobe epileptic patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during seizure and returned to the plane when seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from the individual patient. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy.
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Affiliation(s)
- Xiaoya Fan
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Federico Lucchetti
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Laboratoire de Neurophysiologie Sensorielle et Cognitive, Hôpital Brugmann, Brussels, Belgium
| | - Rudy Ercek
- Laboratories of Image, Signal Processing and Acoustics (LISA), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
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Chen RH, Ito T, Kulkarni KR, Cole MW. The Human Brain Traverses a Common Activation-Pattern State Space Across Task and Rest. Brain Connect 2018; 8:429-443. [PMID: 29999413 PMCID: PMC6152856 DOI: 10.1089/brain.2018.0586] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional magnetic resonance imaging data, we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation-pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified "task-positive" and "task-negative" activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals "rested," suggesting task-focused activity may occur during rest. Together our results suggest that brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states potentially reflecting a wide variety of cognitive processes.
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Affiliation(s)
- Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey
| | - Kaustubh R. Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
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A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks. Brain Topogr 2018; 32:28-65. [PMID: 30076488 DOI: 10.1007/s10548-018-0666-3] [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: 11/08/2017] [Accepted: 07/28/2018] [Indexed: 10/28/2022]
Abstract
Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).
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Boureghda M, Bouden T. A deconvolution scheme for the stochastic metabolic/hemodynamic model (sMHM) based on the square root cubature Kalman filter and maximum likelihood estimation. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Croce P, Zappasodi F, Merla A, Chiarelli AM. Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data. J Neural Eng 2018; 14:046029. [PMID: 28504643 DOI: 10.1088/1741-2552/aa7321] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. APPROACH Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). MAIN RESULTS We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. SIGNIFICANCE The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.
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Affiliation(s)
- Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, 'G.dAnnunzio' University, Chieti, Italy. Institute of Advanced Biomedical Technologies, 'G.dAnnunzio' University, Chieti, Italy
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31
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Ramaihgari B, Pustovyy OM, Waggoner P, Beyers RJ, Wildey C, Morrison E, Salibi N, Katz JS, Denney TS, Vodyanoy VJ, Deshpande G. Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs. Front Vet Sci 2018; 5:127. [PMID: 30013977 PMCID: PMC6036133 DOI: 10.3389/fvets.2018.00127] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 05/23/2018] [Indexed: 01/01/2023] Open
Abstract
Prior functional Magnetic Resonance Imaging (fMRI) studies have indicated increased neural activation when zinc nanoparticles are added to odorants in canines. Here we demonstrate that zinc nanoparticles up-regulate directional brain connectivity in parts of the canine olfactory network. This provides an explanation for previously reported enhancement in the odor detection capability of the dogs in the presence of zinc nanoparticles. In this study, we obtained fMRI data from awake and unrestrained dogs while they were being exposed to odorants with and without zinc nanoparticles, zinc nanoparticles suspended in water vapor, as well as just water vapor alone. We obtained directional connectivity between the brain regions of the olfactory network that were significantly stronger for the condition of odorant + zinc nanoparticles compared to just odorants, water vapor + zinc nanoparticles and water vapor alone. We observed significant strengthening of the paths of the canine olfactory network in the presence of zinc nanoparticles. This result indicates that zinc nanoparticles could potentially be used to increase canine detection capabilities in the environments of very low concentrations of the odorants, which would have otherwise been undetected.
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Affiliation(s)
- Bhavitha Ramaihgari
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Oleg M. Pustovyy
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Paul Waggoner
- Canine Detection Research Institute, Auburn UniversityAuburn, AL, United States
| | - Ronald J. Beyers
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Chester Wildey
- MRRA Inc., University of Alabama at Birmingham, Euless, TX, United States
| | - Edward Morrison
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Nouha Salibi
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- MR Research and Development, Siemens Healthcare, Malvern, PA, United States
| | - Jeffrey S. Katz
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
| | - Thomas S. Denney
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
| | - Vitaly J. Vodyanoy
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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32
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Rangaprakash D, Bohon C, Lawrence KE, Moody T, Morfini F, Khalsa SS, Strober M, Feusner JD. Aberrant Dynamic Connectivity for Fear Processing in Anorexia Nervosa and Body Dysmorphic Disorder. Front Psychiatry 2018; 9:273. [PMID: 29997532 PMCID: PMC6028703 DOI: 10.3389/fpsyt.2018.00273] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 06/05/2018] [Indexed: 01/20/2023] Open
Abstract
Anorexia nervosa (AN) and body dysmorphic disorder (BDD) share distorted perceptions of appearance with extreme negative emotion, yet the neural phenotypes of emotion processing remain underexplored in them, and they have never been directly compared. We sought to determine if shared and disorder-specific fronto-limbic connectivity patterns characterize these disorders. FMRI data was obtained from three unmedicated groups: BDD (n = 32), weight-restored AN (n = 25), and healthy controls (HC; n = 37), while they viewed fearful faces and rated their own degree of fearfulness in response. We performed dynamic effective connectivity modeling with medial prefrontal cortex (mPFC), rostral anterior cingulate cortex (rACC), and amygdala as regions-of-interest (ROI), and assessed associations between connectivity and clinical variables. HCs exhibited significant within-group bidirectional mPFC-amygdala connectivity, which increased across the blocks, whereas BDD participants exhibited only significant mPFC-to-amygdala connectivity (P < 0.05, family-wise error corrected). In contrast, participants with AN lacked significant prefrontal-amygdala connectivity in either direction. AN showed significantly weaker mPFC-to-amygdala connectivity compared to HCs (P = 0.0015) and BDD (P = 0.0050). The mPFC-to-amygdala connectivity was associated with greater subjective fear ratings (R2 = 0.11, P = 0.0016), eating disorder symptoms (R2 = 0.33, P = 0.0029), and anxiety (R2 = 0.29, P = 0.0055) intensity scores. Our findings, which suggest a complex nosological relationship, have implications for understanding emotion regulation circuitry in these related psychiatric disorders, and may have relevance for current and novel therapeutic approaches.
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Affiliation(s)
- D. Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Katherine E. Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Teena Moody
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Francesca Morfini
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sahib S. Khalsa
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
- Laureate Institute for Brain Research, University of Tulsa, Tulsa, OK, United States
| | - Michael Strober
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jamie D. Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
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33
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Wang Y, Wang Y, Lui YW. Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis. Neuroimage 2018; 178:385-402. [PMID: 29782993 DOI: 10.1016/j.neuroimage.2018.05.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/23/2018] [Accepted: 05/17/2018] [Indexed: 11/16/2022] Open
Abstract
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.
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Affiliation(s)
- Yuan Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA.
| | - Yao Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA
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Madi MK, Karameh FN. Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling. J Neural Eng 2018; 15:046028. [PMID: 29749350 DOI: 10.1088/1741-2552/aac3f7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. APPROACH We herein introduce an adaptive framework in which optimal input design is integrated with square root cubature Kalman filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. MAIN RESULTS For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 ms in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. SIGNIFICANCE Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications.
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Affiliation(s)
- Mahmoud K Madi
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
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35
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med 2018; 80:1697-1713. [DOI: 10.1002/mrm.27146] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/26/2023]
Affiliation(s)
- D. Rangaprakash
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychiatry and Biobehavioral Sciences; University of California Los Angeles; Los Angeles California
| | - Guo-Rong Wu
- Department of Data Analysis; University of Ghent; Ghent Belgium
- Key Laboratory of Cognition and Personality, Southwest University; Chongqing China
| | | | - Xiaoping Hu
- Department of Bioengineering; University of California Riverside; Riverside California
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychology; Auburn University; Auburn Alabama
- Center for Health Ecology and Equity Research, Auburn University; Auburn Alabama
- Alabama Advanced Imaging Consortium, Auburn University, University of South Alabama and University of Alabama at Tuscaloosa and Birmingham; Alabama
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36
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Yan W, Rangaprakash D, Deshpande G. Aberrant hemodynamic responses in autism: Implications for resting state fMRI functional connectivity studies. Neuroimage Clin 2018; 19:320-330. [PMID: 30013915 PMCID: PMC6044186 DOI: 10.1016/j.nicl.2018.04.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/28/2018] [Accepted: 04/11/2018] [Indexed: 11/19/2022]
Abstract
Functional MRI (fMRI) is modeled as a convolution of the hemodynamic response function (HRF) and an unmeasured latent neural signal. However, HRF itself is variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. Aberrations in underlying neurochemical mechanisms, which control HRF shape, have been reported in autism spectrum disorders (ASD). Therefore, we hypothesized that this will lead to voxel-specific, yet systematic differences in HRF shape between ASD and healthy controls. As a corollary, we also hypothesized that such alterations will lead to differences in estimated functional connectivity in fMRI space compared to latent neural space. To test these hypotheses, we performed blind deconvolution of resting-state fMRI time series acquired from large number of ASD and control subjects obtained from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 1102). Many brain regions previously implicated in autism showed systematic differences in HRF shape in ASD. Specifically, we found that precuneus had aberrations in all HRF parameters. Consequently, we obtained precuneus-seed-based functional connectivity differences between ASD and controls using fMRI as well as using latent neural signals. We found that non-deconvolved fMRI data failed to detect group differences in connectivity between precuneus and certain brain regions that were instead observed in deconvolved data. Our results are relevant for the understanding of hemodynamic and neurochemical aberrations in ASD, as well as have methodological implications for resting-state functional connectivity studies in Autism, and more generally in disorders that are accompanied by neurochemical alterations that may impact HRF shape.
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Affiliation(s)
- Wenjing Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA.
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37
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Zambri B, Djellouli R, Laleg-Kirati TM. An efficient multistage algorithm for full calibration of the hemodynamic model from BOLD signal responses. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2875. [PMID: 28226417 DOI: 10.1002/cnm.2875] [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: 07/15/2016] [Revised: 11/10/2016] [Accepted: 02/19/2017] [Indexed: 06/06/2023]
Abstract
We propose a computational strategy that falls into the category of prediction/correction iterative-type approaches, for calibrating the hemodynamic model. The proposed method is used to estimate consecutively the values of the two sets of model parameters. Numerical results corresponding to both synthetic and real functional magnetic resonance imaging measurements for a single stimulus as well as for multiple stimuli are reported to highlight the capability of this computational methodology to fully calibrate the considered hemodynamic model.
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Affiliation(s)
- Brian Zambri
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, California State University, Northridge, CA 91330, USA
| | - Rabia Djellouli
- Department of Mathematics & Interdisciplinary Research Institute for the Sciences, California State University, Northridge, CA 91330, USA
| | - Taous-Meriem Laleg-Kirati
- Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA
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38
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Calhoun VD, de Lacy N. Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am 2017; 27:561-579. [PMID: 28985929 DOI: 10.1016/j.nic.2017.06.012.ten] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.
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Affiliation(s)
- Vince D Calhoun
- The Mind Research Network, 1101 Yale Boulevard Northeast, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, 1 University of New Mexico, Albuquerque, NM 87131, USA.
| | - Nina de Lacy
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA
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39
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Di X, Biswal BB. Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution. Front Neurosci 2017; 11:573. [PMID: 29089865 PMCID: PMC5651039 DOI: 10.3389/fnins.2017.00573] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 10/02/2017] [Indexed: 11/18/2022] Open
Abstract
Psychophysiological interaction (PPI) is a regression based method to study task modulated brain connectivity. Despite its popularity in functional MRI (fMRI) studies, its reliability and reproducibility have not been evaluated. We investigated reproducibility and reliability of PPI effects during a simple visual task, and examined the effect of deconvolution on the PPI results. A large open-access dataset was analyzed (n = 138), where a visual task was scanned twice with repetition times (TRs) of 645 and 1,400 ms, respectively. We first replicated our previous results by using the left and right middle occipital gyrus as seeds. Then regions of interest (ROI)-wise analysis was performed among 20 visual-related thalamic and cortical regions, and negative PPI effects were found between many ROIs with the posterior fusiform gyrus as a hub region. Both the seed-based and ROI-wise results were similar between the two runs and between the two PPI methods with and without deconvolution. The non-deconvolution method and the short TR run in general had larger effect sizes and greater extents. However, the deconvolution method performed worse in the 645 ms TR run than the 1,400 ms TR run in the voxel-wise analysis. Given the general similar results between the two methods and the uncertainty of deconvolution, we suggest that deconvolution may be not necessary for PPI analysis on block-designed data. Lastly, intraclass correlations (ICC) between the two runs were much lower for the PPI effects than the activation main effects, which raise cautions on performing inter-subject correlations and group comparisons on PPI effects.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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40
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Thompson GJ. Neural and metabolic basis of dynamic resting state fMRI. Neuroimage 2017; 180:448-462. [PMID: 28899744 DOI: 10.1016/j.neuroimage.2017.09.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 02/07/2023] Open
Abstract
Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.
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Affiliation(s)
- Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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41
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Calhoun VD, de Lacy N. Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am 2017; 27:561-579. [PMID: 28985929 DOI: 10.1016/j.nic.2017.06.012] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.
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Affiliation(s)
- Vince D Calhoun
- The Mind Research Network, 1101 Yale Boulevard Northeast, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, 1 University of New Mexico, Albuquerque, NM 87131, USA.
| | - Nina de Lacy
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA
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42
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Zhang K, Huang B, Zhang J, Glymour C, Schölkopf B. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI : PROCEEDINGS OF THE CONFERENCE 2017; 2017:1347-1353. [PMID: 28966540 PMCID: PMC5617646 DOI: 10.24963/ijcai.2017/187] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.
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Affiliation(s)
- Kun Zhang
- Department of philosophy, Carnegie Mellon University
| | - Biwei Huang
- Department of philosophy, Carnegie Mellon University
- MPI for Intelligent Systems, Tübingen, Germany
| | | | - Clark Glymour
- Department of philosophy, Carnegie Mellon University
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43
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Madi MK, Karameh FN. Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics. PLoS One 2017; 12:e0181513. [PMID: 28727850 PMCID: PMC5519212 DOI: 10.1371/journal.pone.0181513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 07/03/2017] [Indexed: 11/18/2022] Open
Abstract
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.
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Affiliation(s)
- Mahmoud K. Madi
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
| | - Fadi N. Karameh
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
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44
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Deshpande G, Rangaprakash D, Oeding L, Cichocki A, Hu XP. A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition. Front Neurosci 2017. [PMID: 28638316 PMCID: PMC5461249 DOI: 10.3389/fnins.2017.00246] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, USA.,Department of Psychology, Auburn UniversityAuburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama at BirminghamBirmingham, AL, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los AngelesLos Angeles, CA, USA
| | - Luke Oeding
- Department of Mathematics and Statistics, Auburn UniversityAuburn, AL, USA
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (Skoltech)Moscow, Russia.,Nicolaus Copernicus University (UMK)Torun, Poland.,Systems Research Institute, Polish Academy of ScienceWarsaw, Poland
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, RiversideRiverside, CA, USA
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45
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Chen Q, Wang W, Yin C, Jin X, Zhou J. Distributed cubature information filtering based on weighted average consensus. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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46
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Sreenivasan K, Zhuang X, Banks SJ, Mishra V, Yang Z, Deshpande G, Cordes D. Olfactory Network Differences in Master Sommeliers: Connectivity Analysis Using Granger Causality and Graph Theoretical Approach. Brain Connect 2017; 7:123-136. [PMID: 28125912 DOI: 10.1089/brain.2016.0458] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Previous studies investigating the differences in olfactory processing and judgments between trained sommeliers and controls have shown increased activations in brain regions involving higher level cognitive processes in sommeliers. However, there is little information about the influence of expertise on causal connectivity and topological properties of the connectivity networks between these regions. Therefore, the current study focuses on addressing these questions in a functional magnetic resonance imaging (fMRI) study of olfactory perception in Master Sommeliers. fMRI data were acquired from Master Sommeliers and control participants during different olfactory and nonolfactory tasks. Mean time series were extracted from 90 different regions of interest (ROIs; based on Automated Anatomical Labeling atlas). The underlying neuronal variables were extracted using blind hemodynamic deconvolution and then input into a dynamic multivariate autoregressive model to obtain connectivity between every pair of ROIs as a function of time. These connectivity values were then statistically compared to obtain paths that were significantly different between the two groups. The obtained connectivity matrices were further studied using graph theoretical methods. In sommeliers, significantly greater connectivity was observed in connections involving the precuneus, caudate, putamen, and several frontal and temporal regions. The controls showed increased connectivity from the left hippocampus to the frontal regions. Furthermore, the sommeliers exhibited significantly higher small-world topology than the controls. These findings are significant, given that learning about neuroplasticity in adulthood in these regions may then have added clinical importance in diseases such as Alzheimer's and Parkinson's where early neurodegeneration is isolated to regions important in smell.
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Affiliation(s)
| | - Xiaowei Zhuang
- 1 Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada
| | - Sarah J Banks
- 1 Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada
| | - Virendra Mishra
- 1 Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada
| | - Zhengshi Yang
- 1 Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada
| | - Gopikrishna Deshpande
- 2 Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University , Auburn, Alabama
- 3 Department of Psychology, Auburn University , Auburn, Alabama
- 4 Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham , Birmingham, Alabama
| | - Dietmar Cordes
- 1 Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada
- 5 Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado
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47
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Lacey S, Stilla R, Deshpande G, Zhao S, Stephens C, McCormick K, Kemmerer D, Sathian K. Engagement of the left extrastriate body area during body-part metaphor comprehension. BRAIN AND LANGUAGE 2017; 166:1-18. [PMID: 27951437 DOI: 10.1016/j.bandl.2016.11.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 09/13/2016] [Accepted: 11/17/2016] [Indexed: 06/06/2023]
Abstract
Grounded cognition explanations of metaphor comprehension predict activation of sensorimotor cortices relevant to the metaphor's source domain. We tested this prediction for body-part metaphors using functional magnetic resonance imaging while participants heard sentences containing metaphorical or literal references to body parts, and comparable control sentences. Localizer scans identified body-part-specific motor, somatosensory and visual cortical regions. Both subject- and item-wise analyses showed that, relative to control sentences, metaphorical but not literal sentences evoked limb metaphor-specific activity in the left extrastriate body area (EBA), paralleling the EBA's known visual limb-selectivity. The EBA focus exhibited resting-state functional connectivity with ipsilateral semantic processing regions. In some of these regions, the strength of resting-state connectivity correlated with individual preference for verbal processing. Effective connectivity analyses showed that, during metaphor comprehension, activity in some semantic regions drove that in the EBA. These results provide converging evidence for grounding of metaphor processing in domain-specific sensorimotor cortical activity.
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Affiliation(s)
- Simon Lacey
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Randall Stilla
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Auburn University & University of Alabama, Birmingham, AL, USA
| | - Sinan Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | | | - Kelly McCormick
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - David Kemmerer
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA; Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA
| | - K Sathian
- Department of Neurology, Emory University, Atlanta, GA, USA; Department of Rehabilitation Medicine, Emory University, Atlanta, GA, USA; Department of Psychology, Emory University, Atlanta, GA, USA; Rehabilitation R&D Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA, USA.
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48
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Mill RD, Bagic A, Bostan A, Schneider W, Cole MW. Empirical validation of directed functional connectivity. Neuroimage 2017; 146:275-287. [PMID: 27856312 PMCID: PMC5321749 DOI: 10.1016/j.neuroimage.2016.11.037] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 10/18/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022] Open
Abstract
Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established "sensory reactivation" effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ("raw" and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.
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Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, USA
| | - Anto Bagic
- Department of Neurology, University of Pittsburgh, USA
| | - Andreea Bostan
- Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA
| | - Walter Schneider
- Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA; Department of Psychology, University of Pittsburgh, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, USA; Center for Neuroscience, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, USA
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49
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Mill RD, Ito T, Cole MW. From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage 2017; 160:124-139. [PMID: 28131891 DOI: 10.1016/j.neuroimage.2017.01.060] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 12/17/2016] [Accepted: 01/25/2017] [Indexed: 11/30/2022] Open
Abstract
Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.
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Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA.
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
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50
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Miller RL, Vergara VM, Keator DB, Calhoun VD. A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow. IEEE Trans Biomed Eng 2016; 63:2525-2539. [PMID: 27541329 PMCID: PMC5737021 DOI: 10.1109/tbme.2016.2600637] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We introduce a method for analyzing dynamically changing functional magnetic resonance imaging (fMRI) network connectivity estimates as they vary within and between broad functional domains. The method captures evidence of intertemporal directionality in cross joint functional-domain influence and extends standard whole-brain dynamic network connectivity approaches into additional functionally meaningful dimensions by evaluating transition probabilities between clustered intradomain and interdomain connectivity patterns. Results: In applying this method to a large (N = 314) multisite resting-state fMRI dataset balanced between schizophrenia patients and healthy controls, we find evidence of joint functional domains that are global catalyzers, broadly shaping downstream functional relationships throughout the brain. Multiple interesting differences between patients and controls in both time-varying joint functional-domain connectivity patterns and in cross joint functional-domain intertemporal information flow were identified. Conclusion and Significance: Our proposed approach, thus, unifies the concepts of brain connectivity and interdomain connectivity and provides a powerful new way to evaluate functional connectivity data in the context of both the healthy and diseased brain.
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