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Huynh N, Deshpande G. A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders. Front Neurosci 2024; 18:1333712. [PMID: 38686334 PMCID: PMC11057233 DOI: 10.3389/fnins.2024.1333712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 02/19/2024] [Indexed: 05/02/2024] Open
Abstract
Structural and functional MRI (magnetic resonance imaging) based diagnostic classification using machine learning has long held promise, but there are many roadblocks to achieving their potential. While traditional machine learning models suffered from their inability to capture the complex non-linear mapping, deep learning models tend to overfit the model. This is because there is data scarcity and imbalanced classes in neuroimaging; it is expensive to acquire data from human subjects and even more so in clinical populations. Due to their ability to augment data by learning underlying distributions, generative adversarial networks (GAN) provide a potential solution to this problem. Here, we provide a methodological primer on GANs and review the applications of GANs to classification of mental health disorders from neuroimaging data such as functional MRI and showcase the progress made thus far. We also highlight gaps in methodology as well as interpretability that are yet to be addressed. This provides directions about how the field can move forward. We suggest that since there are a range of methodological choices available to users, it is critical for users to interact with method developers so that the latter can tailor their development according to the users' needs. The field can be enriched by such synthesis between method developers and users in neuroimaging.
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Affiliation(s)
- Nguyen Huynh
- Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad, India
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2
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Deshpande G, Zhao S, Waggoner P, Beyers R, Morrison E, Huynh N, Vodyanoy V, Denney TS, Katz JS. Two Separate Brain Networks for Predicting Trainability and Tracking Training-Related Plasticity in Working Dogs. Animals (Basel) 2024; 14:1082. [PMID: 38612321 PMCID: PMC11010877 DOI: 10.3390/ani14071082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months' post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.
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Affiliation(s)
- Gopikrishna Deshpande
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
- Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad 502285, India
| | - Sinan Zhao
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Paul Waggoner
- Canine Performance Sciences Program, College of Veterinary Medicine, Auburn University, Auburn, AL 36849, USA;
| | - Ronald Beyers
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Edward Morrison
- Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA; (E.M.); (V.V.)
| | - Nguyen Huynh
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Vitaly Vodyanoy
- Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA; (E.M.); (V.V.)
| | - Thomas S. Denney
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
| | - Jeffrey S. Katz
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Kumar S, Shovon AR, Deshpande G. The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI. Hum Brain Mapp 2023; 44:4637-4651. [PMID: 37449464 PMCID: PMC10400795 DOI: 10.1002/hbm.26403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 07/18/2023] Open
Abstract
There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.
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Affiliation(s)
- Sidharth Kumar
- Computer Science DepartmentUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | | | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research CenterAuburn UniversityAlabamaUSA
- Department of Psychological SciencesAuburn UniversityAuburnAlabamaUSA
- Alabama Advanced Imaging ConsortiumBirminghamAlabamaUSA
- Center for NeuroscienceAuburn UniversityAuburnAlabamaUSA
- School of PsychologyCapital Normal UniversityBeijingChina
- Key Laboratory for Learning and CognitionCapital Normal UniversityBeijingChina
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Centre for Brain ResearchIndian Institute of ScienceBangaloreIndia
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Demirayak P, DeCarlo D, Deshpande G, Denney T, Visscher K. Layer-specific functional changes associated with compensation for central vision loss due to macular degeneration. J Vis 2022. [DOI: 10.1167/jov.22.14.3713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Pinar Demirayak
- Civitan International Research Center, University of Alabama at Birmingham
- Department of Neurobiology, University of Alabama at Birmingham
| | - Dawn DeCarlo
- Ophthalmology & Visual Sciences, University of Alabama at Birmingham
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, Auburn University
- MRI Research Center, Auburn University
| | - Thomas Denney
- Department of Electrical and Computer Engineering, Auburn University
- MRI Research Center, Auburn University
| | - Kristina Visscher
- Civitan International Research Center, University of Alabama at Birmingham
- Department of Neurobiology, University of Alabama at Birmingham
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7
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Demirayak P, Deshpande G, Visscher K. Laminar functional magnetic resonance imaging in vision research. Front Neurosci 2022; 16:910443. [PMID: 36267240 PMCID: PMC9577024 DOI: 10.3389/fnins.2022.910443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) scanners at ultra-high magnetic fields have become available to use in humans, thus enabling researchers to investigate the human brain in detail. By increasing the spatial resolution, ultra-high field MR allows both structural and functional characterization of cortical layers. Techniques that can differentiate cortical layers, such as histological studies and electrode-based measurements have made critical contributions to the understanding of brain function, but these techniques are invasive and thus mainly available in animal models. There are likely to be differences in the organization of circuits between humans and even our closest evolutionary neighbors. Thus research on the human brain is essential. Ultra-high field MRI can observe differences between cortical layers, but is non-invasive and can be used in humans. Extensive previous literature has shown that neuronal connections between brain areas that transmit feedback and feedforward information terminate in different layers of the cortex. Layer-specific functional MRI (fMRI) allows the identification of layer-specific hemodynamic responses, distinguishing feedback and feedforward pathways. This capability has been particularly important for understanding visual processing, as it has allowed researchers to test hypotheses concerning feedback and feedforward information in visual cortical areas. In this review, we provide a general overview of successful ultra-high field MRI applications in vision research as examples of future research.
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Affiliation(s)
- Pinar Demirayak
- Civitan International Research Center, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Pinar Demirayak,
| | - 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
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory of 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
| | - Kristina Visscher
- Civitan International Research Center, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Korann V, Jacob A, Lu B, Devi P, Thonse U, Nagendra B, Maria Chacko D, Dey A, Padmanabha A, Shivakumar V, Dawn Bharath R, Kumar V, Varambally S, Venkatasubramanian G, Deshpande G, Rao NP. Effect of Intranasal Oxytocin on Resting-state Effective Connectivity in Schizophrenia. Schizophr Bull 2022; 48:1115-1124. [PMID: 35759349 PMCID: PMC9434443 DOI: 10.1093/schbul/sbac066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES Evidence from several lines of research suggests the critical role of neuropeptide oxytocin in social cognition and social behavior. Though a few studies have examined the effect of oxytocin on clinical symptoms of schizophrenia, the underlying neurobiological changes are underexamined. Hence, in this study, we examined the effect of oxytocin on the brain's effective connectivity in schizophrenia. METHODS 31 male patients with schizophrenia (SCZ) and 21 healthy male volunteers (HV) underwent resting functional magnetic resonance imaging scans with intra-nasal oxytocin (24 IU) and placebo administered in counterbalanced order. We conducted a whole-brain effective connectivity analysis using a multivariate vector autoregressive granger causality model. We performed a conjunction analysis to control for spurious changes and canonical correlation analysis between changes in connectivity and clinical and demographic variables. RESULTS Three connections, sourced from the left caudate survived the FDR correction threshold with the conjunction analysis; connections to the left supplementary motor area, left precentral gyrus, and left frontal inferior triangular gyrus. At baseline, SCZ patients had significantly weaker connectivity from caudate to these three regions. Oxytocin, but not placebo, significantly increased the strength of connectivity in these connections. Better cognitive insight and lower negative symptoms were associated with a greater increase in connectivity with oxytocin. CONCLUSIONS These findings provide a preliminary mechanistic understanding of the effect of oxytocin on brain connectivity in schizophrenia. The study findings provide the rationale to examine the potential utility of oxytocin for social cognitive deficits in schizophrenia.
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Affiliation(s)
| | | | - Bonian Lu
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Priyanka Devi
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Umesh Thonse
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Bhargavi Nagendra
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Dona Maria Chacko
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Avyarthana Dey
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Anantha Padmanabha
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Venkataram Shivakumar
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Rose Dawn Bharath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Vijay Kumar
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Shivarama Varambally
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Ganesan Venkatasubramanian
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | | | - Naren P Rao
- To whom correspondence should be addressed; tel: +91-80-26995879, e-mail:
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Deshpande G, Alluri V, Sharma A, Ingalhalikar M. Editorial: It Is a Matter of Matters: Deciphering Structural and Functional Brain Connectivity. Front Neurosci 2022; 16:951001. [PMID: 35784853 PMCID: PMC9245537 DOI: 10.3389/fnins.2022.951001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- 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
- *Correspondence: Gopikrishna Deshpande
| | - Vinoo Alluri
- Cognitive Science Laboratory, Indian Institute of Information Technology, Hyderabad, India
| | - Aaryana Sharma
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, India
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Deshpande G, Zhao X, Robinson J. Functional Parcellation of the Hippocampus based on its Layer-specific Connectivity with Default Mode and Dorsal Attention Networks. Neuroimage 2022; 254:119078. [PMID: 35276366 DOI: 10.1016/j.neuroimage.2022.119078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 01/29/2022] [Accepted: 03/07/2022] [Indexed: 12/25/2022] Open
Abstract
Recent neuroimaging evidence suggests that there might be an anterior-posterior functional differentiation of the hippocampus along the long-axis. The HERNET (hippocampal encoding/retrieval and network) model proposed an encoding/retrieval dichotomy with the anterior hippocampus more connected to the dorsal attention network (DAN) during memory encoding, and the posterior portions more connected to the default mode network (DMN) during retrieval. Evidence both for and against the HERNET model has been reported. In this study, we test the validity of the HERNET model non-invasively in humans by computing functional connectivity (FC) in layer-specific cortico-hippocampal microcircuits. This was achieved by acquiring sub-millimeter functional magnetic resonance imaging (fMRI) data during encoding/retrieval tasks at 7T. Specifically, FC between infra-granular output layers of DAN with hippocampus during encoding and FC between supra-granular input layers of DMN with hippocampus during retrieval were computed to test the predictions of the HERNET model. Our results support some predictions of the HERNET model including anterior-posterior gradient along the long axis of the hippocampus. While preferential relationships between the entire hippocampus and DAN/DMN during encoding/retrieval, respectively, were observed as predicted, anterior-posterior specificity in these network relationships could not be confirmed. The strength and clarity of evidence for/against the HERNET model were superior with layer-specific data compared to conventional volume data.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA; 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.
| | - Xinyu Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Quora Inc., Mountain View, CA, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA
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13
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Deshpande G, Wang Y, Robinson J. Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain. Brain Inform 2022; 9:2. [PMID: 35038072 PMCID: PMC8764001 DOI: 10.1186/s40708-021-00150-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022] Open
Abstract
Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, Birmingham, AL, USA. .,Center for Neuroscience, Auburn University, Auburn, AL, USA. .,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.
| | - Yun Wang
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychiatry, Columbia University, New York, NY, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychological Sciences, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
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14
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Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans Biomed Eng 2021; 68:3628-3637. [PMID: 33989150 PMCID: PMC8696194 DOI: 10.1109/tbme.2021.3080259] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. METHODS We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. RESULTS Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. CONCLUSION Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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15
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Robinson JL, Zhou X, Bird RT, Leavitt MJ, Nichols SJ, Blaine SK, Deshpande G. Neurofunctional Segmentation Shifts in the Hippocampus. Front Hum Neurosci 2021; 15:729836. [PMID: 34790106 PMCID: PMC8592061 DOI: 10.3389/fnhum.2021.729836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/04/2021] [Indexed: 11/24/2022] Open
Abstract
The hippocampus is one of the most phylogenetically preserved structures in the mammalian brain. Engaged in a host of diverse cognitive processes, there has been increasing interest in understanding how the hippocampus dynamically supports these functions. One of the lingering questions is how to reconcile the seemingly disparate cytoarchitectonic organization, which favors a dorsal-ventral layering, with the neurofunctional topography, which has strong support for longitudinal axis (anterior-posterior) and medial-lateral orientation. More recently, meta-analytically driven (e.g., big data) approaches have been employed, however, the question remains whether they are sensitive to important task-specific features such as context, cognitive processes recruited, or the type of stimulus being presented. Here, we used hierarchical clustering on functional magnetic resonance imaging (fMRI) data acquired from healthy individuals at 7T using a battery of tasks that engage the hippocampus to determine whether stimulus or task features influence cluster profiles in the left and right hippocampus. Our data suggest that resting state clustering appears to favor the cytoarchitectonic organization, while task-based clustering favors the neurofunctional clustering. Furthermore, encoding tasks were more sensitive to stimulus type than were recognition tasks. Interestingly, a face-name paired associate task had nearly identical clustering profiles for both the encoding and recognition conditions of the task, which were qualitatively morphometrically different than simple encoding of words or faces. Finally, corroborating previous research, the left hippocampus had more stable cluster profiles compared to the right hippocampus. Together, our data suggest that task-based and resting state cluster profiles are different and may account for the disparity or inconsistency in results across studies.
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Affiliation(s)
- Jennifer L Robinson
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States.,Department of Electrical and Computer Engineering, Auburn University Magnetic Resonance Imaging Research Center, Auburn University, Auburn, AL, United States.,Center for Neuroscience Initiative, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States
| | - Xinyu Zhou
- Department of Electrical and Computer Engineering, Auburn University Magnetic Resonance Imaging Research Center, Auburn University, Auburn, AL, United States.,Quora Inc., Mountain View, CA, United States
| | - Ryan T Bird
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
| | - Mackenzie J Leavitt
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
| | - Steven J Nichols
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
| | - Sara K Blaine
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States.,Center for Neuroscience Initiative, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States
| | - Gopikrishna Deshpande
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States.,Department of Electrical and Computer Engineering, Auburn University Magnetic Resonance Imaging Research Center, Auburn University, Auburn, AL, United States.,Center for Neuroscience Initiative, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States.,Key Lab for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.,Center for Brain Research, Indian Institute of Science, Bengaluru, India
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16
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Behere RV, Deshpande G, Bandyopadhyay SK, Yajnik C. Maternal vitamin B 12, folate during pregnancy and neurocognitive outcomes in young adults of the Pune Maternal Nutrition Study (PMNS) prospective birth cohort: study protocol. BMJ Open 2021; 11:e046242. [PMID: 34551940 PMCID: PMC8461273 DOI: 10.1136/bmjopen-2020-046242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION The Developmental Origins of Health and Disease (DOHaD) hypothesis proposes that intrauterine and early life exposures significantly influence fetal development and risk for disease in later life. Evidence from prospective birth cohorts suggests a role for maternal B12 and folate in influencing neurocognitive outcomes in the offspring. In the Indian setting, B12 deficiency is common during the pregnancy while rates of folate deficiency are lower. The long-term influences of maternal nutrition during the pregnancy on adult neurocognitive outcomes have not been examined. The Pune Maternal Nutrition Study (PMNS) is a preconceptional birth cohort into its 24th year and is considered a unique resource to study the DOHaD hypothesis. We found an association between maternal B12 status in pregnancy and child's neurocognitive status at 9 years of age. We now plan to assess neurocognitive function and MRI measurements of brain structural-functional connectivity at young adult age to study its association with maternal nutritional exposures during the pregnancy. METHODS AND ANALYSIS As part of ongoing prospective follow-up in young adults of the PMNS at the Diabetes Unit, KEM Hospital Research Center, Pune India, the following measurements will be done: neurocognitive performance (Standardised Tests of Intelligence, Verbal and Visual Memory, Attention and Executive Functions), temperament (Adult Temperament Questionnaire), psychopathology (Brief Symptom Inventory and Clinical Interview on Mini Neuropsychiatric Interview 7.0). Brain MRI for structural T1, resting-state functional connectivity and diffusion tensor imaging will be performed on a subset of the cohort (selected based on exposure to a lower or higher maternal B12 status at 18 weeks of pregnancy). ETHICS AND DISSEMINATION The study is approved by Institutional ethics committee of KEM Hospital Research Center, Pune. The results will be shared at national and international scientific conferences and published in peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER NCT03096028.
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Affiliation(s)
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
- Center for Neuroscience, Auburn University, Auburn, Alabama, USA
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17
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Wang J, Xu Y, Deshpande G, Li K, Sun P, Liang P. The Effect of Light Sedation with Midazolam on Functional Connectivity of the Dorsal Attention Network. Brain Sci 2021; 11:brainsci11081107. [PMID: 34439725 PMCID: PMC8392174 DOI: 10.3390/brainsci11081107] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 11/19/2022] Open
Abstract
Altered connectivity within and between the resting-state networks (RSNs) brought about by anesthetics that induce altered consciousness remains incompletely understood. It is known that the dorsal attention network (DAN) and its anticorrelations with other RSNs have been implicated in consciousness. However, the role of DAN-related functional patterns in drug-induced sedative effects is less clear. In the current study, we investigated altered functional connectivity of the DAN during midazolam-induced light sedation. In a placebo-controlled and within-subjects experimental study, fourteen healthy volunteers received midazolam or saline with a 1-week interval. Resting-state fMRI data were acquired before and after intravenous drug administration. A multiple region of interest-driven analysis was employed to investigate connectivity within and between RSNs. It was found that functional connectivity was significantly decreased by midazolam injection in two regions located in the left inferior parietal lobule and the left middle temporal area within the DAN as compared with the saline condition. We also identified three clusters in anticorrelation between the DAN and other RSNs for the interaction effect, which included the left medial prefrontal cortex, the right superior temporal gyrus, and the right superior frontal gyrus. Connectivity between all regions and DAN was significantly decreased by midazolam injection. The sensorimotor network was minimally affected. Midazolam decreased functional connectivity of the dorsal attention network. These findings advance the understanding of the neural mechanism of sedation, and such functional patterns might have clinical implications in other medical conditions related to patients with cognitive impairment.
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Affiliation(s)
- Junkai Wang
- Department of Psychology, Tsinghua University, Haidian District, Beijing 100084, China;
| | - Yachao Xu
- Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China;
| | - Gopikrishna Deshpande
- School of Psychology, Capital Normal University, Haidian District, Beijing 100048, China;
- Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing 100048, China
- 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, Birmingham, AL 35233, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 560030, India
- Center for Brain Research, Indian Institute of Science, Bangalore 560012, India
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China;
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing 100053, China
| | - Pei Sun
- Department of Psychology, Tsinghua University, Haidian District, Beijing 100084, China;
- Correspondence: (P.S.); (P.L.)
| | - Peipeng Liang
- School of Psychology, Capital Normal University, Haidian District, Beijing 100048, China;
- Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing 100048, China
- Correspondence: (P.S.); (P.L.)
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18
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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 DOI: 10.1007/s11682-020-00358-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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19
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Behere R, Deshpande G, Shah A, Dsouza N, Yajnik C. Intrauterine Exposure to Vitamin B12 and Folate Imbalance and Brain Structure in Young Adults of the Pune Maternal Nutrition Study (PMNS) Birth Cohort. Curr Dev Nutr 2021. [DOI: 10.1093/cdn/nzab049_007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Objectives
Intrauterine nutritional exposures to vitamin B12 and folate are known to influence neurodevelopment. We tested the hypothesis that intrauterine exposure to high folate in presence of low vitamin B12 is associated with poor neurodevelopmental outcomes in young adult offspring.
Methods
PMNS is a preconceptional birth cohort in its 24th year of follow up. We examined Intelligence Quotient (IQ) and obtained brain structural MPRAGE T1 sequence on a 3T MRI Scanner [participants selected based on exposure to maternal B12 at 18 wk pregnancy <103 PM(Low maternal B12 group n = 97) and >175 pM (high maternal B12 group, n = 93)]. Brain morphometric measurements (cortical volumes, thickness and subcortical volumes) were performed on Freesurfer software.
Results
The mean age of participants was 22.3 ± 0.5 years (n = 190, 96 boys). High maternal B12 group showed greater cortical thickness in temporal regions (P < 0.001) and cortical thinning in frontal regions (P < 0.01). Higher maternal folate (median Red Cell Folate at 28 wk pregnancy 420 ng/ml) was associated with greater frontal cortical volumes in the high maternal B12 group but cortical thinning in multiple temporal and parietal cortical areas in the low B12 group(P < 0.05). There was a significant interaction between maternal B12 and folate status in relation to subcortical volumes. Exposure to higher maternal folate was associated with greater subcortical volume in high B12 group but lower volumes in low B12 group (P = 0.02, adjusted for age, education, gender and total intracranial volume). Higher IQ score was associated with larger subcortical brain volume (r = 0.34, P < 0.001) in the offspring.
Conclusions
Intrauterine exposure to higher maternal folate in the presence of low vitamin B12 was associated with poorer cortical development in young adult offspring. Higher maternal folate with adequate B12 benefits subcortical brain development which reflects in higher IQ score. Optimizing maternal vitamin B12 and folate concentrations during pregnancy would benefit neurocognitive development in the offspring.
Funding Sources
DBT Wellcome India Alliance Clinical and Public Health Intermediate Fellowship.
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Affiliation(s)
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University
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20
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Krueger F, Mitchell KC, Deshpande G, Katz JS. Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review. Anim Cogn 2021; 24:371-385. [PMID: 33486634 PMCID: PMC7826496 DOI: 10.1007/s10071-021-01472-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/16/2020] [Accepted: 01/02/2021] [Indexed: 01/31/2023]
Abstract
Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to robots. However, without a clear theoretical framework-building on biological, psychological, and technological knowledge-current societal demands for establishing successful human-robot attachment (HRA) as a new form of inter-species interactions might fail. The study of evolutionarily adaptive animal behavior (i.e., ethology) suggests that human-animal behaviors can be considered as a plausible solution in designing and building models of ethorobots-including modeling the inter-species bond between domesticated animals and humans. Evidence shows that people assign emotional feelings and personality characteristics to animal species leading to cooperation and communication-crucial for designing social robots such as companion robots. Because dogs have excellent social skills with humans, current research applies human-dog relationships as a template to understand HRA. Our goal of this article is twofold. First, we overview the research on how human-dog interactions are implemented as prototypes of non-human social companions in HRA. Second, we review research about attitudes that humans have for interacting with robotic dogs based on their appearance and behavior, the implications for forming attachments, and human-animal interactions in the rising sphere of robot-assisted therapy. The rationale for this review is to provide a new perspective to facilitate future research among biologists, psychologists, and engineers-contributing to the creation of innovative research practices for studying social behaviors and its implications for society addressing HRA.
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Affiliation(s)
- Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA.
- Department of Psychology, George Mason University, Fairfax, VA, USA.
- Institute for Biohealth Innovation, George Mason University, Fairfax, VA, USA.
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, USA.
| | - Kelsey C Mitchell
- School of Systems Biology, George Mason University, Fairfax, VA, USA
| | - 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, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, 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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Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav 2020; 14:2378-2416. [PMID: 31691160 PMCID: PMC7198352 DOI: 10.1007/s11682-019-00191-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini .
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychological Sciences, University of California Merced, Merced, CA, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
- US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McCord, WA, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA.
- Department of Psychology, Auburn University, Auburn, AL, USA.
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
- Center for Neuroscience, Auburn University, Auburn, AL, USA.
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
- Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.
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22
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Rangaprakash D, Odemuyiwa T, Narayana Dutt D, Deshpande G. Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment. Brain Inform 2020; 7:19. [PMID: 33242116 PMCID: PMC7691406 DOI: 10.1186/s40708-020-00120-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 11/29/2022] Open
Abstract
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Toluwanimi Odemuyiwa
- Division of Engineering Science, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada
| | - D Narayana Dutt
- Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, 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.
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23
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Proessl F, Dretsch MN, Connaboy C, Lovalekar M, Dunn-Lewis C, Canino MC, Sterczala AJ, Deshpande G, Katz JS, Denney TS, Flanagan SD. Structural Connectome Disruptions in Military Personnel with Mild Traumatic Brain Injury and Post-Traumatic Stress Disorder. J Neurotrauma 2020; 37:2102-2112. [DOI: 10.1089/neu.2020.6999] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Felix Proessl
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael N. Dretsch
- U.S. Army Medical Research Directorate-West, Walter Reed Army Institute of Research, Joint Base Lewis-McChord, Washington, USA
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama, USA
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
| | - Chris Connaboy
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Mita Lovalekar
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Courtenay Dunn-Lewis
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Maria C. Canino
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Adam J. Sterczala
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gopikrishna Deshpande
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
- Alabama Advanced Imaging Consortium, Alabama, USA
- Center for Neuroscience, Auburn University, Auburn, Alabama, USA
- School of Psychology, Capital Normal University, Beijing, China
| | - Jeffrey S. Katz
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
- Alabama Advanced Imaging Consortium, Alabama, USA
- Center for Neuroscience, Auburn University, Auburn, Alabama, USA
| | - Thomas S. Denney
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
- Alabama Advanced Imaging Consortium, Alabama, USA
- Center for Neuroscience, Auburn University, Auburn, Alabama, USA
| | - Shawn D. Flanagan
- Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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24
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Chaitra N, Vijaya P, Deshpande G. Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102099] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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25
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Lloyd TD, Neal‐Smith G, Fennelly J, Claireaux H, Bretherton C, Carr AJ, Murphy M, Kendrick BJ, Palmer AJR, Wong J, Sharma P, Osei‐Bonsu PK, Ashcroft G, Baigent T, Shirland E, Espey R, Stokes M, Liew I, Dhawal A, Watchorn D, Lum J, Qureshi M, Khaled AS, Kauser S, Hodhody G, Rogers S, Haywood‐Alexander B, Sheikh G, Mahapatra P, Twaij H, Chicco M, Arnaout F, Atherton T, Mutimer J, Sinha P, Oliver E, Stedman T, Gadd R, Kutuzov V, Sattar M, Robiati L, Plastow R, Howe T, Hassan A, Lau B, Collins J, Doshi A, Tan G, Baskaran D, Hari Sunil Kumar K, Agarwal R, Horner M, Gwyn R, Masud S, Beaumont O, Pilarski A, Lebe M, Dawson‐Bowling S, Nolan D, Tsitskaris K, Beamish RE, Jordan C, Alsop S, Hibbert E, Deshpande G, Gould A, Briant‐Evans T, Kilbane L, Crowther I, Ingoe H, Naisbitt A, Gourbault L, Muscat J, Goh EL, Gill J, Elbashir M, Modi N, Archer J, Ismael S, Petrie M, O'Brien H, McCormick M, Koh NP, Lloyd T, King A, Ikram A, Peake J, Yoong A, Rye DS, Newman M, Naraen A, Myatt D, Kapur R, Sgardelis P, Kohli S, Culverhouse‐Mathews M, Haynes S, Boden H, Purmah A, Shenoy R, Raja S, Koh NP, Donovan R, Yeomans D, Ritchie D, Larkin R, Aladwan R, Hughes K, Unsworth R, Cooke R, Samra I, Barrow J, Michael K, Byrne F, Anwar R, Karatzia L, Drysdale H, Wilson H, Jones R, Dass D, Liaw F, Aujla R, Kheiran A, Bell K, Ramavath AL, Telfer R, Nachev K, Lawrence H, Garg V, Shenoy P, Lacey A, Byrom I, Simons M, Manning C, Cheyne N, Williams J. Peri‐operative administration of tranexamic acid in lower limb arthroplasty: a multicentre, prospective cohort study. Anaesthesia 2020; 75:1050-1058. [DOI: 10.1111/anae.15056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2020] [Indexed: 12/20/2022]
Affiliation(s)
- T. D. Lloyd
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
- Oxford Surgical Collaborative for Audit and Research Oxford UK
| | - G. Neal‐Smith
- Oxford Surgical Collaborative for Audit and Research Oxford UK
| | - J. Fennelly
- Oxford Surgical Collaborative for Audit and Research Oxford UK
| | - H. Claireaux
- Oxford Surgical Collaborative for Audit and Research Oxford UK
| | - C. Bretherton
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
- Oxford Surgical Collaborative for Audit and Research Oxford UK
| | - A. J. Carr
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
| | - M. Murphy
- University of Oxford UK
- NHS Blood and Transplant Oxford UK
| | - B. J. Kendrick
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
| | - A. J. R. Palmer
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK
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26
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Gururaja C, Rangaprakash D, Deshpande G. Research Trends in the Application of Yoga to Human Health: A Data Science Approach. Int J Public Ment Health Neurosci 2020; 7:8-13. [PMID: 34553079 PMCID: PMC8455111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Yoga is an integrative mind-body system of wellbeing developed in India since at least three millennia. Yoga has gained considerable attention in recent decades, partly driven by recent research and evidence about its effectiveness. In this work, we extracted research trends on the effects of Yoga on human health from the US National Library of Medicine's PubMed database (peer-reviewed journal papers). We found that Yoga research spans all organ systems and system-wide issues such as pain and cancer. Research on the nervous system far outpaces other systems, which is expected because of the effects of breathing and exercise on stress reduction, which has been a major application of Yoga. The next cluster of impact concerns the musculoskeletal system and pain (both related to the exercise [asana] aspects of Yoga), as well as cardiovascular/endocrine (also related to stress) and cancer. Stress and mental health, pain, diabetes, and cancer are health issues for which a permanent cure is not available in a majority of cases in modern medicine, although alleviating treatments are available. This has probably fueled interest in complementary approaches such as Yoga for these health issues. Research timeline shows that Yoga-related research largely expanded only after the 2000s. There was a specific uptick after 2004. Similar trends are seen if we look at just clinical trials or randomized control trials (RCTs) or systematic reviews. The percentage of trials (Clinical and RCT) among published literature is around 10-15 % This is comparable to other fields that gained traction around 2000s (e.g. non-invasive brain stimulation). Geographical distribution shows that 37% of all Yoga related research output originates in the USA, 19% from India, 13% from Europe and 31% from the rest of the world. Therefore, the interest is widespread and global. At least the uptick in Yoga-related research in the US post-2000s can be attributed to a substantial jump in funding between 1998 and 2005 from US National Institutes of Health's National Center for Complementary and Integrative Health (NCCIH). We can only surmise that research in this field reached a critical mass in late-1990s, which infused more money into this field, generating more research and creating a positive feedback loop that has sustained the growth so far. We propose that in order to sustain or even accelerate future research in the area, rigor and reproducibility must be enhanced in addition to performing more RCT and clinical trials (increasing % of trials to 20-25% from 10-15%). The fruits of research in the field has to reach the common man in terms of evidence-based solutions to health issues. Without this, accelerated funding in democracies such as India and the USA will not be realizable.
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Affiliation(s)
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA,Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA,Department of Psychology, Auburn University, Auburn, Alabama, USA,Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA,Center for Health Ecology and Equity Research, Auburn University, Auburn, 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
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27
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Deshpande G, Jia H. Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance. Front Neurosci 2020; 13:1448. [PMID: 32116487 PMCID: PMC7017718 DOI: 10.3389/fnins.2019.01448] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/27/2019] [Indexed: 11/18/2022] Open
Abstract
Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.
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Affiliation(s)
- Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
| | - Hao Jia
- Department of Automation, College of Information Engineering, Taiyuan University of Technology, Taiyuan, China
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28
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Lanka P, Rangaprakash D, Gotoor SSR, Dretsch MN, Katz JS, Denney TS, Deshpande G. MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data. Data Brief 2020; 29:105213. [PMID: 32090157 PMCID: PMC7025186 DOI: 10.1016/j.dib.2020.105213] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/20/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled “Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets” [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample.
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychological Sciences, University of California Merced, Merced, CA, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Health Science and Technology, Massachusetts Institute of Technology, Boston, MA, USA
| | - Sai Sheshan Roy Gotoor
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.,US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, USA.,Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- 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, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Thomas S Denney
- 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, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, 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, Birmingham, AL, USA.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA.,Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
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29
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Syed MA, Yang Z, Rangaprakash D, Hu X, Dretsch MN, Katz JS, Denney TS, Deshpande G. DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders. Neuroinformatics 2020; 18:87-107. [PMID: 31187352 PMCID: PMC6904532 DOI: 10.1007/s12021-019-09422-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.
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Affiliation(s)
- Mohammed A Syed
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
- The Boeing Company, Seattle, WA, USA
| | - Zhi Yang
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
- US Army Medical Research Directorate-West, Joint Base Lewis-McCord, Tacoma, WA, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Birmingham, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Birmingham, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.
- Department of Psychology, Auburn University, Auburn, AL, USA.
- Center for Neuroscience, Auburn University, Birmingham, AL, USA.
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
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30
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Palaniyappan L, Deshpande G, Lanka P, Rangaprakash D, Iwabuchi S, Francis S, Liddle PF. Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level. Schizophr Res 2019; 214:24-33. [PMID: 29398207 DOI: 10.1016/j.schres.2018.01.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 01/08/2018] [Accepted: 01/11/2018] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Schizophrenia spectrum disorders (SSD) and psychotic bipolar disorder share a number of genetic and neurobiological features, despite a divergence in clinical course and outcome trajectories. We studied the diagnostic classification potential that can be achieved on the basis of the structure and connectivity within a triple network system (the default mode, salience and central executive network) in patients with SSD and psychotic bipolar disorder. METHODS Directed static connectivity and its dynamic variance was estimated among 8 nodes of the three large-scale networks. Multivariate autoregressive models of deconvolved resting state functional magnetic resonance imaging time series were obtained from 57 patients (38 with SSD and 19 with bipolar disorder and psychosis). We used 2/3 of the patients for training and validation of the classifier and the remaining 1/3 as an independent hold-out test data for performance estimation. RESULTS A high level of discrimination between bipolar disorder with psychosis and SSD (combined balanced accuracy = 96.2%; class accuracies 100% for bipolar and 92.3% for SSD) was achieved when effective connectivity and morphometry of the triple network nodes was combined with symptom scores. Patients with SSD were discriminated from patients with bipolar disorder and psychosis as showing higher clinical severity of disorganization and higher variability in the effective connectivity between salience and executive networks. CONCLUSIONS Our results support the view that the study of network-level connectivity patterns can not only clarify the pathophysiology of SSD but also provide a measure of excellent clinical utility to identify discrete diagnostic/prognostic groups among individuals with psychosis.
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Affiliation(s)
- Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada.
| | - 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.
| | - Pradyumna Lanka
- 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
| | - Sarina Iwabuchi
- Centre for Translational Neuroimaging, Division of Psychiatry & Applied Psychology, Institute of Mental Health, University of Nottingham, UK
| | - Susan Francis
- Sir Peter Mansfield MR Centre, University of Nottingham, UK
| | - Peter F Liddle
- Centre for Translational Neuroimaging, Division of Psychiatry & Applied Psychology, Institute of Mental Health, University of Nottingham, UK
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31
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Feng C, Feng X, Wang L, Wang L, Gu R, Ni A, Deshpande G, Li Z, Luo YJ. The neural signatures of egocentric bias in normative decision-making. Brain Imaging Behav 2019; 13:685-698. [PMID: 29767303 DOI: 10.1007/s11682-018-9893-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Bargaining parties often disagree on what fair is, due to the reason that people are prone to believe that what favors oneself is fair, i.e., an egocentric bias. In this study, we investigated the neural signatures underlying egocentric bias in fairness decision-making, conjoining an adapted ultimatum game (UG) with event-related fMRI and functional connectivity. Participants earned monetary rewards with a partner in a production stage, wherein their contributions to the earnings were manipulated. Afterwards, the joint earnings were randomly divided, and the distribution was presented simultaneously with contribution information to participants, who accepted/rejected distributions of earnings as the same manner in standard UG. We identified an egocentric bias in fairness decisions, such that participants frequently rejected self-contributed disadvantageous outcomes, but much less so in response to other-contributed advantageous outcomes, although both involved mismatch between contribution and payoff. This bias was underpinned by regions involved in representing fairness norms, including the anterior insula and dorsal anterior cingulate cortex (dACC). Furthermore, the thalamus activity was predictive of the bias, such that the level of egocentric bias decreased as a function of the activation level of the thalamus. Finally, our functional-connectivity findings indicated that the thalamus worked together with insula and dACC to modulate behavioral egocentric bias in fairness-related decisions. Our findings uncover the neural basis underlying the modulation of egocentric bias in normative decision-making, and highlight the role of neural circuits associated with norm enforcement in this phenomenon.
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Affiliation(s)
- Chunliang Feng
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China.,College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xue Feng
- Key Laboratory of Modern Teaching Technology of Ministry of Education, Shaanxi Normal University, Xi'an, China
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Lili Wang
- School of Educational Science, Huaiyin Normal University, Huai'an, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Aiping Ni
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, Auburn University MRI Research Center, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Auburn, AL, USA
| | - Zhihao Li
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China.
| | - Yue-Jia Luo
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China. .,Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China.
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32
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Abstract
We present a canine functional Magnetic Resonance Imaging (fMRI) training protocol that can be done in a cost-effective manner, with high-energy dogs, for acquisition of functional and structural data. This method of training dogs for awake, unrestrained fMRI employs a generalization procedure of stationing in several dissimilar locations to facilitate transfer of the stationing behavior to the real MRI scan environment; it does so without the need for extensive training time in the MRI scan environment, which can be expensive. Further, this method splits the training of a stationing (i.e., chin rest) behavior from desensitization to the MRI environment (i.e., 100+ decibel scan audio), the latter accomplished during dedicated Auditory Exposure conditioning sessions. The complete training and testing protocol required 14 hours and resulted in immediate transfer to novel locations. We also present examples of canine fMRI data that have been acquired from visual face processing and olfactory discrimination paradigms.
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Affiliation(s)
| | - L Paul Waggoner
- Canine Performance Sciences Program, College of Veterinary Medicine, Auburn University
| | - Gopikrishna Deshpande
- Department of Psychology, Auburn University; AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University; Alabama Advanced Imaging Consortium; Center for Neuroscience, Auburn University
| | - Jeffrey S Katz
- Department of Psychology, Auburn University; AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University; Alabama Advanced Imaging Consortium; Center for Neuroscience, Auburn University;
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33
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Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Front Neurosci 2019; 13:803. [PMID: 31507353 PMCID: PMC6716456 DOI: 10.3389/fnins.2019.00803] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/17/2019] [Indexed: 01/08/2023] Open
Abstract
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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Affiliation(s)
- D Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Thomas S Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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34
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Lanka P, Deshpande G. Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data. Brain Behav 2019; 9:e01341. [PMID: 31297966 PMCID: PMC6710196 DOI: 10.1002/brb3.1341] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/20/2019] [Accepted: 05/23/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Head movement in the scanner causes spurious signal changes in the blood-oxygen-level-dependent (BOLD) signal, confounding resting state functional connectivity (RSFC) estimates obtained from functional magnetic resonance imaging (fMRI). We examined the effectiveness of Prospective Acquisition CorrEction (PACE) in reducing motion artifacts in BOLD data. METHODS Using PACE-corrected RS-fMRI data obtained from 44 subjects and subdividing them into low- and high-motion cohorts, we investigated voxel-wise motion-BOLD relationships, the distance-dependent functional connectivity artifact and the correlation between head motion and connectivity metrics such as posterior cingulate seed-based connectivity and network degree centrality. RESULTS Our results indicate that, when PACE is used in combination with standard retrospective motion correction strategies, it provides two principal advantages over conventional echo-planar imaging (EPI) RS-fMRI data: (a) PACE was effective in eliminating significant negative motion-BOLD relationships, shown to be associated with signal dropouts caused by head motion, and (b) Censoring with a lower threshold (framewise displacement >0.5 mm) and a smaller window around the motion corrupted time point provided qualitatively equivalent reductions in the motion artifact with PACE when compared to a more conservative threshold of 0.2 mm required with conventional EPI data. CONCLUSIONS PACE when used in conjunction with retrospective motion correction methods including nuisance signal and motion parameter regression, and censoring, did prove effective in almost eliminating head motion artifacts, even with a lower censoring threshold. Use of a lower censoring threshold could provide substantial savings in data that would otherwise be lost to censoring. Three-dimensional PACE has negligible overhead in terms of scan time, sequence modifications or additional and hence presents an attractive option for head motion correction in high-throughput resting-state BOLD imaging.
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Affiliation(s)
- Pradyumna Lanka
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, Alabama.,Department of Psychological Sciences, University of California, Merced, California
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, 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 and University of Alabama Birmingham, Alabama.,Center for Neuroscience, Auburn University, Auburn, Alabama.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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35
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Dretsch MN, Rangaprakash D, Katz JS, Daniel TA, Goodman AM, Denney TS, Deshpande G. Strength and Temporal Variance of the Default Mode Network to Investigate Chronic Mild Traumatic Brain Injury in Service Members with Psychological Trauma. J Exp Neurosci 2019; 13:1179069519833966. [PMID: 30911222 PMCID: PMC6423682 DOI: 10.1177/1179069519833966] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/05/2019] [Indexed: 11/21/2022] Open
Abstract
Background: There is a significant number of military personnel with a history of mild
traumatic brain injury (mTBI) who suffer from comorbid posttraumatic stress
symptoms (PTS). Although there is evidence of disruptions of the default
mode network (DMN) associated with PTS and mTBI, previous studies have only
studied static connectivity while ignoring temporal variability of
connectivity. Objective: To assess DMN disrupted or dysregulated neurocircuitry, cognitive
functioning, and psychological health of active-duty military with mTBI and
PTS. Method: U.S. Army soldiers with PTS (n = 14), mTBI + PTS (n = 25), and healthy
controls (n = 21) voluntarily completed a cognitive and symptom battery. In
addition, participants had magnetic resonance imaging (MRI) to assess both
static functional connectivity (SFC) and variance of dynamic functional
connectivity (vDFC) of the DMN. Results: Both the PTS and mTBI + PTS groups had significant symptoms, but only the
comorbid group had significant decrements in cognitive functioning. Both
groups showed less stable and disrupted neural signatures of the DMN, mainly
constituting the cingulate-frontal-temporal-parietal attention network.
Specifically, the PTS group showed a combination of both
reduced contralateral strength and reduced unilateral
variability of
frontal-cingulate-temporal
connectivities, as well as increased variability of
frontal-parietal connectivities. The
mTBI + PTS group had fewer abnormal connectives than the PTS group, all of
which included reduced strength of
frontal-temporal regions and
reduced variability
frontal-cingulate-temporal
regions. Greater SFC and vDFC connectivity of the left dorsolateral
prefrontal cortex (dlPFC) ↔ precuneus was associated with
higher cognitive scores and lower symptom scores. Conclusions: Findings suggest that individuals with PTS and mTBI + PTS have a propensity
for accentuated generation of thoughts, feelings, sensations, and/or images
while in a resting state. Compared with controls, only the PTS group was
associated with accentuated variability of the
frontal-parietal attention network.
While there were no significant differences in DMN connectivity strength
between the mTBI + PTS and PTS groups, variability of connectivity was able
to distinguish them.
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Affiliation(s)
- Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, USA
| | - D Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.,AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- Department of Psychology, Auburn University, Auburn, AL, USA.,AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Thomas A Daniel
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Adam M Goodman
- Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and The University of Alabama at Birmingham, Birmingham, AL, USA
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36
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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37
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Zhao X, Rangaprakash D, Denney TS, Katz JS, Dretsch MN, Deshpande G. Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code. Data Brief 2019; 22:570-573. [PMID: 30627610 PMCID: PMC6321965 DOI: 10.1016/j.dib.2018.01.080] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 01/10/2018] [Accepted: 01/29/2018] [Indexed: 01/01/2023] Open
Abstract
This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome-along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters.
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Affiliation(s)
- Xinyu Zhao
- 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
| | - Thomas S. Denney
- 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, Birmingham, USA
- Center for Neuroscience, Auburn University, USA
| | - Jeffrey S. Katz
- 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, Birmingham, USA
- Center for Neuroscience, Auburn University, USA
| | - Michael N. Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA
- US Army Aeromedical Research Laboratory, Fort Rucker, AL, 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, Birmingham, USA
- Center for Health Ecology and Equity Research, Auburn University, USA
- Center for Neuroscience, Auburn University, USA
- Correspondence to: Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA. Fax: +1 334 844 0214.
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38
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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39
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Xie W, Cappiello M, Yassa M, Ester E, Deshpande G, Zhang W. Decoding item-specific information in visual short-term memory from the hippocampal DG/CA3 subfield using high-resolution fMRI. J Vis 2018. [DOI: 10.1167/18.10.370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Weizhen Xie
- Department of Psychology, University of California, Riverside
| | | | - Michael Yassa
- Department of Neurobiology and Behavior, University of California, Irvine
| | - Edward Ester
- Department of Psychology, Florida Atlantic University
| | | | - Weiwei Zhang
- Department of Psychology, University of California, Riverside
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41
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Zhao X, Rangaprakash D, Yuan B, Denney TS, Katz JS, Dretsch MN, Deshpande G. Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning. Front Appl Math Stat 2018; 4:25. [PMID: 30393630 PMCID: PMC6214192 DOI: 10.3389/fams.2018.00025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.
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Affiliation(s)
- Xinyu Zhao
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Quora, Inc., Mountain View, CA, United States
| | - D. Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bowen Yuan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
| | - Thomas S. Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Jeffrey S. Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Michael N. Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, United States
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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42
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Daniel TA, Townsend KM, Wang Y, Martin DS, Katz JS, Deshpande G. North American Football Fans Show Neurofunctional Differences in Response to Violence: Implications for Public Health and Policy. Front Public Health 2018; 6:177. [PMID: 30035105 PMCID: PMC6043673 DOI: 10.3389/fpubh.2018.00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 05/29/2018] [Indexed: 11/18/2022] Open
Abstract
While social and behavioral effects of violence in the media have been studied extensively, much less is known about how sports affect perceptions of violence. The current study examined neurofunctional differences between fans and non-fans of North American football (a contact sport) while viewing violent imagery. Participants viewed images of violence in both football and non-football settings while high-resolution functional magnetic resonance imaging (fMRI) data were acquired from their brains. Neurological activation was compared between these violence types and between groups. Fans of football show diminished activation in brain regions involved in pain perception and empathy such as the anterior cingulate cortex, fusiform gyrus, insula, and temporal pole when viewing violence in the context of football compared to more broadly violent images. Non-fans of football showed no such effect for the types of violent imagery and had higher activation levels than fans of football for the specified brain regions. These differences show that fans of football may perceive violence differently when it is in the context of football. These fan attitudes have potential policy implications for addressing the issue of concussions in North American football.
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Affiliation(s)
- Thomas A Daniel
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Department of Psychology, Westfield State University, Westfield, MA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Kyle M Townsend
- Cecil B. Day School of Hospitality Administration, Robinson College of Business, Georgia State University, Atlanta, GA, United States
| | - Yun Wang
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Department of Psychiatry, Columbia University, New York, NY, United States
| | - David S Martin
- Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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43
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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44
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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45
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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46
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data Brief 2018; 17:1175-1179. [PMID: 29876476 PMCID: PMC5988211 DOI: 10.1016/j.dib.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 12/22/2017] [Accepted: 01/02/2018] [Indexed: 01/10/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
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Affiliation(s)
- 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
| | - 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, 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
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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47
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Lanka P, Deshpande G. Resting state fMRI data from subjects scanned with the EPI-PACE (Echoplanar Imaging - Prospective Acquisition CorrEction) sequence. Data Brief 2018; 20:2072-2075. [PMID: 30345338 PMCID: PMC6192010 DOI: 10.1016/j.dib.2018.01.089] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 01/11/2018] [Accepted: 01/30/2018] [Indexed: 11/23/2022] Open
Abstract
Due to the confounding effects of head motion artifacts on resting-state functional connectivity (RSFC), there has been a growing interest in both acquisition and preprocessing strategies for removing motion-related artifacts from resting state functional Magnetic Resonance Imaging (Rs-fMRI) data. Prospective motion correction by the Siemens’ EPI-PACE sequence could offer new insights on the effectiveness of this sequence to correct head motion artifacts in Rs-fMRI data. The head motion parameters along with Rs-fMRI data obtained from 47 healthy individuals scanned with the EPI-PACE sequence is presented in this article. This data is useful to understand the effectiveness of prospective motion correction strategies such as 3D PACE for reducing head motion artifacts in Rs-fMRI data and help devise effective motion correction strategies. The utility of the EPI-PACE sequence in reducing motion correction artifacts in healthy controls can be found in our research article on the topic [1].
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, University of California, Merced, 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
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Corresponding author at: AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
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48
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Kyathanahally SP, Wang Y, Calhoun VD, Deshpande G. Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data. Front Neuroinform 2017; 11:74. [PMID: 29311887 PMCID: PMC5743737 DOI: 10.3389/fninf.2017.00074] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 12/07/2017] [Indexed: 11/13/2022] Open
Abstract
Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100 ms. Based on the scale free properties of EEG microstates and their correlation with resting state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling (TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution. Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.
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Affiliation(s)
- Sreenath P Kyathanahally
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Clinical Research/AMSM, University of Bern, Bern, Switzerland
| | - Yun Wang
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychiatry, Columbia University, New York, NY, United States
| | - Vince D Calhoun
- Mind Research Network and Lovelace Biomedical, Environmental Research Institute, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, 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
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49
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Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- 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
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
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50
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Dretsch MN, Daniel TA, Goodman AM, Katz JS, Denney T, Deshpande G, Robinson JL. Differential neural activation when voluntarily regulating emotions in service members with chronic mild traumatic brain injury. Appl Neuropsychol Adult 2017; 26:76-88. [PMID: 28925716 DOI: 10.1080/23279095.2017.1362406] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The objective of this study was to characterize the functional activation of the neural correlates of voluntary regulation of emotion in soldiers both with and without chronic mild traumatic brain injury (mTBI). Using functional magnetic resonance imaging (fMRI) and a battery of cognitive and psychological health measures, we assessed differences between active-duty U.S. soldiers with chronic mTBI (n = 37) and without (Controls, n = 35). Participants were instructed to maintain (passively view), enhance, and suppress emotions associated with negative and neutral visual stimuli. The mTBI group showed significantly greater clinical symptoms, but only a mild decrement in attention. Group contrasts, while controlling for posttraumatic stress disorder (PTSD) symptoms, revealed a differential neural activation pattern compared to controls, but only during the enhance condition. Specifically, the mTBI group showed greater activation in the precentral gyrus, postcentral gyrus, inferior parietal lobe, insula, and superior temporal gyrus. Finally, the effect of PTSD symptoms during the enhance condition was associated with accentuated activation of the frontal and limbic regions implicated in both emotion regulation and PTSD. Hyperactivation of neural regions in the mTBI group during the enhance condition may reflect vigilance towards negative contextual stimuli and/or poor strategy that might result in suboptimal allocation of resources to regulate emotions.
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Affiliation(s)
- Michael N Dretsch
- a US Army Aeromedical Research Laboratory , Fort Rucker , Alabama , USA.,b Human Dimension Division , Headquarters Training and Doctrine Command , Fort Eustis , Virginia , USA
| | - Thomas A Daniel
- c Department of Psychology , Auburn University , Auburn , Alabama , USA
| | - Adam M Goodman
- c Department of Psychology , Auburn University , Auburn , Alabama , USA
| | - Jeffrey S Katz
- c Department of Psychology , Auburn University , Auburn , Alabama , USA.,d Auburn University MRI Research Center, Department of Electrical & Computer Engineering , Auburn University , Auburn , Alabama , USA.,e Alabama Advanced Imaging Consortium , Auburn University and University of Alabama , Birmingham , Alabama , USA
| | - Thomas Denney
- c Department of Psychology , Auburn University , Auburn , Alabama , USA.,d Auburn University MRI Research Center, Department of Electrical & Computer Engineering , Auburn University , Auburn , Alabama , USA.,e Alabama Advanced Imaging Consortium , Auburn University and University of Alabama , Birmingham , Alabama , USA
| | - Gopikrishna Deshpande
- c Department of Psychology , Auburn University , Auburn , Alabama , USA.,d Auburn University MRI Research Center, Department of Electrical & Computer Engineering , Auburn University , Auburn , Alabama , USA.,e Alabama Advanced Imaging Consortium , Auburn University and University of Alabama , Birmingham , Alabama , USA
| | - Jennifer L Robinson
- c Department of Psychology , Auburn University , Auburn , Alabama , USA.,d Auburn University MRI Research Center, Department of Electrical & Computer Engineering , Auburn University , Auburn , Alabama , USA.,e Alabama Advanced Imaging Consortium , Auburn University and University of Alabama , Birmingham , Alabama , USA
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