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Chicos LA, Rangaprakash D, Srinivasan SS, Gutierrez-Arango S, Song H, Barry RL, Herr HM. Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation. Sci Rep 2024; 14:13456. [PMID: 38862558 PMCID: PMC11166995 DOI: 10.1038/s41598-024-63134-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 05/24/2024] [Indexed: 06/13/2024] Open
Abstract
The agonist-antagonist myoneural interface (AMI) is an amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects conducted by Srinivasan et al. (2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this study on resting state functional magnetic resonance imaging in AMI subjects, we compared functional connectivity in patients with transtibial AMI (n = 12) and traditional (n = 7) amputations (TA). To test our hypothesis that we would find significant neurophysiological differences between AMI and TA subjects, we performed a whole-brain exploratory analysis to identify a seed region; namely, we conducted ANOVA, followed by t-test statistics to locate a seed in the salience network. Then, we implemented a seed-based connectivity analysis to gather cluster-level inferences contrasting our subject groups. We show evidence supporting our hypothesis that the AMI surgery induces functional network reorganization resulting in a neural configuration that significantly differs from the neural configuration after TA surgery. AMI subjects show significantly less coupling with regions functionally dedicated to selecting where to focus attention when it comes to salient stimuli. Our findings provide researchers and clinicians with a critical mechanistic understanding of the effect of AMI amputation on brain networks at rest, which has promising implications for improved neurorehabilitation and prosthetic control.
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Affiliation(s)
- Laura A Chicos
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA.
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Shriya S Srinivasan
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02134, USA
| | - Samantha Gutierrez-Arango
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hyungeun Song
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Hugh M Herr
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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Oujamaa L, Delon-Martin C, Jaroszynski C, Termenon M, Silva S, Payen JF, Achard S. Functional hub disruption emphasizes consciousness recovery in severe traumatic brain injury. Brain Commun 2023; 5:fcad319. [PMID: 38757093 PMCID: PMC11098044 DOI: 10.1093/braincomms/fcad319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/20/2023] [Accepted: 11/21/2023] [Indexed: 05/18/2024] Open
Abstract
Severe traumatic brain injury can lead to transient or even chronic disorder of consciousness. To increase diagnosis and prognosis accuracy of disorder of consciousness, functional neuroimaging is recommended 1 month post-injury. Here, we investigated brain networks remodelling on longitudinal data between 1 and 3 months post severe traumatic brain injury related to change of consciousness. Thirty-four severe traumatic brain-injured patients were included in a cross-sectional and longitudinal clinical study, and their MRI data were compared to those of 20 healthy subjects. Long duration resting-state functional MRI were acquired in minimally conscious and conscious patients at two time points after their brain injury. The first time corresponds to the exit from intensive care unit and the second one to the discharge from post-intensive care rehabilitation ward. Brain networks data were extracted using graph analysis and metrics at each node quantifying local (clustering) and global (degree) connectivity characteristics. Comparison with brain networks of healthy subjects revealed patterns of hyper- and hypo-connectivity that characterize brain networks reorganization through the hub disruption index, a value quantifying the functional disruption in each individual severe traumatic brain injury graph. At discharge from intensive care unit, 24 patients' graphs (9 minimally conscious and 15 conscious) were fully analysed and demonstrated significant network disruption. Clustering and degree nodal metrics, respectively, related to segregation and integration properties of the network, were relevant to distinguish minimally conscious and conscious groups. At discharge from post-intensive care rehabilitation unit, 15 patients' graphs (2 minimally conscious, 13 conscious) were fully analysed. The conscious group still presented a significant difference with healthy subjects. Using mixed effects models, we showed that consciousness state, rather than time, explained the hub disruption index differences between minimally conscious and conscious groups. While severe traumatic brain-injured patients recovered full consciousness, regional functional connectivity evolved towards a healthy pattern. More specifically, the restoration of a healthy brain functional segregation could be necessary for consciousness recovery after severe traumatic brain injury. For the first time, extracting the hub disruption index directly from each patient's graph, we were able to track the clinical alteration and subsequent recovery of consciousness during the first 3 months following a severe traumatic brain injury.
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Affiliation(s)
- Lydia Oujamaa
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Chantal Delon-Martin
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Chloé Jaroszynski
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Maite Termenon
- Faculty of Engineering, Biomedical Engineering Department, Mondragon Unibertsitatea (MU-ENG), 20500 Mondragon, Spain
| | - Stein Silva
- Toulouse NeuroImaging Center, Toulouse III Paul Sabatier University, Inserm, 31062 Toulouse, France
- Critical Care Unit, University Teaching Hospital of Purpan, 31059 Toulouse, France
| | - Jean-François Payen
- University Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, CHU Grenoble Alpes, 38000 Grenoble, France
| | - Sophie Achard
- University Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France
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Rangaprakash D, David O, Barry RL, Deshpande G. Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530359. [PMID: 37961471 PMCID: PMC10634675 DOI: 10.1101/2023.02.27.530359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
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Bailes SM, Gomez DEP, Setzer B, Lewis LD. Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing. eLife 2023; 12:e86453. [PMID: 37565644 PMCID: PMC10506795 DOI: 10.7554/elife.86453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 08/10/2023] [Indexed: 08/12/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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Affiliation(s)
- Sydney M Bailes
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
| | - Daniel EP Gomez
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Beverly Setzer
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Graduate Program for Neuroscience, Boston UniversityBostonUnited States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Institute for Medical Engineering and Science, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyCambridgeUnited States
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5
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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Jacob M, Ford J, Deacon T. Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI. Front Hum Neurosci 2023; 17:976036. [PMID: 37113322 PMCID: PMC10126302 DOI: 10.3389/fnhum.2023.976036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 04/29/2023] Open
Abstract
The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.
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Affiliation(s)
- Michael Jacob
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Ford
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Terrence Deacon
- Department of Anthropology, University of California, Berkeley, Berkeley, CA, United States
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Chuang KC, Ramakrishnapillai S, Madden K, St Amant J, McKlveen K, Gwizdala K, Dhullipudi R, Bazzano L, Carmichael O. Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study. Front Aging Neurosci 2023; 15:1110434. [PMID: 36998317 PMCID: PMC10043334 DOI: 10.3389/fnagi.2023.1110434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/22/2023] [Indexed: 03/16/2023] Open
Abstract
IntroductionEffective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.MethodsIn this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.ResultsWomen and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004).DiscussionIn a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.
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Affiliation(s)
- Kai-Cheng Chuang
- Department of Physics & Astronomy, Louisiana State University, Baton Rouge, LA, United States
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
- *Correspondence: Kai-Cheng Chuang,
| | - Sreekrishna Ramakrishnapillai
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
- Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Kaitlyn Madden
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Julia St Amant
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Kevin McKlveen
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Kathryn Gwizdala
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | | | - Lydia Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
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Chicos L, Rangaprakash D, Barry R, Herr H. Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation. RESEARCH SQUARE 2023:rs.3.rs-2362961. [PMID: 36798194 PMCID: PMC9934762 DOI: 10.21203/rs.3.rs-2362961/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
The agonist-antagonist myoneural interface (AMI) is a novel amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects (Srinivasan et al., Sci. Transl. Med. 2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this first study on resting state fMRI in AMI subjects, we compared resting state functional connectivity in patients with transtibial AMI (n=12) and traditional (n=7) amputations, as well as biologically intact control subjects (n=10). We hypothesized that the AMI surgery will induce functional network reorganization that significantly differs from the traditional amputation surgery and also more closely resembles the neural configuration of controls. We found AMI subjects to have lower connectivity with salience and motor seed regions compared to traditional amputees. Additionally, with connections affected in traditional amputees, AMI subjects exhibited a connectivity pattern more closely resembling controls. Lastly, sensorimotor connectivity in amputee cohorts was significantly associated with phantom sensation (R2=0.7, p=0.0008). These findings provide researchers and clinicians with a critical mechanistic understanding of the effects of the AMI surgery on the brain at rest, spearheading future research towards improved prosthetic control and embodiment.
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Affiliation(s)
| | | | - Robert Barry
- Massachusetts General Hospital & Harvard Medical School
| | - Hugh Herr
- Massachusetts Institute of Technology
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9
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Bailes SM, Gomez DEP, Setzer B, Lewis LD. Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525528. [PMID: 36747821 PMCID: PMC9900794 DOI: 10.1101/2023.01.25.525528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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Affiliation(s)
| | - Daniel E. P. Gomez
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Beverly Setzer
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
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10
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Zhang D, Fu Q, Xue C, Xiao C, Sun Y, Liu W, Hu X. Characterization of Hemodynamic Alteration in Parkinson's Disease and Effect on Resting-State Connectivity. Neuroscience 2023:S0306-4522(23)00006-4. [PMID: 36642395 DOI: 10.1016/j.neuroscience.2023.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover rate-corrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences.
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Affiliation(s)
- Da Zhang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qianyi Fu
- International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yu Sun
- International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Research Centre for University of Birmingham and Southeast University, Southeast University, Nanjing, Jiangsu, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Xiao Hu
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China.
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11
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Yan W, Palaniyappan L, Liddle PF, Rangaprakash D, Wei W, Deshpande G. Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity. Schizophr Bull 2022; 48:695-711. [PMID: 34951473 PMCID: PMC9077436 DOI: 10.1093/schbul/sbab140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.
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Affiliation(s)
- Wenjing Yan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Peter F Liddle
- Centre for Translational Neuroimaging, Division of Mental Health and Clinical Neuroscience, Institute of Mental Health, University of Nottingham, UK
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Wei
- Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL
- Alabama Advanced Imaging Consortium, Birmingham, AL
- Center for Neuroscience, Auburn University, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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12
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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] [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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13
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Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage 2021; 244:118591. [PMID: 34560269 DOI: 10.1016/j.neuroimage.2021.118591] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/25/2021] [Accepted: 09/16/2021] [Indexed: 10/20/2022] Open
Abstract
The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China; Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.
| | - Nigel Colenbier
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven 3001, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Kenzo Clauw
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Amogh Johri
- International Institute of Information Technology, Bangalore 560100, India
| | - Madhur Tandon
- Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
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14
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Rangaprakash D, Tadayonnejad R, Deshpande G, O'Neill J, Feusner JD. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging Behav 2021; 15:1622-1640. [PMID: 32761566 PMCID: PMC7865013 DOI: 10.1007/s11682-020-00358-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.
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Affiliation(s)
- D Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School and Harvard-MIT Health Sciences and Technology, Cambridge, MA, 02129, USA
| | - Reza Tadayonnejad
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, 36849, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, 36849, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Auburn, AL, USA
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joseph O'Neill
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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15
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Jiang S, Li H, Pei H, Liu L, Li Z, Chen Y, Li X, Li Q, Yao D, Luo C. Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy. Brain Struct Funct 2021; 226:1423-1435. [PMID: 33730218 DOI: 10.1007/s00429-021-02248-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/27/2021] [Indexed: 11/28/2022]
Abstract
This study aims to characterize the connective profiles and the coupling relationship between dynamic and static functional connectivity (dFC and sFC) in large-scale brain networks in patients with generalized epilepsy (GE). Functional, structural and diffuse MRI data were collected from 83 patients with GE and 106 matched healthy controls (HC). Resting-state BOLD time course was deconvolved to neural time course using a blind hemodynamic deconvolution method. Then, five connective profiles, including the structural connectivity (SC) and BOLD/neural time course-derived sFC/dFC networks, were constructed based on the proposed whole brain atlas. Network-level weighted correlation probability (NWCP) were proposed to evaluate the association between dFC and sFC. Both the BOLD signal and neural time course showed highly concordant findings and the present study emphasized the consistent findings between two functional approaches. The patients with GE showed hypervariability and enhancement of FC, and notably decreased SC in the subcortical network. Besides, increased dFC, weaker anatomic links, and complex alterations of sFC were observed in the default mode network of GE. Moreover, significantly increased SC and predominantly increased sFC were found in the frontoparietal network. Remarkably, antagonism between dFC and sFC was observed in large-scale networks in HC, while patients with GE showed significantly decreased antagonism in core epileptic networks. In sum, our study revealed distinct connective profiles in different epileptic networks and provided new clues to the brain network mechanism of epilepsy from the perspective of antagonism between dynamic and static functional connectivity.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Linli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Qifu Li
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China.,Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China. .,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China. .,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
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16
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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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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17
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Berman BD, Groth CL, Shelton E, Sillau SH, Sutton B, Legget KT, Tregellas JR. Hemodynamic responses are abnormal in isolated cervical dystonia. J Neurosci Res 2020; 98:692-703. [PMID: 31692015 PMCID: PMC7015799 DOI: 10.1002/jnr.24547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/10/2019] [Accepted: 10/10/2019] [Indexed: 01/04/2023]
Abstract
Neuroimaging studies using functional magnetic resonance imaging (fMRI), which measures brain activity by detecting the changes in blood oxygenation levels, are advancing our understanding of the pathophysiology of dystonia. Neurobiological disturbances in dystonia, however, may affect neurovascular coupling and impact the interpretability of fMRI studies. We evaluated here whether the hemodynamic response patterns during a behaviorally matched motor task are altered in isolated cervical dystonia (CD). Twenty-five CD patients and 25 healthy controls (HCs) underwent fMRI scanning during a paced finger tapping task (nondystonic task in patients). Imaging data were analyzed using a constrained principal component analysis-a statistical method that combines regression analysis and principal component analysis and enables the extraction of task-related functional networks and determination of the spatial and temporal hemodynamic response patterns associated with the task performance. Data from three patients and two controls were removed due to excessive movement. No significant differences in demographics or motor performance were observed. Three task-associated functional brain networks were identified. During task performance, reduced hemodynamic responses were seen in a sensorimotor network and in a network that included key nodes of the default mode, executive control and visual networks. During rest, reductions in hemodynamic responses were seen in the cognitive/visual network. Lower hemodynamic responses within the primary sensorimotor network in patients were correlated with the increased dystonia severity. Pathophysiological disturbances in isolated CD, such as alterations in inhibitory signaling and dopaminergic neurotransmission, may impact neurovascular coupling. Not accounting for hemodynamic response differences in fMRI studies of dystonia could lead to inaccurate results and interpretations.
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Affiliation(s)
- Brian D. Berman
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
- Neurology Section, Denver VA Medical Center, Aurora, CO, USA
| | - Christopher L. Groth
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - Erica Shelton
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO
| | - Stefan H. Sillau
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO
| | - Brianne Sutton
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
| | - Kristina T. Legget
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
| | - Jason R. Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
- Research Service, Denver VA Medical Center, Aurora, CO USA
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18
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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] [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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19
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Zhao S, Rangaprakash D, Liang P, Deshpande G. Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks. Brain Inform 2019; 6:8. [PMID: 31792630 PMCID: PMC6888786 DOI: 10.1186/s40708-019-0101-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/11/2019] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks. METHODS Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network. RESULTS MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC. CONCLUSION Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.
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Affiliation(s)
- Sinan Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
| | - 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
| | - Peipeng Liang
- School of Psychology, Capital Normal University, Beijing, China
| | - 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, Auburn, 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 Health and Neurosciences, Bangalore, India.
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20
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Boukrina O, Kucukboyaci NE, Dobryakova E. Considerations of power and sample size in rehabilitation research. Int J Psychophysiol 2019; 154:6-14. [PMID: 31655185 DOI: 10.1016/j.ijpsycho.2019.08.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 05/22/2019] [Accepted: 08/23/2019] [Indexed: 01/26/2023]
Abstract
With the current emphasis on power and reproducibility, pressures are rising to increase sample sizes in rehabilitation research in order to reflect more accurate effect estimation and generalizable results. The conventional way of increasing power by enrolling more participants is less feasible in some fields of research. In particular, rehabilitation research faces considerable challenges in achieving this goal. We describe the specific challenges to increasing power by recruiting large sample sizes and obtaining large effects in rehabilitation research. Specifically, we discuss how variability within clinical populations, lack of common standards for selecting appropriate control groups; potentially reduced reliability of measurements of brain function in individuals recovering from a brain injury; biases involved in a priori effect size estimation, and higher budgetary and staffing requirements can influence considerations of sample and effect size in rehabilitation. We also describe solutions to these challenges, such as increased sampling per participant, improving experimental control, appropriate analyses, transparent result reporting and using innovative ways of harnessing the inherent variability of clinical populations. These solutions can improve statistical power and produce reliable and valid results even in the face of limited availability of large samples.
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Affiliation(s)
- Olga Boukrina
- Center for Stroke Rehabilitation Research, Kessler Foundation, West Orange, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA
| | - N Erkut Kucukboyaci
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA.
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21
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Modulation of the spontaneous hemodynamic response function across levels of consciousness. Neuroimage 2019; 200:450-459. [DOI: 10.1016/j.neuroimage.2019.07.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 07/03/2019] [Accepted: 07/04/2019] [Indexed: 01/06/2023] Open
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22
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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] [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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23
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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] [Abstract] [Key Words] [Grants] [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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24
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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. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 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] [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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25
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Ramaihgari B, Pustovyy OM, Waggoner P, Beyers RJ, Wildey C, Morrison E, Salibi N, Katz JS, Denney TS, Vodyanoy VJ, Deshpande G. Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs. Front Vet Sci 2018; 5:127. [PMID: 30013977 PMCID: PMC6036133 DOI: 10.3389/fvets.2018.00127] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 05/23/2018] [Indexed: 01/01/2023] Open
Abstract
Prior functional Magnetic Resonance Imaging (fMRI) studies have indicated increased neural activation when zinc nanoparticles are added to odorants in canines. Here we demonstrate that zinc nanoparticles up-regulate directional brain connectivity in parts of the canine olfactory network. This provides an explanation for previously reported enhancement in the odor detection capability of the dogs in the presence of zinc nanoparticles. In this study, we obtained fMRI data from awake and unrestrained dogs while they were being exposed to odorants with and without zinc nanoparticles, zinc nanoparticles suspended in water vapor, as well as just water vapor alone. We obtained directional connectivity between the brain regions of the olfactory network that were significantly stronger for the condition of odorant + zinc nanoparticles compared to just odorants, water vapor + zinc nanoparticles and water vapor alone. We observed significant strengthening of the paths of the canine olfactory network in the presence of zinc nanoparticles. This result indicates that zinc nanoparticles could potentially be used to increase canine detection capabilities in the environments of very low concentrations of the odorants, which would have otherwise been undetected.
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Affiliation(s)
- Bhavitha Ramaihgari
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Oleg M. Pustovyy
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Paul Waggoner
- Canine Detection Research Institute, Auburn UniversityAuburn, AL, United States
| | - Ronald J. Beyers
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Chester Wildey
- MRRA Inc., University of Alabama at Birmingham, Euless, TX, United States
| | - Edward Morrison
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Nouha Salibi
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- MR Research and Development, Siemens Healthcare, Malvern, PA, United States
| | - Jeffrey S. Katz
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
| | - Thomas S. Denney
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
| | - Vitaly J. Vodyanoy
- Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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26
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Estimated hemodynamic response function parameters obtained from resting state BOLD fMRI signals in subjects with autism spectrum disorder and matched healthy subjects. Data Brief 2018; 19:1305-1309. [PMID: 30225289 PMCID: PMC6139368 DOI: 10.1016/j.dib.2018.04.126] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/27/2018] [Accepted: 04/30/2018] [Indexed: 11/20/2022] Open
Abstract
In Functional magnetic resonance imaging (fMRI), the blood oxygen level dependent (BOLD) signal is modeled as a convolution of the hemodynamic response function (HRF) and the unmeasured latent neural signal. Although most cortical and subcortical brain regions share the canonical shape of the HRF, the temporal structure of HRFs are variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. The variability between subjects can be examined by three parameters that characterize the HRF: response height (RH), time-to-peak (TTP) and full-width at half-max (FWHM). This data provides three HRF parameters at every voxel, obtained from Autism Spectrum Disorder (ASD) patients (N = 531), and matched healthy controls (N = 571). Since ongoing studies suggest that non-standard populations have important differences in their HRFs when compared with healthy control, this data set is valuable in studying variability of HRF in ASD group and inferring the underlying pathology that also affects the HRF. It also has implications for fMRI analyses like resting-sate connectivity analysis.
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27
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med 2018; 80:1697-1713. [DOI: 10.1002/mrm.27146] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/26/2023]
Affiliation(s)
- D. Rangaprakash
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychiatry and Biobehavioral Sciences; University of California Los Angeles; Los Angeles California
| | - Guo-Rong Wu
- Department of Data Analysis; University of Ghent; Ghent Belgium
- Key Laboratory of Cognition and Personality, Southwest University; Chongqing China
| | | | - Xiaoping Hu
- Department of Bioengineering; University of California Riverside; Riverside California
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychology; Auburn University; Auburn Alabama
- Center for Health Ecology and Equity Research, Auburn University; Auburn Alabama
- Alabama Advanced Imaging Consortium, Auburn University, University of South Alabama and University of Alabama at Tuscaloosa and Birmingham; Alabama
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28
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Yan W, Rangaprakash D, Deshpande G. Aberrant hemodynamic responses in autism: Implications for resting state fMRI functional connectivity studies. Neuroimage Clin 2018; 19:320-330. [PMID: 30013915 PMCID: PMC6044186 DOI: 10.1016/j.nicl.2018.04.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/28/2018] [Accepted: 04/11/2018] [Indexed: 11/19/2022]
Abstract
Functional MRI (fMRI) is modeled as a convolution of the hemodynamic response function (HRF) and an unmeasured latent neural signal. However, HRF itself is variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. Aberrations in underlying neurochemical mechanisms, which control HRF shape, have been reported in autism spectrum disorders (ASD). Therefore, we hypothesized that this will lead to voxel-specific, yet systematic differences in HRF shape between ASD and healthy controls. As a corollary, we also hypothesized that such alterations will lead to differences in estimated functional connectivity in fMRI space compared to latent neural space. To test these hypotheses, we performed blind deconvolution of resting-state fMRI time series acquired from large number of ASD and control subjects obtained from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 1102). Many brain regions previously implicated in autism showed systematic differences in HRF shape in ASD. Specifically, we found that precuneus had aberrations in all HRF parameters. Consequently, we obtained precuneus-seed-based functional connectivity differences between ASD and controls using fMRI as well as using latent neural signals. We found that non-deconvolved fMRI data failed to detect group differences in connectivity between precuneus and certain brain regions that were instead observed in deconvolved data. Our results are relevant for the understanding of hemodynamic and neurochemical aberrations in ASD, as well as have methodological implications for resting-state functional connectivity studies in Autism, and more generally in disorders that are accompanied by neurochemical alterations that may impact HRF shape.
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Affiliation(s)
- Wenjing Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA.
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29
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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] [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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30
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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] [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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31
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Rangaprakash D, Dretsch MN, Yan W, Katz JS, Denney TS, Deshpande G. Hemodynamic response function parameters obtained from resting-state functional MRI data in soldiers with trauma. Data Brief 2017; 14:558-562. [PMID: 28861454 PMCID: PMC5567973 DOI: 10.1016/j.dib.2017.07.072] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/12/2017] [Accepted: 07/27/2017] [Indexed: 11/21/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of brain activity, i.e. it is a convolution of the latent (unmeasured) neural signal and the hemodynamic response function (HRF). As such, the HRF has been shown to vary across brain regions and individuals. The shape of the HRF is controlled by both neural and non-neural factors. The shape of the HRF can be characterized by three parameters (response height, time-to-peak and full-width at half-max). The data presented here provides the three HRF parameters at every voxel, obtained from U.S. Army soldiers (N=87) diagnosed with posttraumatic stress disorder (PTSD), with comorbid PTSD and mild-traumatic brain injury (mTBI), and matched healthy combat controls. Findings from this data and further interpretations are available in our recent research study (Rangaprakash et al., 2017) [1]. This data is a valuable asset in studying the impact of HRF variability on fMRI data analysis, specifically resting state functional connectivity.
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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
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA.,US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
| | - Wenjing Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, 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, Auburn University and University of Alabama Birmingham, 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, Auburn University and University of Alabama Birmingham, 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, Auburn University and University of Alabama Birmingham, AL, USA
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