1
|
Mayer AR, Dodd AB, Robertson-Benta CR, Zotev V, Ryman SG, Meier TB, Campbell RA, Phillips JP, van der Horn HJ, Hogeveen J, Tarawneh R, Sapien RE. Multifaceted neural and vascular pathologies after pediatric mild traumatic brain injury. J Cereb Blood Flow Metab 2024; 44:118-130. [PMID: 37724718 PMCID: PMC10905640 DOI: 10.1177/0271678x231197188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/01/2023] [Accepted: 07/25/2023] [Indexed: 09/21/2023]
Abstract
Dynamic changes in neurodevelopment and cognitive functioning occur during adolescence, including a switch from reactive to more proactive forms of cognitive control, including response inhibition. Pediatric mild traumatic brain injury (pmTBI) affects these cognitions immediately post-injury, but the role of vascular versus neural injury in cognitive dysfunction remains debated. This study consecutively recruited 214 sub-acute pmTBI (8-18 years) and age/sex-matched healthy controls (HC; N = 186), with high retention rates (>80%) at four months post-injury. Multimodal imaging (functional MRI during response inhibition, cerebral blood flow and cerebrovascular reactivity) assessed for pathologies within the neurovascular unit. Patients exhibited increased errors of commission and hypoactivation of motor circuitry during processing of probes. Evidence of increased/delayed cerebrovascular reactivity within motor circuitry during hypercapnia was present along with normal perfusion. Neither age-at-injury nor post-concussive symptom load were strongly associated with imaging abnormalities. Collectively, mild cognitive impairments and clinical symptoms may continue up to four months post-injury. Prolonged dysfunction within the neurovascular unit was observed during proactive response inhibition, with preliminary evidence that neural and pure vascular trauma are statistically independent. These findings suggest pmTBI is characterized by multifaceted pathologies during the sub-acute injury stage that persist several months post-injury.
Collapse
Affiliation(s)
- Andrew R Mayer
- The Mind Research Network/LBERI, Albuquerque, NM, USA
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
- Department of Neurology, University of New Mexico, Albuquerque, NM, USA
- Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Andrew B Dodd
- The Mind Research Network/LBERI, Albuquerque, NM, USA
| | | | - Vadim Zotev
- The Mind Research Network/LBERI, Albuquerque, NM, USA
| | | | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Richard A Campbell
- Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - John P Phillips
- The Mind Research Network/LBERI, Albuquerque, NM, USA
- Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | | | - Jeremy Hogeveen
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Rawan Tarawneh
- Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Robert E Sapien
- Department of Emergency Medicine, University of New Mexico, Albuquerque, NM, USA
| |
Collapse
|
2
|
Hoeppli ME, Garenfeld MA, Mortensen CK, Nahman‐Averbuch H, King CD, Coghill RC. Denoising task-related fMRI: Balancing noise reduction against signal loss. Hum Brain Mapp 2023; 44:5523-5546. [PMID: 37753711 PMCID: PMC10619396 DOI: 10.1002/hbm.26447] [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: 03/30/2022] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 09/28/2023] Open
Abstract
Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.
Collapse
Affiliation(s)
- M. E. Hoeppli
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - M. A. Garenfeld
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of Health Science and TechnologyAalborg UniversityAalborgDenmark
| | - C. K. Mortensen
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - H. Nahman‐Averbuch
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Washington University Pain Center, Department of AnesthesiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - C. D. King
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati, College of MedicineCincinnatiOhioUSA
| | - R. C. Coghill
- Division of Behavioral Medicine and Clinical PsychologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati, College of MedicineCincinnatiOhioUSA
| |
Collapse
|
3
|
Zhang H, Meng C, Di X, Wu X, Biswal B. Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states. Netw Neurosci 2023; 7:1034-1050. [PMID: 37781145 PMCID: PMC10473282 DOI: 10.1162/netn_a_00314] [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: 08/29/2022] [Accepted: 03/21/2023] [Indexed: 10/03/2023] Open
Abstract
Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.
Collapse
Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Xiao Wu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| |
Collapse
|
4
|
Van Schuerbeek P, De Wandel L, Baeken C. The optimized combination of aCompCor and ICA-AROMA to reduce motion and physiologic noise in task fMRI data. Biomed Phys Eng Express 2022; 8. [PMID: 35378526 DOI: 10.1088/2057-1976/ac63f0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
One of the main challenges in fMRI processing is filtering the task BOLD signals from the noise. Independent component analysis with automatic removal of motion artifacts (ICA-AROMA) reduces motion artifacts by identifying ICA noise components based on their location at the brain edges and cerebrospinal fluid (CSF), high frequency content and correlation with motion regressors. In anatomical component correction (aCompCor), physiological noise regressors extracted from CSF were regressed out from the fMRI time series. In this study, we compared three methods to combine aCompCor and ICA-AROMA denoising in one denoising step. In the first analysis, we regressed the temporal signals of the ICA components identified as noise by ICA-AROMA together with the noise signals determined by aCompCor from the fMRI signals. For the second and third analyses, the correlation between the temporal signals of the ICA components and the aCompCor noise signals was used as an additional criterion to identify the noise components. In the second analysis, the temporal signals of the ICA components classified as noise were regressed from the fMRI signals. In the third analysis, the noise components were removed. To compare the denoising strategies, we examined the fractional amplitude of low-frequency fluctuations (fALFF) and the overlap between the contrast maps. Our results revealed that including the aCompCor noise signals as regressors in ICA-AROMA resulted in more correctly identified noise components, higher fALFF values, and larger activation maps. Moreover, combining the temporal signals of the noise components identified by ICA-AROMA with the aCompCor signals in a noise regression matrix resulted in deactivations. These results suggest that using the correlation between the ICA component temporal signals and the aCompCor signals as noise identification criteria in ICA-AROMA is the best approach for combining both denoising methods.
Collapse
Affiliation(s)
- P Van Schuerbeek
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Laarbeeklaan 101, 1090 Brussels, Belgium
| | - L De Wandel
- Faculty of Medicine and Health Sciences, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium
| | - C Baeken
- Faculty of Medicine and Health Sciences, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium.,Department of Psychiatry, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Laarbeeklaan 101, 1090 Brussels, Belgium.,Eindhoven University of Technology, Department of Electrical Engineering, The Netherlands
| |
Collapse
|
5
|
Ekhtiari H, Zare-Bidoky M, Sangchooli A, Janes AC, Kaufman MJ, Oliver JA, Prisciandaro JJ, Wüstenberg T, Anton RF, Bach P, Baldacchino A, Beck A, Bjork JM, Brewer J, Childress AR, Claus ED, Courtney KE, Ebrahimi M, Filbey FM, Ghahremani DG, Azbari PG, Goldstein RZ, Goudriaan AE, Grodin EN, Hamilton JP, Hanlon CA, Hassani-Abharian P, Heinz A, Joseph JE, Kiefer F, Zonoozi AK, Kober H, Kuplicki R, Li Q, London ED, McClernon J, Noori HR, Owens MM, Paulus MP, Perini I, Potenza M, Potvin S, Ray L, Schacht JP, Seo D, Sinha R, Smolka MN, Spanagel R, Steele VR, Stein EA, Steins-Loeber S, Tapert SF, Verdejo-Garcia A, Vollstädt-Klein S, Wetherill RR, Wilson SJ, Witkiewitz K, Yuan K, Zhang X, Zilverstand A. A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus. Nat Protoc 2022; 17:567-595. [PMID: 35121856 PMCID: PMC9063851 DOI: 10.1038/s41596-021-00649-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/21/2021] [Indexed: 12/23/2022]
Abstract
Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.
Collapse
Affiliation(s)
- Hamed Ekhtiari
- Laureate Institute for Brain Research, Tulsa, OK, USA. .,Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Shahid-Sadoughi University of Medical Sciences, Yazd, Iran.,These authors contributed equally: Mehran Zare-Bidoky, Arshiya Sangchooli
| | - Arshiya Sangchooli
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,These authors contributed equally: Mehran Zare-Bidoky, Arshiya Sangchooli
| | - Amy C. Janes
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Marc J. Kaufman
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Jason A. Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,TSET Health Promotion Research Center, Stephenson Cancer Center, Oklahoma City, OK, USA.,Department of Psychiatry & Behavioral Sciences, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - James J. Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Torsten Wüstenberg
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Raymond F. Anton
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Patrick Bach
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Alex Baldacchino
- Division of Population Studies and Behavioural Sciences, St Andrews University Medical School, University of St Andrews, Scotland, UK
| | - Anne Beck
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany.,Faculty of Health, Health and Medical University, Campus Potsdam, Potsdam, Germany
| | - James M. Bjork
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Judson Brewer
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Anna Rose Childress
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric D. Claus
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Kelly E. Courtney
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Mohsen Ebrahimi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Francesca M. Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Dara G. Ghahremani
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peyman Ghobadi Azbari
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Department of Biomedical Engineering, Shahed University, Tehran, Iran
| | - Rita Z. Goldstein
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anna E. Goudriaan
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam and Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Erica N. Grodin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - J. Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Colleen A. Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Andreas Heinz
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Jane E. Joseph
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Falk Kiefer
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Arash Khojasteh Zonoozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hedy Kober
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Qiang Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, China
| | - Edythe D. London
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Hamid R. Noori
- International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)/Institute of Neuroscience (ION), Chinese Academy of Sciences, Shanghai, China.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Max M. Owens
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Irene Perini
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Marc Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.,Connecticut Mental Health Center, New Haven, CT, USA.,Connecticut Council on Problem Gambling, Wethersfield, CT, USA.,Department of Neuroscience, Child Study Center and Wu Tsai Institute, Yale School of Medicine, New Haven, CT, USA
| | - Stéphane Potvin
- Centre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, University of Montreal, Montreal, Canada
| | - Lara Ray
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Dongju Seo
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Michael N. Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Mannheim, Germany
| | - Vaughn R. Steele
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Elliot A. Stein
- Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Sabine Steins-Loeber
- Department of Clinical Psychology and Psychotherapy, Otto-Friedrich-University of Bamberg, Bamberg, Germany
| | - Susan F. Tapert
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | | | - Sabine Vollstädt-Klein
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Reagan R. Wetherill
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J. Wilson
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi’an, China
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Anhui, China.,Department of Radiology, First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Science at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Anhui, China
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
6
|
Lewis JD, Bezgin G, Fonov VS, Collins DL, Evans AC. A sub+cortical fMRI-based surface parcellation. Hum Brain Mapp 2021; 43:616-632. [PMID: 34761459 PMCID: PMC8720195 DOI: 10.1002/hbm.25675] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.
Collapse
Affiliation(s)
- John D Lewis
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Gleb Bezgin
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Verdun, Quebec, Canada
| | - Vladimir S Fonov
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
7
|
Dodd AB, Lu H, Wertz CJ, Ling JM, Shaff NA, Wasserott BC, Meier TB, Park G, Oglesbee SJ, Phillips JP, Campbell RA, Liu P, Mayer AR. Persistent alterations in cerebrovascular reactivity in response to hypercapnia following pediatric mild traumatic brain injury. J Cereb Blood Flow Metab 2020; 40:2491-2504. [PMID: 31903838 PMCID: PMC7820694 DOI: 10.1177/0271678x19896883] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Much attention has been paid to the effects of mild traumatic brain injury (mTBI) on cerebrovascular reactivity in adult populations, yet it remains understudied in pediatric injury. In this study, 30 adolescents (12-18 years old) with pediatric mTBI (pmTBI) and 35 age- and sex-matched healthy controls (HC) underwent clinical and neuroimaging assessments during sub-acute (6.9 ± 2.2 days) and early chronic (120.4 ± 11.7 days) phases of injury. Relative to controls, pmTBI reported greater initial post-concussion symptoms, headache, pain, and anxiety, resolving by four months post-injury. Patients reported increased sleep issues and exhibited deficits in processing speed and attention across both visits. In grey-white matter interface areas throughout the brain, pmTBI displayed increased maximal fit/amplitude of a time-shifted end-tidal CO2 regressor to blood oxygen-level dependent response relative to HC, as well as increased latency to maximal fit. The alterations persisted through the early chronic phase of injury, with maximal fit being associated with complaints of ongoing sleep disturbances during post hoc analyses but not cognitive measures of processing speed or attention. Collectively, these findings suggest that deficits in the speed and degree of cerebrovascular reactivity may persist longer than current conceptualizations about clinical recovery within 30 days.
Collapse
Affiliation(s)
- Andrew B Dodd
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher J Wertz
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
| | - Josef M Ling
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
| | - Nicholas A Shaff
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
| | - Benjamin C Wasserott
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
| | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Departments of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Grace Park
- Department of Pediatric Emergency Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Scott J Oglesbee
- Department of Pediatric Emergency Medicine, University of New Mexico, Albuquerque, NM, USA
| | - John P Phillips
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
- Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Richard A Campbell
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Peiying Liu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew R Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA
- Department of Neurology, University of New Mexico, Albuquerque, NM, USA
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
- Andrew R Mayer, The Mind Research Network, Pete & Nancy Domenici Hall, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA.
| |
Collapse
|
8
|
De Blasi B, Caciagli L, Storti SF, Galovic M, Koepp M, Menegaz G, Barnes A, Galazzo IB. Noise removal in resting-state and task fMRI: functional connectivity and activation maps. J Neural Eng 2020; 17:046040. [PMID: 32663803 DOI: 10.1088/1741-2552/aba5cc] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. MAIN RESULTS Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.
Collapse
Affiliation(s)
- Bianca De Blasi
- Department of Medical Physics and Bioengineering, University College London, London, United Kingdom. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | | | |
Collapse
|
9
|
Mayer AR, Stephenson DD, Wertz CJ, Dodd AB, Shaff NA, Ling JM, Park G, Oglesbee SJ, Wasserott BC, Meier TB, Witkiewitz K, Campbell RA, Yeo RA, Phillips JP, Quinn DK, Pottenger A. Proactive inhibition deficits with normal perfusion after pediatric mild traumatic brain injury. Hum Brain Mapp 2019; 40:5370-5381. [PMID: 31456319 PMCID: PMC6864901 DOI: 10.1002/hbm.24778] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 07/11/2019] [Accepted: 08/16/2019] [Indexed: 12/14/2022] Open
Abstract
Although much attention has been generated in popular media regarding the deleterious effects of pediatric mild traumatic brain injury (pmTBI), a paucity of empirical evidence exists regarding the natural course of biological recovery. Fifty pmTBI patients (12–18 years old) were consecutively recruited from Emergency Departments and seen approximately 1 week and 4 months post‐injury in this prospective cohort study. Data from 53 sex‐ and age‐matched healthy controls (HC) were also collected. Functional magnetic resonance imaging was obtained during proactive response inhibition and at rest, in conjunction with independent measures of resting cerebral blood flow. High temporal resolution imaging enabled separate modeling of neural responses for preparation and execution of proactive response inhibition. A priori predictions of failed inhibitory responses (i.e., hyperactivation) were observed in motor circuitry (pmTBI>HC) and sensory areas sub‐acutely and at 4 months post‐injury. Paradoxically, pmTBI demonstrated hypoactivation (HC>pmTBI) during target processing, along with decreased activation within prefrontal cognitive control areas. Functional connectivity within motor circuitry at rest suggested that deficits were limited to engagement during the inhibitory task, whereas normal resting cerebral perfusion ruled out deficits in basal perfusion. In conclusion, current results suggest blood oxygen‐level dependent deficits during inhibitory control may exceed commonly held beliefs about physiological recovery following pmTBI, potentially lasting up to 4 months post‐injury.
Collapse
Affiliation(s)
- Andrew R Mayer
- The Mind Research Network/LBERI, Albuquerque, New Mexico.,Department of Psychology, University of New Mexico, Albuquerque, New Mexico.,Department of Neurology, University of New Mexico, Albuquerque, New Mexico.,Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | | | | | - Andrew B Dodd
- The Mind Research Network/LBERI, Albuquerque, New Mexico
| | | | - Josef M Ling
- The Mind Research Network/LBERI, Albuquerque, New Mexico
| | - Grace Park
- Emergency Medicine, University of New Mexico, Albuquerque, New Mexico
| | - Scott J Oglesbee
- Emergency Medicine, University of New Mexico, Albuquerque, New Mexico
| | | | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin.,Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, New Mexico
| | - Richard A Campbell
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Ronald A Yeo
- Department of Psychology, University of New Mexico, Albuquerque, New Mexico
| | - John P Phillips
- The Mind Research Network/LBERI, Albuquerque, New Mexico.,Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Davin K Quinn
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Amy Pottenger
- Emergency Medicine, University of New Mexico, Albuquerque, New Mexico
| |
Collapse
|
10
|
Mayer AR, Ling JM, Dodd AB, Shaff NA, Wertz CJ, Hanlon FM. A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data. Hum Brain Mapp 2019; 40:3843-3859. [PMID: 31119818 DOI: 10.1002/hbm.24635] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 03/15/2019] [Accepted: 05/06/2019] [Indexed: 11/08/2022] Open
Abstract
It has been known for decades that head motion/other artifacts affect the blood oxygen level-dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind-source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event-related functional magnetic resonance imaging (erfMRI) and block-design (bdfMRI) datasets collected with multiband 32- (repetition time [TR] = 460 ms) or older 12-channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind-source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind-source denoising approaches appear to remove signal as well as noise from task-related data at individual subject and group levels.
Collapse
Affiliation(s)
- Andrew R Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,Departments of Neurology and Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico.,Department of Psychology, University of New Mexico, Albuquerque, New Mexico
| | - Josef M Ling
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Andrew B Dodd
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Nicholas A Shaff
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Christopher J Wertz
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Faith M Hanlon
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| |
Collapse
|