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Huang S, Vigotsky AD, Apkarian AV, Huang L. Body mass index associated with respiration predicts motion in resting-state functional magnetic resonance imaging scans. Hum Brain Mapp 2024; 45:e70015. [PMID: 39225333 PMCID: PMC11369907 DOI: 10.1002/hbm.70015] [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: 03/06/2024] [Revised: 07/01/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Decreasing body mass index (BMI) reduces head motion in resting-state fMRI (rs-fMRI) data. Yet, the mechanism by which BMI affects head motion remains poorly understood. Understanding how BMI interacts with respiration to affect head motion can improve head motion reduction strategies. A total of 254 patients with back pain were included in this study, each of whom had two visits (interval time = 13.85 ± 7.81 weeks) during which two consecutive re-fMRI scans were obtained. We investigated the relationships between head motion and demographic and pain-related characteristics-head motion was reliable across scans and correlated with age, pain intensity, and BMI. Multiple linear regression models determined that BMI was the main determinant in predicting head motion. BMI was also associated with two features derived from respiration signal. Anterior-posterior and superior-inferior motion dominated both overall motion magnitude and the coupling between motion and respiration. BMI interacted with respiration to influence motion only in the pitch dimension. These findings indicate that BMI should be a critical parameter in both study designs and analyses of fMRI data.
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Affiliation(s)
- Shishi Huang
- Department of NeurologyThe Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Andrew D. Vigotsky
- Department of Biomedical EngineeringNorthwestern UniversityEvanstonIllinoisUSA
- Department of StatisticsNorthwestern UniversityEvanstonIllinoisUSA
| | - Apkar Vania Apkarian
- Department of Neuroscience, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Center for Translational Pain Research, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Center for Translational Pain Research, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
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2
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Thomson P, Loosley V, Friedel E, Silk TJ. Changes in MRI head motion across development: typical development and ADHD. Brain Imaging Behav 2024:10.1007/s11682-024-00910-w. [PMID: 39190098 DOI: 10.1007/s11682-024-00910-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2024] [Indexed: 08/28/2024]
Abstract
Head motion is a major confounding variable for magnetic resonance imaging (MRI) analysis, and is commonly seen in individuals with neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). This study investigated the trajectory of change in head motion in typically developing children and children with ADHD, and examined possible altered trajectories in head motion between children with remitted and persistent ADHD. 105 children with ADHD and 84 controls completed diffusion and resting-state functional MRI scans at up to three waves over ages 9-14 years. In-scanner head motion was calculated using framewise displacement, and longitudinal trajectories analyzed using generalized additive mixed modelling. Results revealed a significant age effect on framewise displacement where head motion decreased as age increased during both diffusion (p < .001) and resting-state functional MRI (p < .001). A significant effect of group was also observed; children with ADHD displayed greater framewise displacement than controls over the age range (diffusion MRI p = .036, functional MRI p = .004). Further analyses revealed continued elevation in head motion in children in remission from ADHD (diffusion MRI p = .020, functional MRI p = .011) compared to controls. Rates of change in head motion did not significantly differ between diagnostic groups. Findings indicate a critical link between in-scanner head motion and developmental age within children regardless of ADHD diagnosis, important to consider in studies of neurodevelopment. Findings also suggest change in head motion with age does not differ between individuals with remitted and persistent ADHD, adding further evidence that behavioral manifestations of ADHD may continue despite clinical remission.
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Affiliation(s)
| | - Vanessa Loosley
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, VIC, 3125, Australia
| | - Emily Friedel
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, VIC, 3125, Australia
| | - Timothy J Silk
- Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, VIC, 3125, Australia.
- Developmental Imaging, Murdoch Children's Research Institute, Flemington Road, Parkville, VIC, 3052, Australia.
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3
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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4
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Strike LT, Kerestes R, McMahon KL, de Zubicaray GI, Harding IH, Medland SE. Heritability of cerebellar subregion volumes in adolescent and young adult twins. Hum Brain Mapp 2024; 45:e26717. [PMID: 38798116 PMCID: PMC11128777 DOI: 10.1002/hbm.26717] [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: 01/02/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine-grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum (h 2 $$ {h}^2 $$ 47%-91%). Additionally, the good to excellent test-retest reliability for a subsample of QTIM participants suggests that non-genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome-wide association studies.
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Affiliation(s)
- Lachlan T. Strike
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of Biomedical Sciences, Faculty of MedicineUniversity of QueenslandBrisbaneAustralia
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Greig I. de Zubicaray
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
- Cerebellum and Neurodegeneration, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of PsychologyUniversity of QueenslandBrisbaneAustralia
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5
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Smith DV, Ludwig RM, Dennison JB, Reeck C, Fareri DS. An fMRI Dataset on Social Reward Processing and Decision Making in Younger and Older Adults. Sci Data 2024; 11:158. [PMID: 38302470 PMCID: PMC10834522 DOI: 10.1038/s41597-024-02931-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
Behavioural and neuroimaging research has shown that older adults are less sensitive to financial losses compared to younger adults. Yet relatively less is known about age-related differences in social decisions and social reward processing. As part of a pilot study, we collected behavioural and functional magnetic resonance imaging (fMRI) data from 50 participants (Younger: N = 26, ages 18-34 years; Older: N = 24, ages 63-80 years) who completed three tasks in the scanner: an economic trust game as the investor with three partners (computer, stranger, friend) as the investee; a card-guessing task with monetary gains and losses shared with three partners (computer, stranger, friend); and an ultimatum game as responder to three anonymous proposers (computer, age-similar adults, age-dissimilar adults). We also collected B0 field maps and high-resolution structural images (T1-weighted and T2-weighted images). These data could be reused to answer questions about moment-to-moment variability in fMRI signal, representational similarity between tasks, and brain structure.
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Affiliation(s)
| | - Rita M Ludwig
- Temple University, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B Dennison
- Temple University, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
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Cao H, Barber AD, Rubio JM, Argyelan M, Gallego JA, Lencz T, Malhotra AK. Effects of phase encoding direction on test-retest reliability of human functional connectome. Neuroimage 2023; 277:120238. [PMID: 37364743 PMCID: PMC10529794 DOI: 10.1016/j.neuroimage.2023.120238] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Miklos Argyelan
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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7
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Pollak C, Kügler D, Breteler MMB, Reuter M. Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts. Neuroimage 2023; 275:120176. [PMID: 37209757 DOI: 10.1016/j.neuroimage.2023.120176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023] Open
Abstract
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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Affiliation(s)
- Clemens Pollak
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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8
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Svedell LA, Holmqvist KL, Lindvall MA, Cao Y, Msghina M. Feasibility and tolerability of moderate intensity regular physical exercise as treatment for core symptoms of attention deficit hyperactivity disorder: a randomized pilot study. Front Sports Act Living 2023; 5:1133256. [PMID: 37255729 PMCID: PMC10225649 DOI: 10.3389/fspor.2023.1133256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Background Attention deficit hyperactivity disorder (ADHD) is associated with sedentary lifestyle, low quality of life and low physical fitness. Studies in children with ADHD have shown that regular physical exercise can help reduce core ADHD symptoms, but evidence for this is lacking in adults. Although guidelines recommend multi-modal treatment, central stimulants (CS) remain the mainstay of treatment. CS are effective in the short-term, but their long-term efficacy remains to be established. There is thus huge unmet need for developing non-pharmacological treatment options, and for well-designed randomized controlled trials (RCTs). Objective The study aimed to test the feasibility and tolerability of structured moderate-intensity 12-week physical exercise program for adults with ADHD, as a prelude to an adequately powered RCT which includes long-term follow-up. Materials and methods Fourteen adults with ADHD were recruited, 9 randomized to an intervention group and 5 to a control group. The intervention group received physiotherapist-led 50-minute mixed exercise program, three times a week for 12 weeks, and the control group treatment as usual. Participants were assessed at baseline and after 6 and 12 weeks using clinical and physical evaluations, self-rating questionnaires, and functional magnetic resonance imaging (fMRI) together with paradigms that tested attention, impulsivity and emotion regulation. Results Three participants (21%) dropped out shortly after inclusion before receiving any intervention, while roughly 80% completed the intervention according to protocol. One participant from the intervention group participated in less than 60% of treatment sessions, and one who had done baseline fMRI was unwilling to do post-intervention imaging. Four participants in the intervention group (67%) reported increased stress in prioritizing the intervention due to time-management difficulties. Overall, consistent trends were observed that indicated the feasibility and potential benefits of the intervention on core ADHD symptoms, quality of life, body awareness, sleep and cognitive functioning. Conclusion Physiotherapist-led twelve-week regular physical exercise is a feasible and potentially beneficial intervention for adults with ADHD. There was a 20% drop-out initially and 67% of those who completed the intervention reported stress with time management difficulties due to participation. A third arm was thus added to the planned RCT where cognitive intervention administered by an occupational therapist will be given together with physical exercise.Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT05049239.
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Affiliation(s)
- L. A. Svedell
- Department of Psychiatry, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - K. L. Holmqvist
- Department of Neurology and Rehabilitation Medicine, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- University Health Care Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - M. A. Lindvall
- University Health Care Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Y. Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - M. Msghina
- Department of Psychiatry, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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9
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Lepping RJ, Yeh HW, McPherson BC, Brucks MG, Sabati M, Karcher RT, Brooks WM, Habiger JD, Papa VB, Martin LE. Quality control in resting-state fMRI: the benefits of visual inspection. Front Neurosci 2023; 17:1076824. [PMID: 37214404 PMCID: PMC10192849 DOI: 10.3389/fnins.2023.1076824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 04/07/2023] [Indexed: 05/24/2023] Open
Abstract
Background A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or "scrubbing" volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection. Results The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts. Conclusion Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis.
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Affiliation(s)
- Rebecca J. Lepping
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Hung-Wen Yeh
- Division of Health Services and Outcomes Research, Department of Pediatrics, Children’s Mercy Research Institute, Kansas City, MO, United States
- Department of Pediatrics, School of Medicine, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Brent C. McPherson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Morgan G. Brucks
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
- Department of Population Health, University of Kansas Medical Center, Kansas City, KS, United States
| | - Mohammad Sabati
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
- Bioengineering Program, School of Engineering, University of Kansas, Lawrence, KS, United States
| | - Rainer T. Karcher
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - William M. Brooks
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Joshua D. Habiger
- Department of Statistics, Oklahoma State University, Stillwater, OK, United States
| | - Vlad B. Papa
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Laura E. Martin
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States
- Department of Population Health, University of Kansas Medical Center, Kansas City, KS, United States
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10
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Norman LJ, Sudre G, Price J, Shastri GG, Shaw P. Evidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples. Neuropsychopharmacology 2023; 48:281-289. [PMID: 36100657 PMCID: PMC9751118 DOI: 10.1038/s41386-022-01408-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/10/2022] [Accepted: 07/16/2022] [Indexed: 12/26/2022]
Abstract
We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.
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Affiliation(s)
- Luke J Norman
- Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Gustavo Sudre
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jolie Price
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Gauri G Shastri
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Philip Shaw
- Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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11
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Henry TR, Fogleman ND, Nugiel T, Cohen JR. Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD. Transl Psychiatry 2022; 12:518. [PMID: 36528602 PMCID: PMC9759578 DOI: 10.1038/s41398-022-02283-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/18/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.
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Affiliation(s)
- Teague R Henry
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA, USA.
| | - Nicholas D Fogleman
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tehila Nugiel
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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12
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Öztekin I, Garic D, Bayat M, Hernandez ML, Finlayson MA, Graziano PA, Dick AS. Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children. Eur J Neurosci 2022; 56:6239-6257. [PMID: 36215144 PMCID: PMC10165616 DOI: 10.1111/ejn.15842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/29/2022]
Abstract
The current study aimed to identify the key neurobiology of attention-deficit/hyperactivity disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. To do so, we adapted a predictive modelling approach to identify the key structural and diffusion-weighted brain imaging measures and their relative standing with respect to teacher ratings of executive function (EF) (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function [BRIEF]) and negativity and emotion regulation (ER) (measured by the Emotion Regulation Checklist [ERC]), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion-weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus as a critical predictor of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.
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Affiliation(s)
- Ilke Öztekin
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA.,Exponent, Inc., Philadelphia, Pennsylvania, USA
| | - Dea Garic
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mohammadreza Bayat
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Melissa L Hernandez
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Mark A Finlayson
- School of Computing and Information Sciences, Florida International University, Miami, Florida, USA
| | - Paulo A Graziano
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Anthony Steven Dick
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
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13
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Association between attention-deficit/hyperactivity disorder symptom severity and white matter integrity moderated by in-scanner head motion. Transl Psychiatry 2022; 12:434. [PMID: 36202807 PMCID: PMC9537185 DOI: 10.1038/s41398-022-02117-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 08/11/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder associated with various negative life impacts. The manifestation of ADHD is very heterogeneous, and previous investigations on neuroanatomical alterations in ADHD have yielded inconsistent results. We investigated the mediating effect of in-scanner head motion and ADHD hyperactivity severity on motion-corrected fractional anisotropy (FA) using diffusion tensor imaging in the currently largest sample (n = 739) of medication-naïve children and adolescents (age range 5-22 years). We used automated tractography to examine whole-brain and mean FA of the tracts most frequently reported in ADHD; corpus callosum forceps major and forceps minor, left and right superior-longitudinal fasciculus, and left and right corticospinal tract (CST). Associations between FA and hyperactivity severity appeared when in-scanner head motion was not accounted for as mediator. However, causal mediation analysis revealed that these effects are fully mediated through in-scanner head motion for whole-brain FA, the corpus callosum forceps minor, and left superior-longitudinal fasciculus. Direct effect of hyperactivity severity on FA was only found for the left CST. This study illustrates the crucial role of in-scanner head motion in the identification of white matter integrity alterations in ADHD and shows how neglecting irremediable motion artifacts causes spurious findings. When the mediating effect of in-scanner head motion on FA is accounted for, an association between hyperactivity severity and FA is only present for the left CST; this may play a crucial role in the manifestation of hyperactivity and impulsivity symptoms in ADHD.
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14
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Spisak T. Statistical quantification of confounding bias in machine learning models. Gigascience 2022; 11:giac082. [PMID: 36017878 PMCID: PMC9412867 DOI: 10.1093/gigascience/giac082] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/07/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded. RESULTS The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases. CONCLUSIONS The proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.
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Affiliation(s)
- Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, Institute for Diagnostic and Interventional Radiology and Neuroradiology, Center University Hospital Essen, Essen, D-45147, Germany
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15
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Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Mol Psychiatry 2022; 27:3129-3137. [PMID: 35697759 PMCID: PMC9708554 DOI: 10.1038/s41380-022-01635-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/09/2022] [Accepted: 05/18/2022] [Indexed: 12/11/2022]
Abstract
Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).
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Affiliation(s)
- Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Javid Dadashkarimi
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Michael C Farruggia
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - C Alice Hahn
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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16
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Nikolaidis A, He X, Pekar J, Rosch K, Mostofsky SH. Frontal corticostriatal functional connectivity reveals task positive and negative network dysregulation in relation to ADHD, sex, and inhibitory control. Dev Cogn Neurosci 2022; 54:101101. [PMID: 35338900 PMCID: PMC8956922 DOI: 10.1016/j.dcn.2022.101101] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 01/21/2023] Open
Abstract
Frontal corticostriatal circuits (FCSC) are involved in self-regulation of cognition, emotion, and motor function. While these circuits are implicated in attention-deficit/hyperactivity disorder (ADHD), the literature establishing FCSC associations with ADHD is inconsistent. This may be due to study variability in considerations of how fMRI motion regression was handled between groups, or study specific differences in age, sex, or the striatal subregions under investigation. Given the importance of these domains in ADHD it is crucial to consider the complex interactions of age, sex, striatal subregions and FCSC in ADHD presentation and diagnosis. In this large-scale study of 362 8-12 year-old children with ADHD (n = 165) and typically developing (TD; n = 197) children, we investigate associations between FCSC with ADHD diagnosis and symptoms, sex, and go/no-go (GNG) task performance. Results include: (1) increased striatal connectivity with age across striatal subregions with most of the frontal cortex, (2) increased frontal-limbic striatum connectivity among boys with ADHD only, mostly in default mode network (DMN) regions not associated with age, and (3) increased frontal-motor striatum connectivity to regions of the DMN were associated with greater parent-rated inattention problems, particularly among the ADHD group. Although diagnostic group differences were no longer significant when strictly controlling for head motion, with motion possibly reflecting the phenotypic variance of ADHD itself, the spatial distribution of all symptom, age, sex, and other ADHD group effects were nearly identical to the initial results. These results demonstrate differential associations of FCSC between striatal subregions with the DMN and FPN in relation to age, ADHD, sex, and inhibitory control.
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Affiliation(s)
- Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, USA.
| | - Xiaoning He
- Center for the Developing Brain, Child Mind Institute, USA
| | - James Pekar
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, USA; Department of Radiology, Johns Hopkins University School of Medicine, USA
| | - Keri Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA; Department of Neuropsychology, Kennedy Krieger Institute, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA; Department of Neurology, Johns Hopkins University School of Medicine, USA
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17
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Zhao Y, Nebel MB, Caffo BS, Mostofsky SH, Rosch KS. Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:8-16. [PMID: 35528865 PMCID: PMC9074810 DOI: 10.1016/j.bpsgos.2021.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study, we apply a novel whole-matrix regression approach referred to as covariate assisted principal regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods Participants included 8- to 12-year-old children with ADHD (n = 115; 29 girls) and typically developing control children (n = 102; 35 girls) who completed a resting-state functional magnetic resonance imaging scan and a Go/NoGo task. We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models, suggesting some specificity to the covariates of interest. Conclusions These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.
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18
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Furlong S, Cohen JR, Hopfinger J, Snyder J, Robertson MM, Sheridan MA. Resting-state EEG Connectivity in Young Children with ADHD. JOURNAL OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY : THE OFFICIAL JOURNAL FOR THE SOCIETY OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY, AMERICAN PSYCHOLOGICAL ASSOCIATION, DIVISION 53 2021; 50:746-762. [PMID: 32809852 PMCID: PMC7889746 DOI: 10.1080/15374416.2020.1796680] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Objective: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder. While early childhood is a crucial time for early intervention, it is characterized by instability of ADHD diagnosis. Neural correlates of ADHD have potential to improve diagnostic accuracy; however, minimal research has focused on early childhood. Research indicates that disrupted neural connectivity is associated with ADHD in older children. Here, we explore network connectivity as a potential neural correlate of ADHD diagnosis in early childhood.Method: We collected EEG data in 52 medication-naïve children with ADHD and in 77 typically developing controls (3-7 years). Data was collected with the EGI 128 HydroCel Sensor Net System, but to optimize the ICA, the data was down sampled to the 10-10 system. Connectivity was measured as the synchronization of the time series of each pair of electrodes. Subsequent analyses utilized graph theoretical methods to further characterize network connectivity.Results: Increased global efficiency, which measures the efficiency of information transfer across the entire brain, was associated with increased inattentive symptom severity. Further, this association was robust to controls for age, IQ, SES, and internalizing psychopathology.Conclusions: Overall, our findings indicate that increased global efficiency, which suggests a hyper-connected neural network, is associated with elevated ADHD symptom severity. These findings extend previous work reporting disruption of neural network connectivity in older children with ADHD into early childhood.
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Affiliation(s)
- Sarah Furlong
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph Hopfinger
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jenna Snyder
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA
| | - Madeline M. Robertson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Margaret A. Sheridan
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA
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19
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Thomson P, Johnson KA, Malpas CB, Efron D, Sciberras E, Silk TJ. Head Motion During MRI Predicted by out-of-Scanner Sustained Attention Performance in Attention-Deficit/Hyperactivity Disorder. J Atten Disord 2021; 25:1429-1440. [PMID: 32189534 DOI: 10.1177/1087054720911988] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Objective: To characterize head movements in children with ADHD using an ex-Gaussian distribution and examine associations with out-of-scanner sustained attention. Method: Fifty-six children with ADHD and 61 controls aged 9 to 11 years completed the Sustained Attention to Response Task (SART) and resting-state functional magnetic resonance imaging (fMRI). In-scanner head motion was calculated using ex-Gaussian estimates for mu, sigma, and tau in delta variation signal and framewise displacement. Sustained attention was evaluated through omission errors and tau in response time on the SART. Results: Mediation analysis revealed that out-of-scanner attention lapses (omissions during the SART) mediated the relationship between ADHD diagnosis and in-scanner head motion (tau in delta variation signal), indirect effect: B = 1.29, 95% confidence interval (CI) = [0.07, 3.15], accounting for 29% of the association. Conclusion: Findings suggest a critical link between trait-level sustained attention and infrequent large head movements during scanning (tau in head motion) and highlight fundamental challenges in measuring the neural basis of sustained attention.
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Affiliation(s)
- Phoebe Thomson
- The University of Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | | | - Charles B Malpas
- The University of Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Daryl Efron
- The University of Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia.,The Royal Children's Hospital, Parkville, Victoria, Australia
| | - Emma Sciberras
- The University of Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia.,The Royal Children's Hospital, Parkville, Victoria, Australia.,Deakin University, Burwood, Victoria, Australia
| | - Timothy J Silk
- The University of Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia.,Deakin University, Burwood, Victoria, Australia
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20
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Duffy KA, Rosch KS, Nebel MB, Seymour KE, Lindquist MA, Pekar JJ, Mostofsky SH, Cohen JR. Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control. Dev Cogn Neurosci 2021; 50:100980. [PMID: 34252881 PMCID: PMC8278154 DOI: 10.1016/j.dcn.2021.100980] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/03/2021] [Accepted: 06/17/2021] [Indexed: 01/22/2023] Open
Abstract
Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region’s inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD. The DMN is more integrated with task-relevant networks in children with ADHD. Higher and less variable DMN integration relates to poorer response control in ADHD. DMN dysfunction may play a key role in response control deficits in ADHD.
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Affiliation(s)
- Kelly A Duffy
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Karen E Seymour
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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21
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He N, Palaniyappan L, Linli Z, Guo S. Abnormal hemispheric asymmetry of both brain function and structure in attention deficit/hyperactivity disorder: a meta-analysis of individual participant data. Brain Imaging Behav 2021; 16:54-68. [PMID: 34021487 DOI: 10.1007/s11682-021-00476-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 11/25/2022]
Abstract
Aberration in the asymmetric nature of the human brain is associated with several mental disorders, including attention deficit/hyperactivity disorder (ADHD). In ADHD, these aberrations are thought to reflect key hemispheric differences in the functioning of attention, although the structural and functional bases of these defects are yet to be fully characterized. In this study, we applied a comprehensive meta-analysis to multimodal imaging datasets from 627 subjects (303 typically developing control [TDCs] and 324 patients with ADHD) with both resting-state functional and structural magnetic resonance imaging (MRI), from seven independent publicly available datasets of the ADHD-200 sample. We performed lateralization analysis and calculated the combined effects of ADHD on each of three cortical regional measures (grey matter volume - GMV, fractional amplitude of low frequency fluctuations at rest -fALFF, and regional homogeneity -ReHo). We found that compared with TDC, 68%,73% and 66% of regions showed statistically significant ADHD disorder effects on the asymmetry of GMV, fALFF, and ReHo, respectively, (false discovery rate corrected, q = 0.05). Forty-one percent (41%) of regions had both structural and functional abnormalities in asymmetry, located in the prefrontal, frontal, and subcortical cortices, and the cerebellum. Furthermore, brain asymmetry indices in these regions were higher in children with more severe ADHD symptoms, indicating a crucial pathoplastic role for asymmetry. Our findings highlight the functional asymmetry in ADHD which has (1) a strong structural basis, and thus is likely to be developmental in nature; and (2) is strongly linked to symptom burden and IQ and may carry a possible prognostic value for grading the severity of ADHD.
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Affiliation(s)
- Ningning He
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China.
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, Changsha, People's Republic of China.
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, Changsha, People's Republic of China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China.
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, Changsha, People's Republic of China.
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22
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Pereira-Sanchez V, Franco AR, Vieira D, de Castro-Manglano P, Soutullo C, Milham MP, Castellanos FX. Systematic Review: Medication Effects on Brain Intrinsic Functional Connectivity in Patients With Attention-Deficit/Hyperactivity Disorder. J Am Acad Child Adolesc Psychiatry 2021; 60:222-235. [PMID: 33137412 DOI: 10.1016/j.jaac.2020.10.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 10/24/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Resting-state functional magnetic resonance imaging (R-fMRI) studies of the neural correlates of medication treatment in attention-deficit/hyperactivity disorder (ADHD) have not been systematically reviewed. Our objective was to systematically identify, assess and summarize within-subject R-fMRI studies of pharmacological-induced changes in patients with ADHD. We critically appraised strengths and limitations, and provide recommendations for future research. METHOD Systematic review of published original reports in English meeting criteria in pediatric and adult patients with ADHD up to July 1, 2020. A thorough search preceded selection of studies matching prespecified criteria. Strengths and limitations of selected studies, regarding design and reporting, were identified based on current best practices. RESULTS We identified and reviewed 9 studies (5 pediatric and 4 adult studies). Sample sizes were small-medium (16-38 patients), and included few female participants. Medications were methylphenidate, amphetamines, and atomoxetine. Wide heterogeneity was observed in designs, analyses and results, which could not be combined quantitatively. Qualitatively, the multiplicity of brain regions and networks identified, some of which correlated with clinical improvements, do not support a coherent mechanistic hypothesis of medication effects. Overall, reports did not meet current standards to ensure reproducibility. CONCLUSION In this emerging field, the few studies using R-fMRI to analyze the neural correlates of medications in patients with ADHD suggest a potential modulatory effect of stimulants and atomoxetine on several intrinsic brain activity metrics. However, methodological heterogeneity and reporting issues need to be addressed in future research to validate findings which may contribute to clinical care. Such a goal is not yet at hand.
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Affiliation(s)
- Victor Pereira-Sanchez
- NYU Grossman School of Medicine, New York, New York; Clinica Universidad de Navarra, Pamplona, Navarra, Spain.
| | - Alexandre R Franco
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Child Mind Institute, New York, New York
| | | | | | | | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Child Mind Institute, New York, New York
| | - Francisco X Castellanos
- NYU Grossman School of Medicine, New York, New York; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
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23
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Shappell HM, Duffy KA, Rosch KS, Pekar JJ, Mostofsky SH, Lindquist MA, Cohen JR. Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis. Neuroimage 2021; 229:117753. [PMID: 33454408 DOI: 10.1016/j.neuroimage.2021.117753] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/17/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.
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Affiliation(s)
- Heather M Shappell
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Kelly A Duffy
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Shafer AT, Benoit JR, Brown MRG, Greenshaw AJ, Van Vliet KJ, Vohra S, Dolcos F, Singhal A. Differences in attentional control and white matter microstructure in adolescents with attentional, affective, and behavioral disorders. Brain Imaging Behav 2021; 14:599-614. [PMID: 31838614 DOI: 10.1007/s11682-019-00211-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Adolescence is a critical time of physiological, cognitive, and social development. It is also a time of increased risk-taking and vulnerability for psychopathology. White matter (WM) changes during adolescence have been better elucidated in the last decade, but how WM is impacted by psychopathology during this time remains unclear. Here, we examined the link between WM microstructure and psychopathology during adolescence. Twenty youth diagnosed with affective, attentional, and behavioral disorders (clinical sample), and 20 age-matched controls were recruited to examine group differences in WM microstructure, attentional control, and the link between them. The main results showed that clinical sample had relatively lower attentional control and fractional anisotropy (FA) in WM throughout the brain: two association tracts were identified, and many differences were found in areas rich in callosal and projection fibers. Moreover, increased FA was positively associated with attention performance in the clinical sample in structures supporting ventral WM pathways, whereas a similar link was identified in controls in dorsal WM association fibers. Overall, these results support a model of general impairment in WM microstructure combined with reliance on altered, perhaps less efficient, pathways for attentional control in youth with affective, attentional, and behavioral disorders.
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Affiliation(s)
- Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
| | - James R Benoit
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Matthew R G Brown
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Andy J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - K Jessica Van Vliet
- Department of Educational Psychology, University of Alberta, Edmonton, AB, Canada
| | - Sunita Vohra
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Departments of Pediatrics and Medicine, University of Alberta, Edmonton, AB, Canada
| | - Florin Dolcos
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.,Psychology Department and Neuroscience Program, University of Illinois, Urbana-Champaign, IL, USA.,Beckman Institute for Advanced Science & Technology, University of Illinois, Urbana-Champaign, IL, USA
| | - Anthony Singhal
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. .,Department of Psychology, University of Alberta, Edmonton, AB, Canada.
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25
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Vandewouw MM, Dunkley BT, Lerch JP, Anagnostou E, Taylor MJ. Characterizing Inscapes and resting-state in MEG: Effects in typical and atypical development. Neuroimage 2020; 225:117524. [PMID: 33147510 DOI: 10.1016/j.neuroimage.2020.117524] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Examining the brain at rest is a powerful approach used to understand the intrinsic properties of typical and disordered human brain function, yet task-free paradigms are associated with greater head motion, particularly in young and/or clinical populations such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Inscapes, a non-social and non-verbal movie paradigm, has been introduced to increase attention, thus mitigating head motion, while reducing the task-induced activations found during typical movie watching. Inscapes has not yet been validated for use in magnetoencephalography (MEG), and it has yet to be shown whether its effects are stable in clinical populations. Across typically developing (N = 32) children and adolescents and those with ASD (N = 46) and ADHD (N = 42), we demonstrate that head motion is reduced during Inscapes. Due to the task state evoked by movie paradigms, we also expectedly observed concomitant modulations in local neural activity (oscillatory power) and functional connectivity (phase and envelope coupling) in intrinsic resting-state networks and across the frequency spectra compared to a fixation cross resting-state. Increases in local activity were accompanied by decreases in low-frequency connectivity within and between resting-state networks, primarily the visual network, suggesting that task-state evoked by Inscapes moderates ongoing and spontaneous cortical inhibition that forms the idling intrinsic networks found during a fixation cross resting-state. Importantly, these effects were similar in ASD and ADHD, making Inscapes a well-suited advancement for investigations of resting brain function in young and clinical populations.
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Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
| | - Benjamin T Dunkley
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Evdokia Anagnostou
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
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26
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Privratsky AA, Bush KA, Bach DR, Hahn EM, Cisler JM. Filtering and model-based analysis independently improve skin-conductance response measures in the fMRI environment: Validation in a sample of women with PTSD. Int J Psychophysiol 2020; 158:86-95. [PMID: 33075428 DOI: 10.1016/j.ijpsycho.2020.09.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 08/29/2020] [Accepted: 09/05/2020] [Indexed: 11/25/2022]
Abstract
Numerous methods exist for the pre-processing and analysis of skin-conductance response (SCR) data, but there is incomplete consensus on suitability and implementation, particularly with regard to signal filtering in conventional peak score (PS) analysis. This is particularly relevant when SCRs are measured during fMRI, which introduces additional noise and signal variability. Using SCR-fMRI data (n = 65 women) from a fear conditioning experiment, we compare the impact of three nested data processing methods on analysis using conventional PS as well as psychophysiological modeling. To evaluate the different methods, we quantify effect size to recover a benchmark contrast of interest, namely, discriminating SCR magnitude to a conditioned stimulus (CS+) relative to a CS not followed by reinforcement (CS-). Findings suggest that low-pass filtering reduces PS sensitivity (Δd = -20%), while band-pass filtering improves PS sensitivity (Δd = +27%). We also replicate previous findings that a psychophysiological modeling approach yields superior sensitivity to detect contrasts of interest than even the most sensitive PS method (Δd = +110%). Furthermore, we present preliminary evidence that filtering differences may account for a portion of exclusions made on commonly applied metrics, such as below zero discrimination. Despite some limitations of our sample and experimental design, it appears that SCR processing pipelines that include band-pass filtering, ideally with model-based SCR quantification, may increase the validity of SCR response measures, maximize research productivity, and decrease sampling bias by reducing data exclusion.
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Affiliation(s)
- Anthony A Privratsky
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock 72205, AR, USA.
| | - Keith A Bush
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock 72205, AR, USA
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging and Max-Planck UCL Centre for Computational Psychiatry and Ageing, 12 Queen Square, University College London, London WC1N 3BG, United Kingdom; Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, 8032 Zurich, Switzerland
| | - Emily M Hahn
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth St, Charlestown 02129, MA, USA
| | - Josh M Cisler
- Department of Psychiatry, University of Wisconsin-Madison, Madison 53726, WI, USA
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27
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Beyer F, Prehn K, Wüsten KA, Villringer A, Ordemann J, Flöel A, Witte AV. Weight loss reduces head motion: Revisiting a major confound in neuroimaging. Hum Brain Mapp 2020; 41:2490-2494. [PMID: 32239733 PMCID: PMC7267971 DOI: 10.1002/hbm.24959] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/17/2020] [Accepted: 02/11/2020] [Indexed: 01/09/2023] Open
Abstract
Head motion during magnetic resonance imaging (MRI) induces image artifacts that affect virtually every brain measure. In parallel, cross‐sectional observations indicate a correlation of head motion with age, psychiatric disease status and obesity, raising the possibility of a systematic artifact‐induced bias in neuroimaging outcomes in these conditions, due to the differences in head motion. Yet, a causal link between obesity and head motion has not been tested in an experimental design. Here, we show that a change in body mass index (BMI) (i.e., weight loss after bariatric surgery) systematically decreases head motion during MRI. In this setting, reduced imaging artifacts due to lower head motion might result in biased estimates of neural differences induced by changes in BMI. Overall, our finding urges the need to rigorously control for head motion during MRI to enable valid results of neuroimaging outcomes in populations that differ in head motion due to obesity or other conditions.
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Affiliation(s)
- Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Kristin Prehn
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Psychology, Medical School Hamburg, Hamburg, Germany
| | - Katharina A Wüsten
- Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Jürgen Ordemann
- Center for Bariatric and Metabolic Surgery, Charité University Medicine, Berlin, Germany.,Zentrum für Adipositas und Metabolische Chirurgie, Vivantes Klinikum Spandau, Berlin, Germany
| | - Agnes Flöel
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany.,Center for Stroke Research, Charité University Medicine, Berlin, Germany
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
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28
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Brunkhorst-Kanaan N, Verdenhalven M, Kittel-Schneider S, Vainieri I, Reif A, Grimm O. The Quantified Behavioral Test-A Confirmatory Test in the Diagnostic Process of Adult ADHD? Front Psychiatry 2020; 11:216. [PMID: 32265761 PMCID: PMC7100366 DOI: 10.3389/fpsyt.2020.00216] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 03/05/2020] [Indexed: 11/13/2022] Open
Abstract
The differential diagnosis of attention deficit hyperactivity disorder (ADHD) in adulthood is complicated by comorbid disorders, but also by the overlapping of main symptoms such as inattentiveness, impulsivity, and hyperactivity with other disorders. Neuropsychological tests like continuous performance tests (CPT) try to solve this dilemma by objectively measurable parameters. We investigated in a cohort of n=114 patients presenting to an ADHD outpatient clinic how well a commercially available CPT test (QbTest®) can differentiate between patients with ADHD (n=94) and patients with a disconfirmed ADHD diagnosis (n=20). Both groups showed numerous comorbidities, predominantly depression (27.2% in the ADHD group vs. 45% in the non-ADHD group) and substance-use disorders (18.1% vs. 10%, respectively). Patients with ADHD showed significant higher activity (2.07 ± 1.23) than patients without ADHD (1.34 ± 1.27, dF=112; p=0.019), whereas for the other core parameters, inattention and impulsivity no differences could be found. Reaction time variability has been discussed as a typical marker for inattention in ADHD. Therefore, we investigated how well ex-Gaussian analysis of response time can differentiate between ADHD and other patients, showing, that it does not help to identify patients with ADHD. Even though patients with ADHD showed significantly higher activity, this parameter differed only poorly between patients (accuracy AUC 65% of an ROC-Curve). We conclude that CPTs do not help to identify patients with ADHD in a specialized outpatient clinic. The usability of this test for differentiating between ADHD and other psychiatric disorders is poor and a sophisticated analysis of reaction time did not decisively increase the test accuracy.
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Affiliation(s)
- Nathalie Brunkhorst-Kanaan
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Moritz Verdenhalven
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Würzburg, Würzburg, Germany
| | - Isabella Vainieri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
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29
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Siless V, Hubbard NA, Jones R, Wang J, Lo N, Bauer CCC, Goncalves M, Frosch I, Norton D, Vergara G, Conroy K, De Souza FV, Rosso IM, Wickham AH, Cosby EA, Pinaire M, Hirshfeld-Becker D, Pizzagalli DA, Henin A, Hofmann SG, Auerbach RP, Ghosh S, Gabrieli J, Whitfield-Gabrieli S, Yendiki A. Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study. Neuroimage Clin 2020; 26:102242. [PMID: 32339824 PMCID: PMC7184183 DOI: 10.1016/j.nicl.2020.102242] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/19/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA).
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Affiliation(s)
- Viviana Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicholas A Hubbard
- Massachusetts Institute of Technology, Cambridge, MA, United States; University of Nebraska, Lincoln, Lincoln, NE, United States
| | - Robert Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jonathan Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicole Lo
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Clemens C C Bauer
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | | | - Isabelle Frosch
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Daniel Norton
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | - Isabelle M Rosso
- McLean Hospital, Belmont, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | | | | | - Aude Henin
- Massachusetts General Hospital, Boston, MA, United States
| | | | | | - Satrajit Ghosh
- Harvard Medical School, Boston, MA, United States; Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susan Whitfield-Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
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30
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Maziero D, Rondinoni C, Marins T, Stenger VA, Ernst T. Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion. Neuroimage 2020; 212:116594. [PMID: 32044436 DOI: 10.1016/j.neuroimage.2020.116594] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 12/30/2019] [Accepted: 01/29/2020] [Indexed: 11/19/2022] Open
Abstract
The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.
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Affiliation(s)
- Danilo Maziero
- MR Research Program, Department of Medicine, John A. Burns School of Medicine, University of Hawai'i, HI, USA.
| | - Carlo Rondinoni
- Department of Radiology, University of São Paulo, São Paulo, S.P, Brazil
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Victor Andrew Stenger
- MR Research Program, Department of Medicine, John A. Burns School of Medicine, University of Hawai'i, HI, USA
| | - Thomas Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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31
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Bolton TAW, Kebets V, Glerean E, Zöller D, Li J, Yeo BTT, Caballero-Gaudes C, Van De Ville D. Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. Neuroimage 2019; 209:116433. [PMID: 31841680 DOI: 10.1016/j.neuroimage.2019.116433] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 11/11/2019] [Accepted: 12/02/2019] [Indexed: 12/21/2022] Open
Abstract
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.
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Affiliation(s)
- Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| | - Valeria Kebets
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Daniela Zöller
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Developmental Imaging and Psychopathology Laboratory, Office Médico-Pédagogique, Department of Psychiatry, University of Geneva (UNIGE), Geneva, Switzerland
| | - Jingwei Li
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | | | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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32
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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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33
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Burton CL, Wright L, Shan J, Xiao B, Dupuis A, Goodale T, Shaheen SM, Corfield EC, Arnold PD, Schachar RJ, Crosbie J. SWAN scale for ADHD trait-based genetic research: a validity and polygenic risk study. J Child Psychol Psychiatry 2019; 60:988-997. [PMID: 30908652 DOI: 10.1111/jcpp.13032] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2018] [Indexed: 12/31/2022]
Abstract
BACKGROUND Population-based samples with valid, quantitative and genetically informative trait measures of psychopathology could be a powerful complement to case/control genetic designs. We report the convergent and predictive validity of the parent- and self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN). We tested if SWAN scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as traits and polygenic risk for disorders that co-occur with ADHD: anxiety and obsessive-compulsive disorder (OCD). METHODS We collected parent- and self-report SWAN scores in a sample of 15,560 children and adolescents (6-17 years) recruited at a science museum (Spit for Science sample). We established age and sex norms for the SWAN. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with and without a reported ADHD diagnosis. These cut-points were validated in a clinic sample (266 ADHD cases; 36 controls). Convergent validity was established using the Conners' parent- and self-report scales. Using Spit for Science participants with genome-wide data (n = 5,154), we tested if low, medium and high SWAN scores were associated with polygenic risk for ADHD, OCD and anxiety disorders. RESULTS Parent- and self-report SWAN scores showed high convergent validity with Conners' scales and distinguished ADHD participants with high sensitivity and specificity in the Spit for Science sample. In a clinic sample, the Spit for Science cut-points discriminated ADHD cases from controls with a sensitivity of 84% and specificity of 92%. High SWAN scores and scores above the Spit for Science cut-points were significantly associated with polygenic risk for ADHD. SWAN scores were not associated with polygenic risk for OCD or anxiety disorders. CONCLUSIONS Our study supports the validity of the parent- and self-report SWAN scales and their potential in ADHD population-based genetic research.
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Affiliation(s)
- Christie L Burton
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Leah Wright
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Janet Shan
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Bowei Xiao
- Genetics and Genome Biology Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Annie Dupuis
- Clinical Research Services, Hospital for Sick Children, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Tara Goodale
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - S-M Shaheen
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada.,Departments of Psychiatry & Medical Genetics, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elizabeth C Corfield
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Paul D Arnold
- Genetics and Genome Biology Program, Hospital for Sick Children, Toronto, ON, Canada.,Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada.,Departments of Psychiatry & Medical Genetics, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Russell J Schachar
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jennifer Crosbie
- Neurosciences and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Stange JP, Jenkins LM, Bessette KL, Kling LR, Bark JS, Shepard R, Hamlat EJ, DelDonno S, Phan KL, Passarotti AM, Ajilore O, Langenecker SA. Predictors of Attrition in Longitudinal Neuroimaging Research: Inhibitory Control, Head Movement, and Resting-State Functional Connectivity. Brain Connect 2019; 8:527-536. [PMID: 30411975 DOI: 10.1089/brain.2018.0619] [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] [Indexed: 01/21/2023] Open
Abstract
Attrition is a major problem in longitudinal neuroimaging studies, as it may lead to unreliable estimates of the stability of trait-like processes over time, of the identification of risk factors for clinical outcomes, and of the effects of treatment. Identification of characteristics associated with attrition has implications for participant recruitment and participant retention to achieve representative longitudinal samples. We investigated inhibitory control deficits, head motion, and resting-state functional connectivity within the cognitive control network (CCN) as predictors of attrition. Ninety-seven individuals with remitted major depressive disorder or healthy controls completed a functional magnetic resonance imaging scan, which included a go/no-go task and resting-state functional connectivity. Approximately 2 months later, participants were contacted and invited to return for a second scan. Seventeen individuals were lost to follow-up or declined to participate in the follow-up scan. Worse inhibitory control was correlated with greater movement within the scanner, and each predicted a greater likelihood of attrition, with movement mediating the effects of inhibitory control on attrition. Individuals who dropped out of the study exhibited greater movement than nondropouts across 9 of the 14 runs of the scan, with medium-to-large effect sizes. Finally, exploratory analyses suggested that attenuated resting-state connectivity with the CCN (particularly in bilateral dorsolateral prefrontal cortex) was associated with greater likelihood of attrition after accounting for head motion at several levels of analysis. Inhibitory control and movement within the scanner are associated with attrition, and should be considered for strategic oversampling and participant retention strategies to ensure generalizability of results in longitudinal studies.
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Affiliation(s)
- Jonathan P Stange
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | | | - Katie L Bessette
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - Leah R Kling
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - John S Bark
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - Robert Shepard
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - Elissa J Hamlat
- 3 University of Illinois Urbana-Champaign , Urbana, Illinois
| | - Sophie DelDonno
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - K Luan Phan
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | | | - Olusola Ajilore
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
| | - Scott A Langenecker
- 1 Department of Psychiatry, University of Illinois at Chicago , Chicago, Illinois
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35
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Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls. Neuroimage 2019; 202:116073. [PMID: 31386921 DOI: 10.1016/j.neuroimage.2019.116073] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/27/2019] [Accepted: 08/02/2019] [Indexed: 12/11/2022] Open
Abstract
The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.
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36
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Age moderates the relationship between cortical thickness and cognitive performance. Neuropsychologia 2019; 132:107136. [PMID: 31288025 DOI: 10.1016/j.neuropsychologia.2019.107136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/15/2019] [Accepted: 07/05/2019] [Indexed: 12/30/2022]
Abstract
Findings from cross-sectional and longitudinal magnetic resonance imaging (MRI) studies indicate that cortical thickness declines across the adult lifespan, with regional differences in rate of decline. Global and regional thickness have also been found to co-vary with cognitive performance. Here we examined the relationships between age, mean cortical thickness, and associative recognition performance across three age groups (younger, middle-aged and older adults; total n = 133). Measures of cortical thickness were obtained using a semi-automated method. Older age was associated with decreased memory performance and a reduction in mean cortical thickness. After controlling for the potentially confounding effects of head motion, mean cortical thickness was negatively associated with associative memory performance in the younger participants, but was positively correlated with performance in older participants. A similar but weaker pattern was evident in the relationships between cortical thickness and scores on four cognitive constructs derived from a neuropsychological test battery. This pattern is consistent with prior findings indicating that the direction of the association between cortical thickness and cognitive performance reverses between early and later adulthood. In addition, head motion was independently and negatively correlated with associative recognition performance in younger and middle-aged, but not older, participants, suggesting that variance in head motion is determined by multiple factors that vary in their relative influences with age.
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37
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Wang M, Cui J, Liu Y, Zhou Y, Wang H, Wang Y, Zhu Y, Nguchu BA, Qiu B, Wang X, Yu Y. Structural and functional abnormalities of vision-related brain regions in cirrhotic patients: a MRI study. Neuroradiology 2019; 61:695-702. [PMID: 30949745 PMCID: PMC6511351 DOI: 10.1007/s00234-019-02199-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/12/2019] [Indexed: 12/20/2022]
Abstract
Purpose Previous studies have focused on global cerebral alterations observed in cirrhosis. However, little was known about the specific abnormalities of vision-related brain regions in cirrhotic patients. In this study, we sought to explore neurological alterations of vision-related regions by measuring brain resting-state network connectivity, based on the structural investigation in cirrhotic patients without clinical sign of hepatic encephalopathy (HE). Methods Structural and functional magnetic resonance image (MRI) data were collected from 20 hepatitis B virus (HBV)-related cirrhotic patients without clinical sign of HE and from 20 healthy controls (HC). Voxel-based morphometric (VBM) analysis and brain functional network analysis were performed to detect abnormalities in cerebral structure and function. Results Cirrhotic patients showed regions with the most significant gray matter reduction primarily in vision-related brain regions, including the bilateral lingual gyri, left putamen, right fusiform gyrus, and right calcarine gyrus, and other significant gray matter reductions were distributed in bilateral hippocampus. Based on structural investigation focused on vision-related regions, brain functional network analysis revealed decreased functional connectivity between brain functional networks within vision-related regions (primary visual network (PVN), higher visual network (HVN), visuospatial network (VSN)) in the patient group compared with HC group. Conclusion These results indicate that structural and functional impairment were evident in the vision-related brain regions in cirrhotic patients without clinical sign of hepatic encephalopathy. The physiopathology and clinical relevance of these changes could not be ascertained from the present study, which provided a basis for further evolution of the disease.
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Affiliation(s)
- Mingquan Wang
- Department of Radiology, The First Affiliated Hospital of AnHui Medical University, Hefei, 230022, Anhui, China
| | - Jin Cui
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Yanpeng Liu
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Yawen Zhou
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Huijuan Wang
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Yanming Wang
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Yuying Zhu
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | | | - Bensheng Qiu
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaoxiao Wang
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of AnHui Medical University, Hefei, 230022, Anhui, China.
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38
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Naz S, Najam N. Neurological deficits and comorbidity in children with reading disorder. PSYCHIAT CLIN PSYCH 2019. [DOI: 10.1080/24750573.2019.1589174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Affiliation(s)
- Sajida Naz
- Department of Behavioral Sciences, Fatima Jinnah Women University, Rawalpindi, Pakistan
| | - Najma Najam
- Department of Behavioral Sciences, Fatima Jinnah Women University, Rawalpindi, Pakistan
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39
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Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI. Sci Rep 2019; 9:419. [PMID: 30674933 PMCID: PMC6344520 DOI: 10.1038/s41598-018-36699-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/22/2018] [Indexed: 01/02/2023] Open
Abstract
Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n = 464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9–11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5–6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2–12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.
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40
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Kottaram A, Johnston LA, Cocchi L, Ganella EP, Everall I, Pantelis C, Kotagiri R, Zalesky A. Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Hum Brain Mapp 2019; 40:2212-2228. [PMID: 30664285 DOI: 10.1002/hbm.24519] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/06/2018] [Accepted: 12/26/2018] [Indexed: 02/03/2023] Open
Abstract
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Victoria, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia
| | - Ian Everall
- Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Psychology and Neuroscience, Institute of Psychiatry, Kings College London, London, United Kingdom.,South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia.,Department of Electrical and Electronic Engineering, Centre for Neural Engineering, The University of Melbourne, Victoria, Australia.,North Western Mental Health, Melbourne Health, Victoria, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia
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Rosch KS, Mostofsky SH, Nebel MB. ADHD-related sex differences in fronto-subcortical intrinsic functional connectivity and associations with delay discounting. J Neurodev Disord 2018; 10:34. [PMID: 30541434 PMCID: PMC6292003 DOI: 10.1186/s11689-018-9254-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 11/14/2018] [Indexed: 01/12/2023] Open
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is associated with atypical fronto-subcortical neural circuitry and heightened delay discounting, or a stronger preference for smaller, immediate rewards over larger, delayed rewards. Recent evidence of ADHD-related sex differences in brain structure and function suggests anomalies in fronto-subcortical circuitry may differ among girls and boys with ADHD. The current study examined whether the functional connectivity (FC) within fronto-subcortical neural circuitry differs among girls and boys with ADHD compared to same-sex typically developing (TD) controls and relates to delay discounting. Methods Participants include 8–12-year-old children with ADHD (n = 72, 20 girls) and TD controls (n = 75, 21 girls). Fronto-subcortical regions of interest were functionally defined by applying independent component analysis to resting-state fMRI data. Intrinsic FC between subcortical components, including the striatum and amygdala, and prefrontal components, including ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and anterior dorsolateral prefrontal cortex (dlPFC), was compared across diagnostic groups overall and within sex. Correlations between intrinsic FC of the six fronto-subcortical pairs and delay discounting were also examined. Results Both girls and boys with ADHD show atypical FC between vmPFC and subcortical regions including the striatum (stronger positive FC in ADHD) and amygdala (weaker negative FC in ADHD), with the greatest diagnostic effects among girls. In addition, girls with ADHD show atypical intrinsic FC between the striatum and dlPFC components, including stronger positive FC with ACC and stronger negative FC with dlPFC. Further, girls but not boys, with ADHD, show heightened real-time delay discounting. Brain–behavior correlations suggest (1) stronger negative FC between the striatal and dlPFC components correlated with greater money delay discounting across all participants and (2) stronger FC between the amygdala with both the dlPFC and ACC components was differentially related to heightened real-time discounting among girls and boys with and without ADHD. Conclusions Our findings suggest fronto-subcortical functional networks are affected in children with ADHD, particularly girls, and relate to delay discounting. These results also provide preliminary evidence of greater disruptions in fronto-subcortical FC among girls with ADHD that is not due to elevated inattention symptom severity, intellectual reasoning ability, age, or head motion. Electronic supplementary material The online version of this article (10.1186/s11689-018-9254-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA. .,Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, 21205, USA. .,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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de Lacy N, Calhoun VD. Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder. Netw Neurosci 2018; 3:195-216. [PMID: 30793080 PMCID: PMC6372020 DOI: 10.1162/netn_a_00063] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 06/05/2018] [Indexed: 11/04/2022] Open
Abstract
The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.
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Affiliation(s)
- Nina de Lacy
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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43
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Ma L, Steinberg JL, Bjork JM, Keyser-Marcus L, Vassileva J, Zhu M, Ganapathy V, Wang Q, Boone EL, Ferré S, Bickel WK, Gerard Moeller F. Fronto-striatal effective connectivity of working memory in adults with cannabis use disorder. Psychiatry Res Neuroimaging 2018; 278:21-34. [PMID: 29957349 PMCID: PMC6953485 DOI: 10.1016/j.pscychresns.2018.05.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 05/21/2018] [Accepted: 05/21/2018] [Indexed: 10/14/2022]
Abstract
Previous working memory (WM) studies found that relative to controls, subjects with cannabis use disorder (CUD) showed greater brain activation in some regions (e.g., left [L] and right [R] ventrolateral prefrontal cortex [VLPFC], and L dorsolateral prefrontal cortex [L-DLPFC]), and lower activation in other regions (e.g., R-DLPFC). In this study, effective connectivity (EC) analysis was applied to functional magnetic resonance imaging data acquired from 23 CUD subjects and 23 controls (two groups matched for sociodemographic factors and substance use history) while performing an n-back WM task with interleaved 2-back and 0-back periods. A 2-back minus 0-back modulator was defined to measure the modulatory changes of EC corresponding to the 2-back relative to 0-back conditions. Compared to the controls, the CUD group showed smaller modulatory change in the R-DLPFC to L-caudate pathway, and greater modulatory changes in L-DLPFC to L-caudate, R-DLPFC to R-caudate, and R-VLPFC to L-caudate pathways. Based on previous fMRI studies consistently suggesting that greater brain activations are related to a compensatory mechanism for cannabis neural effects (less regional brain activations), the smaller modulatory change in the R-DLPFC to L-caudate EC may be compensated by the larger modulatory changes in the other prefrontal-striatal ECs in the CUD individuals.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Radiology, Virginia Commonwealth University (VCU), Richmond, VA, USA.
| | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Lori Keyser-Marcus
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Jasmin Vassileva
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Min Zhu
- Radiology Department, Mu Dang Jiang Medical University, Mu Dang Jiang, Hei Long Jiang, China
| | - Venkatesh Ganapathy
- Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Qin Wang
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Edward L Boone
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Sergi Ferré
- Integrative Neurobiology Section, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), 203 East Cary Street, Suite 202, Richmond, VA 23219, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA; Department of Pharmacology & Toxicology, VCU, Richmond, VA, USA; Department of Neurology, VCU, Richmond, VA, USA
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44
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Miranda-Dominguez O, Feczko E, Grayson DS, Walum H, Nigg JT, Fair DA. Heritability of the human connectome: A connectotyping study. Netw Neurosci 2018; 2:175-199. [PMID: 30215032 PMCID: PMC6130446 DOI: 10.1162/netn_a_00029] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 10/02/2017] [Indexed: 11/04/2022] Open
Abstract
Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.
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Affiliation(s)
- Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - David S Grayson
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Hasse Walum
- Silvio O. Conte Center for Oxytocin and Social Cognition, Center for Translational Social Neuroscience, Yerkes National Primate Research Center, Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Joel T Nigg
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
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45
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de Lacy N, Kodish I, Rachakonda S, Calhoun VD. Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD. Hum Brain Mapp 2018; 39:3449-3467. [PMID: 29682852 DOI: 10.1002/hbm.24187] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 03/31/2018] [Accepted: 04/09/2018] [Indexed: 12/21/2022] Open
Abstract
From childhood to adolescence, strengthened coupling in frontal, striatal and parieto-temporal regions associated with cognitive control, and increased anticorrelation between task-positive and task-negative circuits, subserve the reshaping of behavior. ADHD is a common condition peaking in adolescence and regressing in adulthood, with a wide variety of cognitive control deficits. Alternate hypotheses of ADHD emphasize lagging circuitry refinement versus categorical differences in network function. However, quantifying the individual circuit contributions to behavioral findings, and relative roles of maturational versus categorical effects, is challenging in vivo or in meta-analyses using task-based paradigms within the same pipeline, given the multiplicity of neurobehavioral functions implicated. To address this, we analyzed 46 positively-correlated and anticorrelated circuits in a multivariate model in resting-state data from 504 age- and gender-matched youth, and created a novel in silico method to map individual quantified effects to reverse inference maps of 8 neurocognitive functions consistently implicated in ADHD, as well as dopamine and hyperactivity. We identified only age- and gender-related effects in intrinsic connectivity, and found that maturational refinement of circuits in youth with ADHD occupied 3-10x more brain locations than in typical development, with the footprint, effect size and contribution of individual circuits varying substantially. Our analysis supports the maturational hypothesis of ADHD, suggesting lagging connectivity reorganization within specific subnetworks of fronto-parietal control, ventral attention, cingulo-opercular, temporo-limbic and cerebellar sub-networks contribute across neurocognitive findings present in this complex condition. We present the first analysis of anti-correlated connectivity in ADHD and suggest new directions for exploring residual and non-responsive symptoms.
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Affiliation(s)
- Nina de Lacy
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Ian Kodish
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | | | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, 87106.,University of New Mexico, Albuquerque, NM, 87131
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46
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Rosenberg MD, Casey BJ, Holmes AJ. Prediction complements explanation in understanding the developing brain. Nat Commun 2018; 9:589. [PMID: 29467408 PMCID: PMC5821815 DOI: 10.1038/s41467-018-02887-9] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 01/05/2018] [Indexed: 11/08/2022] Open
Abstract
A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.
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Affiliation(s)
| | - B J Casey
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
- Department of Psychiatry, Yale University, New Haven, CT, 06511, USA
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47
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Cwik JC, Sartory G, Nuyken M, Schürholt B, Seitz RJ. Posterior and prefrontal contributions to the development posttraumatic stress disorder symptom severity: an fMRI study of symptom provocation in acute stress disorder. Eur Arch Psychiatry Clin Neurosci 2017; 267:495-505. [PMID: 27455992 DOI: 10.1007/s00406-016-0713-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 07/12/2016] [Indexed: 02/07/2023]
Abstract
Acute stress disorder (ASD) is predictive of the development of posttraumatic stress disorder (PTSD). In response to symptom provocation, the exposure to trauma-related pictures, ASD patients showed increased activation of the medial posterior areas of precuneus and posterior cingulate cortex as well as of superior prefrontal cortex in a previous study. The current study aimed at investigating which activated areas are predictive of the development of PTSD. Nineteen ASD patients took part in an fMRI study in which they were shown personalized trauma-related and neutral pictures within 4 weeks of the traumatic event. They were assessed for severity of PTSD 4 weeks later. Activation contrasts between trauma-related and neutral pictures were correlated with subsequent PTSD symptom severity. Greater activation in, among others, right medial precuneus, left retrosplenial cortex, precentral and right superior temporal gyrus as well as less activation in lateral, superior prefrontal and left fusiform gyrus was related to subsequently increased PTSD severity. The results are broadly in line with neural areas related to etiological models of PTSD, namely multisensory associative learning recruiting posterior regions on the one hand and failure to reappraise maladaptive cognitions, thought to involve prefrontal areas, on the other.
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Affiliation(s)
- Jan C Cwik
- Department of Clinical Psychology and Psychotherapy, School of Human and Social Sciences, Bergische Universität Wuppertal, Max-Horkheimer-Str. 20, Wuppertal, 42097, Germany. .,Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Massenbergstr. 9-13, 44787, Bochum, Germany.
| | - Gudrun Sartory
- Department of Clinical Psychology and Psychotherapy, School of Human and Social Sciences, Bergische Universität Wuppertal, Max-Horkheimer-Str. 20, Wuppertal, 42097, Germany
| | - Malte Nuyken
- Department of Clinical Psychology and Psychotherapy, School of Human and Social Sciences, Bergische Universität Wuppertal, Max-Horkheimer-Str. 20, Wuppertal, 42097, Germany
| | - Benjamin Schürholt
- Department of Clinical Psychology and Psychotherapy, School of Human and Social Sciences, Bergische Universität Wuppertal, Max-Horkheimer-Str. 20, Wuppertal, 42097, Germany
| | - Rüdiger J Seitz
- Department of Neurology, Center for Neurology and Neuropsychiatry, Heinrich-Heine-University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
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48
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Engelhardt LE, Roe MA, Juranek J, DeMaster D, Harden KP, Tucker-Drob EM, Church JA. Children's head motion during fMRI tasks is heritable and stable over time. Dev Cogn Neurosci 2017; 25:58-68. [PMID: 28223034 PMCID: PMC5478437 DOI: 10.1016/j.dcn.2017.01.011] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 11/21/2016] [Accepted: 01/30/2017] [Indexed: 11/30/2022] Open
Abstract
Head motion during fMRI scans negatively impacts data quality, and as post-acquisition techniques for addressing motion become increasingly stringent, data retention decreases. Studies conducted with adult participants suggest that movement acts as a relatively stable, heritable phenotype that serves as a marker for other genetically influenced phenotypes. Whether these patterns extend downward to childhood has critical implications for the interpretation and generalizability of fMRI data acquired from children. We examined factors affecting scanner motion in two samples: a population-based twin sample of 73 participants (ages 7–12 years) and a case-control sample of 32 non-struggling and 78 struggling readers (ages 8–11 years), 30 of whom were scanned multiple times. Age, but not ADHD symptoms, was significantly related to scanner movement. Movement also varied as a function of task type, run length, and session length. Twin pair concordance for head motion was high for monozygotic twins and moderate for dizygotic twins. Cross-session test-retest reliability was high. Together, these findings suggest that children’s head motion is a genetically influenced trait that has the potential to systematically affect individual differences in BOLD changes within and across groups. We discuss recommendations for future work and best practices for pediatric neuroimaging.
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Affiliation(s)
- Laura E Engelhardt
- Department of Psychology, The University of Texas at Austin, United States.
| | - Mary Abbe Roe
- Department of Psychology, The University of Texas at Austin, United States
| | - Jenifer Juranek
- The Children's Learning Institute, The University of Texas Health Science Center at Houston, United States
| | - Dana DeMaster
- The Children's Learning Institute, The University of Texas Health Science Center at Houston, United States
| | - K Paige Harden
- Department of Psychology, The University of Texas at Austin, United States; Population Research Center, The University of Texas at Austin, United States
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, United States; Population Research Center, The University of Texas at Austin, United States
| | - Jessica A Church
- Department of Psychology, The University of Texas at Austin, United States; Imaging Research Center, The University of Texas at Austin, United States
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49
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Fassbender C, Mukherjee P, Schweitzer JB. Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development. Neuroimage 2017; 149:338-347. [PMID: 28130195 DOI: 10.1016/j.neuroimage.2017.01.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 12/21/2022] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies.
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Affiliation(s)
- Catherine Fassbender
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States; UC Davis Imaging Research Center, United States.
| | - Prerona Mukherjee
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States
| | - Julie B Schweitzer
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States
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