151
|
He Y, Byrge L, Kennedy DP. Nonreplication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies. Hum Brain Mapp 2020; 41:1334-1350. [PMID: 31916675 PMCID: PMC7268009 DOI: 10.1002/hbm.24879] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/25/2019] [Accepted: 11/19/2019] [Indexed: 12/24/2022] Open
Abstract
A rapidly growing number of studies on autism spectrum disorder (ASD) have used resting‐state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there remains a pressing need to determine the primary factors influencing replicability. Here, we used resting‐state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group‐averaged functional connectomes and group‐level differences (ASD vs. control) across 33 denoising pipelines and four independently‐acquired datasets. The group‐averaged connectomes were highly consistent across pipelines (r = 0.92 ± 0.06) and sites (r = 0.88 ± 0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76 ± 0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07 ± 0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across‐site similarity remained low even when considering the data at a large‐scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
Collapse
Affiliation(s)
- Ye He
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Cognitive Science Program, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
| |
Collapse
|
152
|
Nunes A, Trappenberg T, Alda M. We need an operational framework for heterogeneity in psychiatric research. J Psychiatry Neurosci 2020; 45:3-6. [PMID: 31845771 PMCID: PMC6919921 DOI: 10.1503/jpn.190198] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Abraham Nunes
- From the Department of Psychiatry (Nunes, Alda) and the Faculty of Computer Science (Trappenberg, Nunes), Dalhousie University, Halifax, NS, Canada
| | - Thomas Trappenberg
- From the Department of Psychiatry (Nunes, Alda) and the Faculty of Computer Science (Trappenberg, Nunes), Dalhousie University, Halifax, NS, Canada
| | - Martin Alda
- From the Department of Psychiatry (Nunes, Alda) and the Faculty of Computer Science (Trappenberg, Nunes), Dalhousie University, Halifax, NS, Canada
| |
Collapse
|
153
|
Maglanoc LA, Kaufmann T, Jonassen R, Hilland E, Beck D, Landrø NI, Westlye LT. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp 2020; 41:241-255. [PMID: 31571370 PMCID: PMC7267936 DOI: 10.1002/hbm.24802] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/08/2019] [Accepted: 09/09/2019] [Indexed: 01/03/2023] Open
Abstract
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.
Collapse
Affiliation(s)
- Luigi A. Maglanoc
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Rune Jonassen
- Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Eva Hilland
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- Division of PsychiatryDiakonhjemmet HospitalOsloNorway
| | - Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Nils Inge Landrø
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| |
Collapse
|
154
|
Tunç B, Yankowitz LD, Parker D, Alappatt JA, Pandey J, Schultz RT, Verma R. Deviation from normative brain development is associated with symptom severity in autism spectrum disorder. Mol Autism 2019; 10:46. [PMID: 31867092 PMCID: PMC6907209 DOI: 10.1186/s13229-019-0301-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 11/29/2019] [Indexed: 12/13/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6–25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.
Collapse
Affiliation(s)
- Birkan Tunç
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,2Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA.,4Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Lisa D Yankowitz
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,5Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Drew Parker
- 6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Jacob A Alappatt
- 6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Juhi Pandey
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Robert T Schultz
- 1Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA.,3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA.,7Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ragini Verma
- 4Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA.,6DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| |
Collapse
|
155
|
Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
Collapse
Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| |
Collapse
|