101
|
Frässle S, Stephan KE. Test-retest reliability of regression dynamic causal modeling. Netw Neurosci 2022; 6:135-160. [PMID: 35356192 PMCID: PMC8959103 DOI: 10.1162/netn_a_00215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/08/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability—a test-theoretical property of particular importance for clinical applications—together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24–0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably—particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.
Collapse
Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Max Planck Institute for Metabolism Research, Cologne, Germany
| |
Collapse
|
102
|
Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
Collapse
|
103
|
Resting-state neuroimaging in social anxiety disorder: a systematic review. Mol Psychiatry 2022; 27:164-179. [PMID: 34035474 DOI: 10.1038/s41380-021-01154-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/21/2021] [Accepted: 04/30/2021] [Indexed: 02/04/2023]
Abstract
There has been a growing interest in resting-state brain alterations in people with social anxiety disorder. However, the evidence has been mixed and contested and further understanding of the neurobiology of this disorder may aid in informing methods to increase diagnostic accuracy and treatment targets. With this systematic review, we aimed to synthesize the findings of the neuroimaging literature on resting-state functional activity and connectivity in social anxiety disorder, and to summarize associations between brain and social anxiety symptoms to further characterize the neurobiology of the disorder. We systematically searched seven databases for empirical research studies. Thirty-five studies met the inclusion criteria, with a total of 1611 participants (795 people with social anxiety disorder and 816 controls). Studies involving resting-state seed-based functional connectivity analyses were the most common. Individuals with social anxiety disorder (vs. controls) displayed both higher and lower connectivity between frontal-amygdala and frontal-parietal regions. Frontal regions were the most consistently implicated across other analysis methods, and most associated with social anxiety symptoms. Small sample sizes and variation in the types of analyses used across studies may have contributed to the inconsistencies in the findings of this review. This review provides novel insights into established neurobiological models of social anxiety disorder and provides an update on what is known about the neurobiology of this disorder in the absence of any overt tasks (i.e., resting state). The knowledge gained from this body of research enabled us to also provide recommendations for a more standardized imaging pre-processing approach to examine resting-state brain activity and connectivity that could help advance knowledge in this field. We believe this is warranted to take the next step toward clinical translation in social anxiety disorder that may lead to better treatment outcomes by informing the identification of neurobiological targets for treatment.
Collapse
|
104
|
Liebe T, Kaufmann J, Hämmerer D, Betts M, Walter M. In vivo tractography of human locus coeruleus-relation to 7T resting state fMRI, psychological measures and single subject validity. Mol Psychiatry 2022; 27:4984-4993. [PMID: 36117208 PMCID: PMC9763100 DOI: 10.1038/s41380-022-01761-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/10/2022] [Accepted: 08/18/2022] [Indexed: 01/14/2023]
Abstract
The locus coeruleus (LC) in the brainstem as the main regulator of brain noradrenaline gains increasing attention because of its involvement in neurologic and psychiatric diseases and its relevance in general to brain function. In this study, we created a structural connectome of the LC nerve fibers based on in vivo MRI tractography to gain an understanding into LC connectivity and its impact on LC-related psychological measures. We combined our structural results with ultra-high field resting-state functional MRI to learn about the relationship between in vivo LC structural and functional connections. Importantly, we reveal that LC brain fibers are strongly associated with psychological measures of anxiety and alertness indicating that LC-noradrenergic connectivity may have an important role on brain function. Lastly, since we analyzed all our data in subject-specific space, we point out the potential of structural LC connectivity to reveal individual characteristics of LC-noradrenergic function on the single-subject level.
Collapse
Affiliation(s)
- Thomas Liebe
- grid.9613.d0000 0001 1939 2794Department of Psychiatry and Psychotherapy, University of Jena, D-07743 Jena, Germany ,grid.9613.d0000 0001 1939 2794Department of Radiology, University of Jena, D-07743 Jena, Germany ,Clinical Affective Neuroimaging Laboratory (CANLAB), D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany
| | - Jörn Kaufmann
- grid.5807.a0000 0001 1018 4307Department of Neurology, University of Magdeburg, D-39120 Magdeburg, Germany
| | - Dorothea Hämmerer
- grid.5771.40000 0001 2151 8122Department of Psychology, University of Innsbruck, A-6020 Innsbruck, Austria ,grid.83440.3b0000000121901201Institute of Cognitive Neuroscience, University College London, London, UK-WC1E 6BT UK ,grid.5807.a0000 0001 1018 4307Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265CBBS Center for Behavioral Brain Sciences, D-39120 Magdeburg, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), D-39120 Magdeburg, Germany
| | - Matthew Betts
- grid.5807.a0000 0001 1018 4307Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265CBBS Center for Behavioral Brain Sciences, D-39120 Magdeburg, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), D-39120 Magdeburg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University of Jena, D-07743, Jena, Germany. .,Clinical Affective Neuroimaging Laboratory (CANLAB), D-39120, Magdeburg, Germany. .,Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany. .,Department of Psychiatry and Psychotherapy, University Tuebingen, D-72076, Tuebingen, Germany. .,Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), D-07743 Jena, Germany. .,German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, D-07743 Jena, Germany.
| |
Collapse
|
105
|
Korzeczek A, Primaßin A, Wolff von Gudenberg A, Dechent P, Paulus W, Sommer M, Neef NE. Fluency shaping increases integration of the command-to-execution and the auditory-to-motor pathways in persistent developmental stuttering. Neuroimage 2021; 245:118736. [PMID: 34798230 DOI: 10.1016/j.neuroimage.2021.118736] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/10/2021] [Accepted: 11/15/2021] [Indexed: 10/19/2022] Open
Abstract
Fluency-shaping enhances the speech fluency of persons who stutter, yet underlying conditions and neuroplasticity-related mechanisms are largely unknown. While speech production-related brain activity in stuttering is well studied, it is unclear whether therapy repairs networks of altered sensorimotor integration, imprecise neural timing and sequencing, faulty error monitoring, or insufficient speech planning. Here, we tested the impact of one-year fluency-shaping therapy on resting-state fMRI connectivity within sets of brain regions subserving these speech functions. We analyzed resting-state data of 22 patients who participated in a fluency-shaping program, 18 patients not participating in therapy, and 28 fluent control participants, measured one year apart. Improved fluency was accompanied by an increased connectivity within the sensorimotor integration network. Specifically, two connections were strengthened; the left inferior frontal gyrus showed increased connectivity with the precentral gyrus at the representation of the left laryngeal motor cortex, and the left inferior frontal gyrus showed increased connectivity with the right superior temporal gyrus. Thus, therapy-associated neural remediation was based on a strengthened integration of the command-to-execution pathway together with an increased auditory-to-motor coupling. Since we investigated task-free brain activity, we assume that our findings are not biased to network activity involved in compensation but represent long-term focal neuroplasticity effects.
Collapse
Affiliation(s)
- Alexandra Korzeczek
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany.
| | - Annika Primaßin
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany; FH Münster University of Applied Sciences, Münster School of Health (MSH), Münster, Germany.
| | | | - Peter Dechent
- Department of Cognitive Neurology, MR Research in Neurosciences, University Medical Center Göttingen, Göttingen, Germany.
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany.
| | - Martin Sommer
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany; Department of Neurology, University Medical Center Göttingen, Germany; Department of Geriatrics, University Medical Center Göttingen, Germany.
| | - Nicole E Neef
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany; Department of Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, Germany.
| |
Collapse
|
106
|
Changes in Brain Volume Resulting from Cognitive Intervention by Means of the Feuerstein Instrumental Enrichment Program in Older Adults with Mild Cognitive Impairment (MCI): A Pilot Study. Brain Sci 2021; 11:brainsci11121637. [PMID: 34942939 PMCID: PMC8699159 DOI: 10.3390/brainsci11121637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/24/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies, including physical exercise and cognitive interventions, may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on structural cognitive modifiability and the mediated learning experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning, and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 23 subjects who completed the study, there was a significant improvement in memory on the NeuroTrax cognitive assessment battery. Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an interesting and unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE program on brain volumes and cognitive function.
Collapse
|
107
|
NBS-Predict: A prediction-based extension of the network-based statistic. Neuroimage 2021; 244:118625. [PMID: 34610435 DOI: 10.1016/j.neuroimage.2021.118625] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 09/14/2021] [Accepted: 09/27/2021] [Indexed: 01/10/2023] Open
Abstract
Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects' functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.
Collapse
|
108
|
Dalvie S, Chatzinakos C, Al Zoubi O, Georgiadis F, Lancashire L, Daskalakis NP. From genetics to systems biology of stress-related mental disorders. Neurobiol Stress 2021; 15:100393. [PMID: 34584908 PMCID: PMC8456113 DOI: 10.1016/j.ynstr.2021.100393] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 01/20/2023] Open
Abstract
Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.
Collapse
Affiliation(s)
- Shareefa Dalvie
- South African Medical Research Council (SAMRC), Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SAMRC), Unit on Child & Adolescent Health, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Chris Chatzinakos
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Obada Al Zoubi
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Foivos Georgiadis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | | | - Lee Lancashire
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
- Department of Data Science, Cohen Veterans Bioscience, New York, USA
| | - Nikolaos P. Daskalakis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| |
Collapse
|
109
|
Tian Y, Zalesky A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? Neuroimage 2021; 245:118648. [PMID: 34673248 DOI: 10.1016/j.neuroimage.2021.118648] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/03/2021] [Accepted: 10/10/2021] [Indexed: 10/20/2022] Open
Abstract
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
Collapse
Affiliation(s)
- Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia; Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.
| |
Collapse
|
110
|
Lin Q, Yoo K, Shen X, Constable TR, Chun MM. Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory. J Cogn Neurosci 2021; 33:2279-2296. [PMID: 34272957 PMCID: PMC8497062 DOI: 10.1162/jocn_a_01759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.
Collapse
|
111
|
Dufford AJ, Noble S, Gao S, Scheinost D. The instability of functional connectomes across the first year of life. Dev Cogn Neurosci 2021; 51:101007. [PMID: 34419767 PMCID: PMC8379630 DOI: 10.1016/j.dcn.2021.101007] [Citation(s) in RCA: 3] [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: 04/27/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 12/17/2022] Open
Abstract
The uniqueness and stability of the adolescent and adult functional connectome has been demonstrated to be high (80-95 % identification) using connectome-based identification (ID) or "fingerprinting". However, it is unclear to what extent individuals exhibit similar distinctiveness and stability in infancy, a developmental period of rapid and unparalleled brain development. In this study, we examined connectome-based ID rates within and across the first year of life using a longitudinal infant dataset at 1.5 month and 9 months of age. We also calculated the test-retest reliability of individual connections across the first year of life using the intraclass correlation coefficient (ICC). Overall, we found substantially lower infant ID rates than have been reported in adult and adolescent populations. Within-session ID rates were moderate and significant (ID = 48.94-70.83 %). Between-session ID rates were very low and not significant, with task-to-task connectomes resulting in the highest between-session ID rate (ID = 26.6 %). Similarly, average edge-level test-retest reliability was higher within-session than between-session (mean within-session ICC = 0.17, mean between-session ICC = 0.10). These findings suggest a lack of uniqueness and stability in functional connectomes across the first year of life consistent with the unparalleled changes in brain functional organization during this critical period.
Collapse
Affiliation(s)
- Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA.
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA
| |
Collapse
|
112
|
Brief segments of neurophysiological activity enable individual differentiation. Nat Commun 2021; 12:5713. [PMID: 34588439 PMCID: PMC8481307 DOI: 10.1038/s41467-021-25895-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
Collapse
|
113
|
Nemani A, Lowe MJ. Seed-based test-retest reliability of resting state functional magnetic resonance imaging at 3T and 7T. Med Phys 2021; 48:5756-5764. [PMID: 34486120 DOI: 10.1002/mp.15210] [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] [Received: 11/08/2020] [Revised: 05/08/2021] [Accepted: 08/20/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Ultrahigh field (UHF) resting state functional magnetic resonance imaging (rsfMRI) has become increasingly available for clinical and basic research, bringing improvements in resolution and contrast over standard high field imaging. Despite these improvements, UHF connectivity studies present several challenges, including increased sensitivity to physiological confounds and a vastly increased data burden. We present a direct quantitative assessment of test-retest reliability of functional connectivity in several standard functional networks between subjects scanned at 3T and 7T. METHODS Five healthy subjects were scanned over four sessions each in a scan-rescan design at both 3T and 7T field strengths. Resting state fMRI data were segmented into four major intrinsic connectivity networks, and seed-based peak correlations within and between these networks examined. The reliability of these correlations was assessed using intra-class correlation coefficients (ICC). RESULTS Across all data, over 4000 peak correlations were extracted for assessment. The reliability over all intrinsic networks was greater at 7T than 3T (median ICC 0.40 vs. 0.33, p ≤ 0.0014), with each network individually showing improvement. Inter-network reliability was stronger than intra-network reliability, but intra-network reliability showed the greatest improvement between field strengths. CONCLUSION We demonstrate significantly increased reliability of resting state connectivity at UHF strengths over conventional field strengths using a novel hybrid seed-based analysis. This result adds to the growing body of work supporting the migration of functional imaging studies to UHFs.
Collapse
Affiliation(s)
- Ajay Nemani
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mark J Lowe
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| |
Collapse
|
114
|
Cernasov P, Walsh EC, Kinard JL, Kelley L, Phillips R, Pisoni A, Eisenlohr-Moul TA, Arnold M, Lowery SC, Ammirato M, Truong K, Nagy GA, Oliver JA, Haworth K, Smoski M, Dichter GS. Multilevel growth curve analyses of behavioral activation for anhedonia (BATA) and mindfulness-based cognitive therapy effects on anhedonia and resting-state functional connectivity: Interim results of a randomized trial ✰. J Affect Disord 2021; 292:161-171. [PMID: 34126308 PMCID: PMC8282772 DOI: 10.1016/j.jad.2021.05.054] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/03/2021] [Accepted: 05/23/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND The neural mechanisms associated with anhedonia treatment response are poorly understood. Additionally, no study has investigated changes in resting-state functional connectivity (rsFC) accompanying psychosocial treatment for anhedonia. METHODS We evaluated a novel psychotherapy, Behavioral Activation Therapy for Anhedonia (BATA, n = 38) relative to Mindfulness-Based Cognitive Therapy (MBCT, n = 35) in a medication-free, transdiagnostic, anhedonic sample in a parallel randomized controlled trial. Participants completed up to 15 sessions of therapy and up to four 7T MRI scans before, during, and after treatment (n = 185 scans). Growth curve models estimated change over time in anhedonia and in rsFC using average region-of-interest (ROI)-to-ROI connectivity within the default mode network (DMN), frontoparietal network (FPN), salience network, and reward network. Changes in rsFC from pre- to post-treatment were further evaluated using whole-network seed-to-voxel and ROI-to-ROI edgewise analyses. RESULTS Growth curve models showed significant reductions in anhedonia symptoms and in average rsFC within the DMN and FPN over time, across BATA and MBCT. There were no differences in anhedonia reductions between treatments. Within-person, changes in average rsFC were unrelated to changes in anhedonia. Between-person, higher than average FPN rsFC was related to less anhedonia across timepoints. Seed-to-voxel and edgewise rsFC analyses corroborated reductions within the DMN and between the DMN and FPN over time, across the sample. CONCLUSIONS Reductions in rsFC within the DMN, FPN, and between these networks co-occurred with anhedonia improvement across two psychosocial treatments for anhedonia. Future anhedonia clinical trials with a waitlist control group should disambiguate treatment versus time-related effects on rsFC.
Collapse
Affiliation(s)
- Paul Cernasov
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 57514, USA
| | - Jessica L Kinard
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA; Division of Speech and Hearing Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Lisalynn Kelley
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Rachel Phillips
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Angela Pisoni
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Tory A Eisenlohr-Moul
- Department of Psychiatry, University of Illinois at Chicago, Neuropsychiatry Institute, Chicago, IL 60612, USA
| | - Macey Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Sarah C Lowery
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Marcy Ammirato
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kinh Truong
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Gabriela A Nagy
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Duke University School of Nursing, 307 Trent Drive, Durham, NC 27710, USA
| | - Jason A Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Division of Cancer Control and Population Sciences, Duke Cancer Institute, Durham, NC 27705, USA
| | - Kevin Haworth
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Moria Smoski
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Gabriel S Dichter
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA; Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 57514, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA.
| |
Collapse
|
115
|
Bridgeford EW, Wang S, Wang Z, Xu T, Craddock C, Dey J, Kiar G, Gray-Roncal W, Colantuoni C, Douville C, Noble S, Priebe CE, Caffo B, Milham M, Zuo XN, Vogelstein JT. Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. PLoS Comput Biol 2021; 17:e1009279. [PMID: 34529652 PMCID: PMC8500408 DOI: 10.1371/journal.pcbi.1009279] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/08/2021] [Accepted: 07/14/2021] [Indexed: 11/25/2022] Open
Abstract
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.
Collapse
Affiliation(s)
| | - Shangsi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Zeyi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Ting Xu
- Child Mind Institute, New York, New York, United States of America
| | - Cameron Craddock
- Child Mind Institute, New York, New York, United States of America
| | - Jayanta Dey
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | | | - Carlo Colantuoni
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Stephanie Noble
- Yale University, New Haven, Connecticut, United States of America
| | - Carey E. Priebe
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Brian Caffo
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael Milham
- Child Mind Institute, New York, New York, United States of America
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Joshua T. Vogelstein
- Johns Hopkins University, Baltimore, Maryland, United States of America
- Progressive Learning, Baltimore, Maryland, United States of America
| |
Collapse
|
116
|
Elliott ML, Knodt AR, Hariri AR. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn Sci 2021; 25:776-787. [PMID: 34134933 PMCID: PMC8363569 DOI: 10.1016/j.tics.2021.05.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022]
Abstract
fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.
Collapse
Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| |
Collapse
|
117
|
Zhu H, Jin W, Zhou J, Tong S, Xu X, Sun J. Nodal Memberships to Communities of Functional Brain Networks Reveal Functional Flexibility and Individualized Connectome. Cereb Cortex 2021; 31:5090-5106. [PMID: 34387312 DOI: 10.1093/cercor/bhab144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/12/2022] Open
Abstract
Human brain network is organized as interconnected communities for supporting cognition and behavior. Despite studies on the nonoverlapping communities of brain network, overlapping community structure and its relationship to brain function remain largely unknown. With this consideration, we employed the Bayesian nonnegative matrix factorization to decompose the functional brain networks constructed from resting-state fMRI data into overlapping communities with interdigitated mapping to functional subnetworks. By examining the heterogeneous nodal membership to communities, we classified nodes into three classes: Most nodes in somatomotor and limbic subnetworks were affiliated with one dominant community and classified as unimodule nodes; most nodes in attention and frontoparietal subnetworks were affiliated with more than two communities and classified as multimodule nodes; and the remaining nodes affiliated with two communities were classified as bimodule nodes. This three-class paradigm was highly reproducible across sessions and subjects. Furthermore, the more likely a node was classified as multimodule node, the more flexible it will be engaged in multiple tasks. Finally, the FC feature vector associated with multimodule nodes could serve as connectome "fingerprinting" to gain high subject discriminability. Together, our findings offer new insights on the flexible spatial overlapping communities that related to task-based functional flexibility and individual connectome "fingerprinting."
Collapse
Affiliation(s)
- Hong Zhu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wen Jin
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Zhou
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shanbao Tong
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiaoke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
| | - Junfeng Sun
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| |
Collapse
|
118
|
Xifra-Porxas A, Kassinopoulos M, Mitsis GD. Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability. eLife 2021; 10:e62324. [PMID: 34342582 PMCID: PMC8378847 DOI: 10.7554/elife.62324] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
Collapse
Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | | |
Collapse
|
119
|
Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
Collapse
Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | | |
Collapse
|
120
|
Song H, Rosenberg MD. Predicting attention across time and contexts with functional brain connectivity. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2020.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
121
|
Noble S, Scheinost D, Constable RT. A guide to the measurement and interpretation of fMRI test-retest reliability. Curr Opin Behav Sci 2021; 40:27-32. [PMID: 33585666 PMCID: PMC7875178 DOI: 10.1016/j.cobeha.2020.12.012] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The test-retest reliability of functional neuroimaging data has recently been a topic of much discussion. Despite early conflicting reports, converging reports now suggest that test-retest reliability is poor for standard univariate measures-namely, voxel- and region-level task-based activation and edge-level functional connectivity. To better understand the implications of these recent studies requires understanding the nuances of test-retest reliability as commonly measured by the intraclass correlation coefficient (ICC). Here we provide a guide to the measurement and interpretation of test-retest reliability in functional neuroimaging and review major findings in the literature. We highlight the importance of making choices that improve reliability so long as they do not diminish validity, pointing to the potential of multivariate approaches that improve both. Finally, we discuss the implications of recent reports of low test-retest reliability in the context of ongoing work in the field.
Collapse
Affiliation(s)
- Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
- Department of Statistics and Data Science, Yale University
- Child Study Center, Yale School of Medicine
- Department of Biomedical Engineering, Yale School of Medicine
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
- Child Study Center, Yale School of Medicine
- Department of Biomedical Engineering, Yale School of Medicine
- Department of Neurosurgery, Yale School of Medicine
| |
Collapse
|
122
|
Cole M, Murray K, St‐Onge E, Risk B, Zhong J, Schifitto G, Descoteaux M, Zhang Z. Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity. Hum Brain Mapp 2021; 42:3481-3499. [PMID: 33956380 PMCID: PMC8249904 DOI: 10.1002/hbm.25447] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 03/03/2021] [Accepted: 04/06/2021] [Indexed: 01/29/2023] Open
Abstract
There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.
Collapse
Affiliation(s)
- Martin Cole
- Department of Biostatistics and Computational BiologyUniversity of RochesterRochesterNew YorkUSA
| | - Kyle Murray
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
| | - Etienne St‐Onge
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Benjamin Risk
- Department of Biostatistics and BioinformaticsEmory UniversityAtlantaGeorgiaUSA
| | - Jianhui Zhong
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
| | - Giovanni Schifitto
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
- Department of NeurologyUniversity of RochesterRochesterNew YorkUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Zhengwu Zhang
- Department of Statistics and Operations ResearchUniversity of North Carolina at Chapel HillNorth CarolinaUSA
| |
Collapse
|
123
|
Han MJ, Park CU, Kang S, Kim B, Nikolaidis A, Milham MP, Hong SJ, Kim SG, Baeg E. Mapping functional gradients of the striatal circuit using simultaneous microelectric stimulation and ultrahigh-field fMRI in non-human primates. Neuroimage 2021; 236:118077. [PMID: 33878384 DOI: 10.1016/j.neuroimage.2021.118077] [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] [Received: 12/15/2020] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
Advances in functional magnetic resonance imaging (fMRI) have significantly enhanced our understanding of the striatal system of both humans and non-human primates (NHP) over the last few decades. However, its circuit-level functional anatomy remains poorly understood, partly because in-vivo fMRI cannot directly perturb a brain system and map its casual input-output relationship. Also, routine 3T fMRI has an insufficient spatial resolution. We performed electrical microstimulation (EM) of the striatum in lightly-anesthetized NHPs while simultaneously mapping whole-brain activation, using contrast-enhanced fMRI at ultra-high-field 7T. By stimulating multiple positions along the striatum's main (dorsal-to-ventral) axis, we revealed its complex functional circuit concerning mutually connected subsystems in both cortical and subcortical areas. Indeed, within the striatum, there were distinct brain activation patterns across different stimulation sites. Specifically, dorsal stimulation revealed a medial-to-lateral elongated shape of activation in upper caudate and putamen areas, whereas ventral stimulation evoked areas confined to the medial and lower caudate. Such dorsoventral gradients also appeared in neocortical and thalamic activations, indicating consistent embedding profiles of the striatal system across the whole brain. These findings reflect different forms of within-circuit and inter-regional neuronal connectivity between the dorsal and ventromedial striatum. These patterns both shared and contrasted with previous anatomical tract-tracing and in-vivo resting-state fMRI studies. Our approach of combining microstimulation and whole-brain fMRI mapping in NHPs provides a unique opportunity to integrate our understanding of a targeted brain area's meso- and macro-scale functional systems.
Collapse
Affiliation(s)
- Min-Jun Han
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Chan-Ung Park
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sangyun Kang
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Byounghoon Kim
- Neuroscience, University of Wisconsin - Madison, Madison, WI, United States
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, New York, NY, United States
| | - Seok Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea,; Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea,.
| | - Eunha Baeg
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea,.
| |
Collapse
|
124
|
Wang Q, Xu Y, Zhao T, Xu Z, He Y, Liao X. Individual Uniqueness in the Neonatal Functional Connectome. Cereb Cortex 2021; 31:3701-3712. [PMID: 33749736 DOI: 10.1093/cercor/bhab041] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 02/10/2021] [Accepted: 02/10/2021] [Indexed: 01/01/2023] Open
Abstract
The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.
Collapse
Affiliation(s)
- Qiushi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| |
Collapse
|
125
|
Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
Collapse
Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| |
Collapse
|
126
|
Ribeiro FL, Dos Santos FRC, Sato JR, Pinaya WHL, Biazoli CE. Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach. Netw Neurosci 2021; 5:527-548. [PMID: 34189376 PMCID: PMC8233119 DOI: 10.1162/netn_a_00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/10/2021] [Indexed: 11/06/2022] Open
Abstract
Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks. The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.
Collapse
Affiliation(s)
- Fernanda L Ribeiro
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | | | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Claudinei E Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| |
Collapse
|
127
|
Rutherford HJV, Potenza MN, Mayes LC, Scheinost D. The Application of Connectome-Based Predictive Modeling to the Maternal Brain: Implications for Mother-Infant Bonding. Cereb Cortex 2021; 30:1538-1547. [PMID: 31690936 DOI: 10.1093/cercor/bhz185] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 07/15/2019] [Accepted: 07/16/2019] [Indexed: 12/16/2022] Open
Abstract
Maternal bonding early postpartum lays an important foundation for child development. Changing brain structure and function during pregnancy and postpartum may underscore maternal bonding. We employed connectome-based predictive modeling (CPM) to measure brain functional connectivity and predict self-reported maternal bonding in mothers at 2 and 8 months postpartum. At 2 months, CPM predicted maternal anxiety in the bonding relationship: Greater integration between cerebellar and motor-sensory-auditory networks and between frontoparietal and motor-sensory-auditory networks were associated with more maternal anxiety toward their infant. Furthermore, greater segregation between the cerebellar and frontoparietal, and within the motor-sensory-auditory networks, was associated with more maternal anxiety regarding their infant. We did not observe CPM prediction of maternal bonding impairments or rejection/anger toward the infant. Finally, considering 2 and 8 months of data, changes in network connectivity were associated with changes in maternal anxiety in the bonding relationship. Our results suggest that changing connectivity among maternal brain networks may provide insight into the mother-infant bond, specifically in the context of anxiety and the representation of the infant in the mother's mind. These findings provide an opportunity to mechanistically investigate approaches to enhance the connectivity of these networks to optimize the representational and behavioral quality of the caregiving relationship.
Collapse
Affiliation(s)
| | - Marc N Potenza
- Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA.,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA.,The Connecticut Mental Health Center, New Haven, CT 06519, USA.,The Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
| | - Linda C Mayes
- Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA.,Radiology and Bioimaging Sciences, Yale School of Medicine, New Haven, CT, 06510, USA.,Statistics and Data Science, Yale University, New Haven, CT 06510, USA
| |
Collapse
|
128
|
Takao H, Amemiya S, Abe O. Longitudinal stability of resting-state networks in normal aging, mild cognitive impairment, and Alzheimer's disease. Magn Reson Imaging 2021; 82:55-73. [PMID: 34153437 DOI: 10.1016/j.mri.2021.06.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022]
Abstract
Test-retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test-retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test-retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from -0.06 to 0.44 in patients with AD. The overall test-retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test-retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test-retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test-retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.
Collapse
Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| |
Collapse
|
129
|
Moutoussis M, Garzón B, Neufeld S, Bach DR, Rigoli F, Goodyer I, Bullmore E, Guitart-Masip M, Dolan RJ. Decision-making ability, psychopathology, and brain connectivity. Neuron 2021; 109:2025-2040.e7. [PMID: 34019810 PMCID: PMC8221811 DOI: 10.1016/j.neuron.2021.04.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/16/2021] [Accepted: 04/19/2021] [Indexed: 12/11/2022]
Abstract
Decision-making is a cognitive process of central importance for the quality of our lives. Here, we ask whether a common factor underpins our diverse decision-making abilities. We obtained 32 decision-making measures from 830 young people and identified a common factor that we call "decision acuity," which was distinct from IQ and reflected a generic decision-making ability. Decision acuity was decreased in those with aberrant thinking and low general social functioning. Crucially, decision acuity and IQ had dissociable brain signatures, in terms of their associated neural networks of resting-state functional connectivity. Decision acuity was reliably measured, and its relationship with functional connectivity was also stable when measured in the same individuals 18 months later. Thus, our behavioral and brain data identify a new cognitive construct that underpins decision-making ability across multiple domains. This construct may be important for understanding mental health, particularly regarding poor social function and aberrant thought patterns.
Collapse
Affiliation(s)
- Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.
| | - Benjamín Garzón
- Aging Research Centre, Karolinska Institute, Stockholm, Sweden
| | - Sharon Neufeld
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich, 8032 Zurich, Switzerland
| | | | - Ian Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Edward Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Marc Guitart-Masip
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Aging Research Centre, Karolinska Institute, Stockholm, Sweden
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| |
Collapse
|
130
|
Yang W, Zhuang K, Liu P, Guo Y, Chen Q, Wei D, Qiu J. Memory Suppression Ability can be Robustly Predicted by the Internetwork Communication of Frontoparietal Control Network. Cereb Cortex 2021; 31:3451-3461. [PMID: 33662104 DOI: 10.1093/cercor/bhab024] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
Memory suppression (MS) is essential for mental well-being. However, no studies have explored how intrinsic resting-state functional connectivity (rs-FC) predicts this ability. Here, we adopted the connectome-based predictive modeling (CPM) based on the resting-state fMRI data to investigate whether and how rs-FC profiles in predefined brain networks (the frontoparietal control networks or FPCN) can predict MS in healthy individuals with 497 participants. The MS ability was assessed by MS-induced forgetting during the think/no-think paradigm. The results showed that FPCN network was especially informative for generating the prediction model for MS. Some regions of FPCN, such as middle frontal gyrus, superior frontal gyrus and inferior parietal lobe were critical in predicting MS. Moreover, functional interplay between FPCN and multiple networks, such as dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), the limbic system and subcortical regions, enabled prediction of MS. Crucially, the predictive FPCN networks were stable and specific to MS. These results indicated that FPCN flexibility interacts with other networks to underpin the ability of MS. These would also be beneficial for understanding how compromises in these functional networks may have led to the intrusive thoughts and memories characterized in some mental disorders.
Collapse
Affiliation(s)
- Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Peiduo Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Yuhua Guo
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| |
Collapse
|
131
|
Finn ES, Rosenberg MD. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage 2021; 239:118254. [PMID: 34118397 DOI: 10.1016/j.neuroimage.2021.118254] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/25/2021] [Accepted: 06/07/2021] [Indexed: 12/20/2022] Open
Abstract
Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.
Collapse
Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, United States.
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, United States; Neuroscience Institute, University of Chicago, United States.
| |
Collapse
|
132
|
Harnett NG, van Rooij SJH, Ely TD, Lebois LAM, Murty VP, Jovanovic T, Hill SB, Dumornay NM, Merker JB, Bruce SE, House SL, Beaudoin FL, An X, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Lewandowski C, Hendry PL, Sheikh S, Storrow AB, Musey PI, Haran JP, Jones CW, Punches BE, Swor RA, McGrath ME, Pascual JL, Seamon MJ, Mohiuddin K, Chang AM, Pearson C, Peak DA, Domeier RM, Rathlev NK, Sanchez LD, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Sheridan JF, Harte SE, Elliott JM, Kessler RC, Koenen KC, Mclean S, Ressler KJ, Stevens JS. Prognostic neuroimaging biomarkers of trauma-related psychopathology: resting-state fMRI shortly after trauma predicts future PTSD and depression symptoms in the AURORA study. Neuropsychopharmacology 2021; 46:1263-1271. [PMID: 33479509 PMCID: PMC8134491 DOI: 10.1038/s41386-020-00946-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/12/2020] [Accepted: 12/16/2020] [Indexed: 01/30/2023]
Abstract
Neurobiological markers of future susceptibility to posttraumatic stress disorder (PTSD) may facilitate identification of vulnerable individuals in the early aftermath of trauma. Variability in resting-state networks (RSNs), patterns of intrinsic functional connectivity across the brain, has previously been linked to PTSD, and may thus be informative of PTSD susceptibility. The present data are part of an initial analysis from the AURORA study, a longitudinal, multisite study of adverse neuropsychiatric sequalae. Magnetic resonance imaging (MRI) data from 109 recently (i.e., ~2 weeks) traumatized individuals were collected and PTSD and depression symptoms were assessed at 3 months post trauma. We assessed commonly reported RSNs including the default mode network (DMN), central executive network (CEN), and salience network (SN). We also identified a proposed arousal network (AN) composed of a priori brain regions important for PTSD: the amygdala, hippocampus, mamillary bodies, midbrain, and pons. Primary analyses assessed whether variability in functional connectivity at the 2-week imaging timepoint predicted 3-month PTSD symptom severity. Left dorsolateral prefrontal cortex (DLPFC) to AN connectivity at 2 weeks post trauma was negatively related to 3-month PTSD symptoms. Further, right inferior temporal gyrus (ITG) to DMN connectivity was positively related to 3-month PTSD symptoms. Both DLPFC-AN and ITG-DMN connectivity also predicted depression symptoms at 3 months. Our results suggest that, following trauma exposure, acutely assessed variability in RSN connectivity was associated with PTSD symptom severity approximately two and a half months later. However, these patterns may reflect general susceptibility to posttraumatic dysfunction as the imaging patterns were not linked to specific disorder symptoms, at least in the subacute/early chronic phase. The present data suggest that assessment of RSNs in the early aftermath of trauma may be informative of susceptibility to posttraumatic dysfunction, with future work needed to understand neural markers of long-term (e.g., 12 months post trauma) dysfunction. Furthermore, these findings are consistent with neural models suggesting that decreased top-down cortico-limbic regulation and increased network-mediated fear generalization may contribute to ongoing dysfunction in the aftermath of trauma.
Collapse
Affiliation(s)
- Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lauren A M Lebois
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Vishnu P Murty
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Sarah B Hill
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | | | - Julia B Merker
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Steve E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, Springfield, MO, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L Beaudoin
- Department of Emergency Medicine & Health Services, Policy, and Practice, Rhode Island Hospital and The Miriam Hospital, The Alpert Medical School of Brown University, Providence, RI, USA
| | - Xinming An
- Institute of Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California at San Francisco, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sarah D Linnstaedt
- Institute of Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott L Rauch
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | | | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, College of Medicine & College of Nursing, University of Cincinnati, Cincinnati, OH, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA, USA
| | - Jose L Pascual
- Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Mark J Seamon
- Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Kamran Mohiuddin
- Department of Emergency Medicine, Einstein Medical Center, Philadelphia, PA, USA
| | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Massachusetts, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ann Arbor, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MO, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Diego A Pizzagalli
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - John F Sheridan
- Department of Biosciences and Neuroscience, OSU Wexner Medical Center, Columbus, OH, USA
- Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M Elliott
- The Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, Camperdown, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine at Northwestern University, Chicago, IL, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Samuel Mclean
- Institute of Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry J Ressler
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
| |
Collapse
|
133
|
Probabilistic mapping of human functional brain networks identifies regions of high group consensus. Neuroimage 2021; 237:118164. [PMID: 34000397 PMCID: PMC8296467 DOI: 10.1016/j.neuroimage.2021.118164] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/11/2021] [Indexed: 11/21/2022] Open
Abstract
Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
Collapse
|
134
|
Taxali A, Angstadt M, Rutherford S, Sripada C. Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling. Cereb Cortex 2021; 31:2822-2833. [PMID: 33447841 PMCID: PMC8599720 DOI: 10.1093/cercor/bhaa390] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 08/17/2023] Open
Abstract
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
Collapse
Affiliation(s)
- Aman Taxali
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
135
|
Ma Y, MacDonald A. "Impact of ICA Dimensionality on the Test-Retest Reliability of Resting-State Functional Connectivity. Brain Connect 2021; 11:875-886. [PMID: 33926215 DOI: 10.1089/brain.2020.0970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test-retest reliability is increasingly important to understand. Previous literature supports the test-retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale datasets. To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures: (within-component) coherence and (between-component) connectivity was estimated. Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability which benefited mildly from increased dimensionality; the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, non-overlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks. These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest.
Collapse
Affiliation(s)
- Yizhou Ma
- University of Minnesota Twin Cities, 5635, Psychology, Minneapolis, Minnesota, United States;
| | - Angus MacDonald
- University of Minnesota Twin Cities, 5635, Psychology, N219 Elliot Hall 75 E. River Rd., Minneapolis, Minnesota, United States, 55455.,N219 Elliot Hall 75 E. River Rd.Minneapolis, Minnesota, United States, 55455;
| |
Collapse
|
136
|
Lynch CJ, Elbau I, Liston C. Improving precision functional mapping routines with multi-echo fMRI. Curr Opin Behav Sci 2021; 40:113-119. [PMID: 34095359 DOI: 10.1016/j.cobeha.2021.03.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Rapidly developing approaches to acquiring and analyzing densely-sampled, single-subject fMRI data have opened new avenues for understanding the neurobiological basis of individual differences in behavior and could allow fMRI to become a more clinically useful tool. Here, we review briefly key insights from these precision functional mapping studies and a highlight significant barrier to their clinical translation. Specifically, that reliable delineation of functional brain networks in individual humans can require hours of resting-state fMRI data per-subject. We found recently that multi-echo fMRI improves the test-retest reliability of resting-state functional connectivity measurements, mitigating the need for acquiring large quantities of per -subject data. Because the benefits of multi-echo acquisitions are most pronounced in clinically important but artifact-prone brain regions, such as the subgenual cingulate and structures deep in the subcortex, this approach has the potential to increase the impact of precision functional mapping routines in both healthy and clinical populations.
Collapse
Affiliation(s)
- Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Immanuel Elbau
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| |
Collapse
|
137
|
Wu J, Eickhoff SB, Hoffstaedter F, Patil KR, Schwender H, Yeo BTT, Genon S. A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies. Cereb Cortex 2021; 31:3732-3751. [PMID: 33884421 DOI: 10.1093/cercor/bhab044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/01/2023] Open
Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
Collapse
Affiliation(s)
- Jianxiao Wu
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Simon B Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Kaustubh R Patil
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 117575, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City 117597, Singapore.,N. 1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City 117597, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City 117575, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sarah Genon
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| |
Collapse
|
138
|
Alahmadi AAS. Investigating the sub-regions of the superior parietal cortex using functional magnetic resonance imaging connectivity. Insights Imaging 2021; 12:47. [PMID: 33847819 PMCID: PMC8044280 DOI: 10.1186/s13244-021-00993-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/23/2021] [Indexed: 11/17/2022] Open
Abstract
Objectives Traditionally, the superior parietal lobule (SPL) is usually investigated as one region of interest, particularly in functional magnetic resonance imaging (fMRI) studies. However, cytoarchitectonic analysis has shown that the SPL has a complex, heterogeneous topology that comprises more than seven sub-regions. Since previous studies have shown how the SPL is significantly involved in different neurological functions—such as visuomotor, cognitive, sensory, higher order, working memory and attention—this study aims to investigate whether these cytoarchitecturally different sub-regions have different functional connectivity to different functional brain networks. Methods This study examined 198 healthy subjects using resting-state fMRI and investigated the functional connectivity of seven sub-regions of the SPL to eight regional functional networks. Results The findings showed that most of the seven sub-regions were functionally connected to these targeted networks and that there are differences between these sub-regions and their functional connectivity patterns. The most consistent functional connectivity was observed with the visual and attention networks. There were also clear functional differences between Brodmann area (BA) 5 and BA7. BA5, with its three sub-regions, had strong functional connectivity to both the sensorimotor and salience networks. Conclusion These findings have enhanced our understanding of the functional organisations of the complexity of the SPL and its varied topology and also provide clear evidence of the functional patterns and involvements of the SPL in major brain functions.
Collapse
Affiliation(s)
- Adnan A S Alahmadi
- Department of Diagnostic Radiology, College of Applied Medical Science, King Abdulaziz University , Jeddah, Saudi Arabia.
| |
Collapse
|
139
|
Finn ES, Bandettini PA. Movie-watching outperforms rest for functional connectivity-based prediction of behavior. Neuroimage 2021; 235:117963. [PMID: 33813007 PMCID: PMC8204673 DOI: 10.1016/j.neuroimage.2021.117963] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/23/2021] [Accepted: 03/08/2021] [Indexed: 01/31/2023] Open
Abstract
A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.
Collapse
Affiliation(s)
- Emily S Finn
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
| |
Collapse
|
140
|
Ezama L, Hernández-Cabrera JA, Seoane S, Pereda E, Janssen N. Functional connectivity of the hippocampus and its subfields in resting-state networks. Eur J Neurosci 2021; 53:3378-3393. [PMID: 33786931 PMCID: PMC8252772 DOI: 10.1111/ejn.15213] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/14/2021] [Accepted: 03/18/2021] [Indexed: 11/30/2022]
Abstract
Many neuroimaging studies have shown that the hippocampus participates in a resting‐state network called the default mode network. However, how the hippocampus connects to the default mode network, whether the hippocampus connects to other resting‐state networks and how the different hippocampal subfields take part in resting‐state networks remains poorly understood. Here, we examined these issues using the high spatial‐resolution 7T resting‐state fMRI dataset from the Human Connectome Project. We used data‐driven techniques that relied on spatially‐restricted Independent Component Analysis, Dual Regression and linear mixed‐effect group‐analyses based on participant‐specific brain morphology. The results revealed two main activity hotspots inside the hippocampus. The first hotspot was located in an anterior location and was correlated with the somatomotor network. This network was subserved by co‐activity in the CA1, CA3, CA4 and Dentate Gyrus fields. In addition, there was an activity hotspot that extended from middle to posterior locations along the hippocampal long‐axis and correlated with the default mode network. This network reflected activity in the Subiculum, CA4 and Dentate Gyrus fields. These results show how different sections of the hippocampus participate in two known resting‐state networks and how these two resting‐state networks depend on different configurations of hippocampal subfield co‐activity.
Collapse
Affiliation(s)
- Laura Ezama
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
| | - Juan A Hernández-Cabrera
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain.,Basque Center on Cognition Brain and Language, San Sebastián, Spain
| | - Sara Seoane
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
| | - Ernesto Pereda
- Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain.,Facultad de Ingeniería Industrial, Universidad de La Laguna, La Laguna, Spain
| | - Niels Janssen
- Facultad de Psicología, Universidad de la Laguna, La Laguna, Spain.,Instituto de Tecnologías Biomédicas, Universidad de La Laguna, La Laguna, Spain.,Instituto Universitario de Neurociencia, Universidad de la Laguna, La Laguna, Spain
| |
Collapse
|
141
|
Tooley UA, Mackey AP, Ciric R, Ruparel K, Moore TM, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Associations between Neighborhood SES and Functional Brain Network Development. Cereb Cortex 2021; 30:1-19. [PMID: 31220218 PMCID: PMC7029704 DOI: 10.1093/cercor/bhz066] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.
Collapse
Affiliation(s)
- Ursula A Tooley
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.,Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
142
|
Tian L, Ye M, Chen C, Cao X, Shen T. Consistency of functional connectivity across different movies. Neuroimage 2021; 233:117926. [PMID: 33675997 DOI: 10.1016/j.neuroimage.2021.117926] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/24/2021] [Accepted: 02/28/2021] [Indexed: 10/22/2022] Open
Abstract
Movie fMRI has emerged as a powerful tool for investigating human brain function, and functional connectivity (FC) plays a predominant role in fMRI-based studies. Accordingly, movie-watching FC may have great potential for future studies on human brain function. Before wide application of movie-watching FC, however, it is essential to evaluate how much it is influenced by differences in movies. The main aim of this study was to investigate the consistency of movie-watching FC across different movies. For this purpose, we performed three sets of analyses on the four movie fMRI runs (with different movie stimuli) included in the HCP dataset. The first set was performed to evaluate the agreement of movie-watching FC in exact values using intra-class correlation (ICC), and the ICC of movie-watching FC across different movies (0.37 on average) was found to be comparable to that of resting-state FC across repeated scans. The second set was performed to evaluate the agreement of movie-watching FC in connectivity patterns, and the results indicate that individuals could be identified with relatively high accuracies (94%-99%) across different movies based on their FC matrices. The final set was performed to test the generalizability of predictive models based on movie-watching FC, as this generalizability is highly dependent on the consistency of the FC. The results indicate that predictive models trained based on FC extracted from one movie fMRI run can make good predictions on FC extracted from runs with different movie stimuli. Taken together, our findings indicate that movie-watching FC is highly consistent across different movies, and conclusions drawn based on movie-watching FC are generalizable.
Collapse
Affiliation(s)
- Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Mengting Ye
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
| | - Chen Chen
- Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
| | - Xuyu Cao
- Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
| | - Tianhui Shen
- Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
| |
Collapse
|
143
|
Wang Z, Sair HI, Crainiceanu C, Lindquist M, Landman BA, Resnick S, Vogelstein JT, Caffo B. On statistical tests of functional connectome fingerprinting. CAN J STAT 2021. [DOI: 10.1002/cjs.11591] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Zeyi Wang
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
| | - Haris I. Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology Johns Hopkins University Baltimore MD U.S.A
| | | | - Martin Lindquist
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
| | - Bennett A. Landman
- Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN U.S.A
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience National Institute on Aging, National Institutes of Health Baltimore MD U.S.A
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering Institute of Computational Medicine, JHU Baltimore Maryland U.S.A
| | - Brian Caffo
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
| |
Collapse
|
144
|
Teeuw J, Hulshoff Pol HE, Boomsma DI, Brouwer RM. Reliability modelling of resting-state functional connectivity. Neuroimage 2021; 231:117842. [PMID: 33581291 DOI: 10.1016/j.neuroimage.2021.117842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.
Collapse
Affiliation(s)
- Jalmar Teeuw
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rachel M Brouwer
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
| |
Collapse
|
145
|
Cho JW, Korchmaros A, Vogelstein JT, Milham MP, Xu T. Impact of concatenating fMRI data on reliability for functional connectomics. Neuroimage 2021; 226:117549. [PMID: 33248255 PMCID: PMC7983579 DOI: 10.1016/j.neuroimage.2020.117549] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 11/30/2022] Open
Abstract
Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that 'more data is better' emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of cortical functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.
Collapse
Affiliation(s)
- Jae Wook Cho
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | | | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400N. Charles St Baltimore, MD 21218, United States
| | - Michael P Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
| |
Collapse
|
146
|
Yang Z, Telesford QK, Franco AR, Lim R, Gu S, Xu T, Ai L, Castellanos FX, Yan CG, Colcombe S, Milham MP. Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations. Neuroimage 2021; 225:117489. [PMID: 33130272 PMCID: PMC7829665 DOI: 10.1016/j.neuroimage.2020.117489] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 10/21/2020] [Indexed: 01/16/2023] Open
Abstract
Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.
Collapse
Affiliation(s)
- Zhen Yang
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States.
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Alexandre R Franco
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ryan Lim
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Shi Gu
- University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Ting Xu
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Francisco X Castellanos
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
| |
Collapse
|
147
|
Kraus BT, Perez D, Ladwig Z, Seitzman BA, Dworetsky A, Petersen SE, Gratton C. Network variants are similar between task and rest states. Neuroimage 2021; 229:117743. [PMID: 33454409 PMCID: PMC8080895 DOI: 10.1016/j.neuroimage.2021.117743] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 01/06/2021] [Indexed: 01/29/2023] Open
Abstract
Recent work has demonstrated that individual-specific variations in functional networks (termed “network variants”) can be identified in individuals using resting state functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time, suggesting that they may be trait-like markers of individual differences in brain organization. However, while networks variants are reliable at rest, is is still untested whether they are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in task and rest states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.
Collapse
Affiliation(s)
- Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, IL 60208, United States
| | - Diana Perez
- Department of Psychology, Northwestern University, Evanston, IL 60208, United States
| | - Zach Ladwig
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL 60611, United States
| | - Benjamin A Seitzman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ally Dworetsky
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Steven E Petersen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, United States; Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States; Department of Neuroscience, Washington University in St. Louis, St. Louis, MO 63110, United States; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63110, United States; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL 60208, United States; Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL 60611, United States; Department of Neurology, Northwestern University, Chicago, IL 60611, United States.
| |
Collapse
|
148
|
Barber AD, Hegarty CE, Lindquist M, Karlsgodt KH. Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks. Cereb Cortex 2021; 31:2834-2844. [PMID: 33429433 DOI: 10.1093/cercor/bhaa391] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 01/26/2023] Open
Abstract
Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.
Collapse
Affiliation(s)
- Anita D Barber
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York, 11004, USA.,Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, New York, 11030, USA.,Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | | | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, 21205, USA
| | - Katherine H Karlsgodt
- Department of Psychology, University of California, Los Angeles, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 90095, USA
| |
Collapse
|
149
|
Ho TC, Walker JC, Teresi GI, Kulla A, Kirshenbaum JS, Gifuni AJ, Singh MK, Gotlib IH. Default mode and salience network alterations in suicidal and non-suicidal self-injurious thoughts and behaviors in adolescents with depression. Transl Psychiatry 2021; 11:38. [PMID: 33436537 PMCID: PMC7804956 DOI: 10.1038/s41398-020-01103-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 12/27/2022] Open
Abstract
Suicidal ideation (SI) and non-suicidal self-injury (NSSI) are two distinct yet often co-occurring risk factors for suicide deaths in adolescents. Elucidating the neurobiological patterns that specifically characterize SI and NSSI in adolescents is needed to inform the use of these markers in intervention studies and to develop brain-based treatment targets. Here, we clinically assessed 70 adolescents-49 adolescents with depression and 21 healthy controls-to determine SI and NSSI history. Twenty-eight of the depressed adolescents had a history of SI and 29 had a history of NSSI (20 overlapping). All participants underwent a resting-state fMRI scan. We compared groups in network coherence of subdivisions of the central executive network (CEN), default mode network (DMN), and salience network (SN). We also examined group differences in between-network connectivity and explored brain-behavior correlations. Depressed adolescents with SI and with NSSI had lower coherence in the ventral DMN compared to those without SI or NSSI, respectively, and healthy controls (all ps < 0.043, uncorrected). Depressed adolescents with NSSI had lower coherence in the anterior DMN and in insula-SN (all ps < 0.030, uncorrected), and higher CEN-DMN connectivity compared to those without NSSI and healthy controls (all ps < 0.030, uncorrected). Lower network coherence in all DMN subnetworks and insula-SN were associated with higher past-month SI and NSSI (all ps < 0.001, uncorrected). Thus, in our sample, both SI and NSSI are related to brain networks associated with difficulties in self-referential processing and future planning, while NSSI specifically is related to brain networks associated with disruptions in interoceptive awareness.
Collapse
Affiliation(s)
- Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Johanna C Walker
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Giana I Teresi
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Artenisa Kulla
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Anthony J Gifuni
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Manpreet K Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| |
Collapse
|
150
|
Functional connectivity patterns predict naturalistic viewing versus rest across development. Neuroimage 2021; 229:117630. [PMID: 33401011 DOI: 10.1016/j.neuroimage.2020.117630] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/16/2020] [Accepted: 12/07/2020] [Indexed: 11/22/2022] Open
Abstract
Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.
Collapse
|