1
|
Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
Collapse
Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
2
|
Huang S, De Brigard F, Cabeza R, Davis SW. Connectivity analyses for task-based fMRI. Phys Life Rev 2024; 49:139-156. [PMID: 38728902 PMCID: PMC11116041 DOI: 10.1016/j.plrev.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.
Collapse
Affiliation(s)
- Shenyang Huang
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States.
| | - Felipe De Brigard
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States
| | - Roberto Cabeza
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
| | - Simon W Davis
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States; Department of Neurology, Duke University School of Medicine, Durham, NC 27708, United States
| |
Collapse
|
3
|
Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
Collapse
Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
| |
Collapse
|
4
|
Tozzi L, Zhang X, Pines A, Olmsted AM, Zhai ES, Anene ET, Chesnut M, Holt-Gosselin B, Chang S, Stetz PC, Ramirez CA, Hack LM, Korgaonkar MS, Wintermark M, Gotlib IH, Ma J, Williams LM. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med 2024:10.1038/s41591-024-03057-9. [PMID: 38886626 DOI: 10.1038/s41591-024-03057-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/09/2024] [Indexed: 06/20/2024]
Abstract
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.
Collapse
Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Alisa M Olmsted
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Emily S Zhai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Esther T Anene
- Department of Counseling and Clinical Psychology, Teacher's College, Columbia University, New York, NY, USA
| | - Megan Chesnut
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Bailey Holt-Gosselin
- Interdepartmental Neuroscience Graduate Program, Yale University School of Medicine, New Haven, CT, USA
| | - Sarah Chang
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Patrick C Stetz
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Carolina A Ramirez
- Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia
- Department of Radiology, Westmead Hospital, Western Sydney Local Health District, Westmead, New South Wales, Australia
| | - Max Wintermark
- Department of Neuroradiology, the University of Texas MD Anderson Center, Houston, TX, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jun Ma
- Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
| |
Collapse
|
5
|
Tetereva A, Pat N. Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals. eLife 2024; 12:RP87297. [PMID: 38869938 PMCID: PMC11175613 DOI: 10.7554/elife.87297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.
Collapse
Affiliation(s)
- Alina Tetereva
- Department of Psychology, University of OtagoDunedinNew Zealand
| | - Narun Pat
- Department of Psychology, University of OtagoDunedinNew Zealand
| |
Collapse
|
6
|
Dan R, Whitton AE, Treadway MT, Rutherford AV, Kumar P, Ironside ML, Kaiser RH, Ren B, Pizzagalli DA. Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome. Neuropsychopharmacology 2024; 49:1162-1170. [PMID: 38480910 PMCID: PMC11109096 DOI: 10.1038/s41386-024-01842-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/27/2024] [Accepted: 03/01/2024] [Indexed: 03/26/2024]
Abstract
Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.
Collapse
Affiliation(s)
- Rotem Dan
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Alexis E Whitton
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Black Dog Institute, University of New South Wales, Sydney, Australia
| | - Michael T Treadway
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Ashleigh V Rutherford
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Manon L Ironside
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
7
|
van der Wijk G, Zamyadi M, Bray S, Hassel S, Arnott SR, Frey BN, Kennedy SH, Davis AD, Hall GB, Lam RW, Milev R, Müller DJ, Parikh S, Soares C, Macqueen GM, Strother SC, Protzner AB. Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy. eNeuro 2024; 11:ENEURO.0286-23.2024. [PMID: 38830756 PMCID: PMC11163402 DOI: 10.1523/eneuro.0286-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/05/2024] Open
Abstract
Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.
Collapse
Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Mojdeh Zamyadi
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
| | - Signe Bray
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
| | - Stephen R Arnott
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario L8S 4L8, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario L8N 4A6, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario M5G 2C4, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Krembil Research Institute, Toronto Western Hospital, Toronto, Ontario M5T 2S8, Canada
| | - Andrew D Davis
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
- Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 4A6, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 2A1, Canada
| | - Roumen Milev
- Department of Psychiatry and Psychology, and Providence Care Hospital, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Sagar Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan 48109
| | - Claudio Soares
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario K7L 3N6, Canada
| | - Glenda M Macqueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
| | - Stephen C Strother
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario M6A 2E1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Andrea B Protzner
- Department of Psychology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta T2N 14, Canada
| |
Collapse
|
8
|
Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 DOI: 10.1038/s41593-024-01618-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
Collapse
Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
| |
Collapse
|
9
|
Fanelli G, Robinson J, Fabbri C, Bralten J, Roth Mota N, Arenella M, Sprooten E, Franke B, Kas M, Andlauer TFM, Serretti A. Shared genetics linking sociability with the brain's default mode network. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307883. [PMID: 38826220 PMCID: PMC11142265 DOI: 10.1101/2024.05.24.24307883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.
Collapse
Affiliation(s)
- Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jamie Robinson
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Janita Bralten
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martina Arenella
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Emma Sprooten
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martien Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Till FM Andlauer
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Department of Medicine and Surgery, Kore University of Enna, Enna, Italy
| |
Collapse
|
10
|
Mochalski LN, Friedrich P, Li X, Kröll JP, Eickhoff SB, Weis S. Inter- and intra-subject similarity in network functional connectivity across a full narrative movie. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594107. [PMID: 38798405 PMCID: PMC11118367 DOI: 10.1101/2024.05.14.594107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.
Collapse
|
11
|
Tetereva A, Knodt AR, Melzer TR, van der Vliet W, Gibson B, Hariri AR, Whitman ET, Li J, Deng J, Ireland D, Ramrakha S, Pat N. Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.589404. [PMID: 38746222 PMCID: PMC11092590 DOI: 10.1101/2024.05.03.589404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.
Collapse
|
12
|
Kim N, Kim MJ, Strauman TJ, Hariri AR. Intrinsic functional connectivity of motor and heteromodal association cortex predicts individual differences in regulatory focus. PNAS NEXUS 2024; 3:pgae167. [PMID: 38711811 PMCID: PMC11071117 DOI: 10.1093/pnasnexus/pgae167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024]
Abstract
Regulatory focus theory (RFT) describes two cognitive-motivational systems for goal pursuit-the promotion and prevention systems-important for self-regulation and previously implicated in vulnerability to psychopathology. According to RFT, the promotion system is engaged in attaining ideal goals (e.g. hopes and dreams), whereas the prevention system is associated with accomplishing ought goals (e.g. duties and obligations). Prior task-based functional magnetic resonance imaging (fMRI) studies have mostly explored the mapping of these two systems onto the activity of a priori brain regions supporting motivation and executive control in both healthy and depressed adults. However, complex behavioral processes such as those guided by individual differences in regulatory focus are likely supported by widely distributed patterns of intrinsic functional connectivity. We used data-driven connectome-based predictive modeling to identify patterns of distributed whole-brain intrinsic network connectivity associated with individual differences in promotion and prevention system orientation in 1,307 young university volunteers. Our analyses produced a network model predictive of prevention but not promotion orientation, specifically the subjective experience of successful goal pursuit using prevention strategies. The predictive model of prevention success was highlighted by decreased intrinsic functional connectivity of both heteromodal association cortices in the parietal and limbic networks and the primary motor cortex. We discuss these findings in the context of strategic inaction, which drives individuals with a strong dispositional prevention orientation to inhibit their behavioral tendencies in order to shield the self from potential losses, thus maintaining the safety of the status quo but also leading to trade-offs in goal pursuit success.
Collapse
Affiliation(s)
- Nayoung Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - Timothy J Strauman
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| |
Collapse
|
13
|
Lho SK, Kim T, Moon SY, Kim M, Kwon JS. Alteration in left frontoparietal connectivity correlates with impaired cognitive reappraisal in early psychosis. Schizophr Res 2024; 267:130-137. [PMID: 38531160 DOI: 10.1016/j.schres.2024.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/08/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Impaired cognitive reappraisal is a notable symptom of early psychosis, but its neurobiological basis remains underexplored. We aimed to identify the underlying neurobiological mechanism of this impairment by using resting-state functional connectivity (FC) analyses focused on brain regions related to cognitive reappraisal. METHODS Resting-state functional magnetic resonance images were collected from 36 first-episode psychosis (FEP) patients, 32 clinical high-risk (CHR) individuals, and 48 healthy controls (HCs). Whole-brain FC maps using seed regions associated with cognitive reappraisal were generated and compared across the FEP, CHR and HC groups. We assessed the correlation between resting-state FC, reappraisal success ratio, positive symptom severity and social functioning controlling for covariates. RESULTS FEP patients showed higher FC between the left superior parietal lobe and left inferior frontal gyrus than HCs. Higher FC between the left superior parietal lobe and left inferior frontal gyrus negatively correlated with the reappraisal success ratio in the FEP group after controlling for covariates. Lower FC correlated with lower positive symptom severity and improved global functioning in the FEP group. CONCLUSIONS Alteration in left frontoparietal connectivity reflects impaired cognitive reappraisal in early psychosis, and such alteration correlates with increased positive symptoms and decreased global functioning. These findings offer a potential path for interventions targeting newly emerging symptoms in the early stages of psychosis.
Collapse
Affiliation(s)
- Silvia Kyungjin Lho
- Department of Psychiatry, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea; Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Sun-Young Moon
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| |
Collapse
|
14
|
Williams JC, Tubiolo PN, Zheng ZJ, Silver-Frankel EB, Pham DT, Haubold NK, Abeykoon SK, Abi-Dargham A, Horga G, Van Snellenberg JX. Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.28.591516. [PMID: 38746171 PMCID: PMC11092475 DOI: 10.1101/2024.04.28.591516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.
Collapse
|
15
|
Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
Collapse
Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
16
|
Pelletier-Baldelli A, Sheridan MA, Rudolph MD, Eisenlohr-Moul T, Martin S, Srabani EM, Giletta M, Hastings PD, Nock MK, Slavich GM, Rudolph KD, Prinstein MJ, Miller AB. Brain network connectivity during peer evaluation in adolescent females: Associations with age, pubertal hormones, timing, and status. Dev Cogn Neurosci 2024; 66:101357. [PMID: 38359577 PMCID: PMC10878848 DOI: 10.1016/j.dcn.2024.101357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/17/2024] Open
Abstract
Despite copious data linking brain function with changes to social behavior and mental health, little is known about how puberty relates to brain functioning. We investigated the specificity of brain network connectivity associations with pubertal indices and age to inform neurodevelopmental models of adolescence. We examined how brain network connectivity during a peer evaluation fMRI task related to pubertal hormones (dehydroepiandrosterone and testosterone), pubertal timing and status, and age. Participants were 99 adolescents assigned female at birth aged 9-15 (M = 12.38, SD = 1.81) enriched for the presence of internalizing symptoms. Multivariate analysis revealed that within Salience, between Frontoparietal - Reward and Cinguloopercular - Reward network connectivity were associated with all measures of pubertal development and age. Specifically, Salience connectivity linked with age, pubertal hormones, and status, but not timing. In contrast, Frontoparietal - Reward connectivity was only associated with hormones. Finally, Cinguloopercular - Reward connectivity related to age and pubertal status, but not hormones or timing. These results provide evidence that the salience processing underlying peer evaluation is jointly influenced by various indices of puberty and age, while coordination between cognitive control and reward circuitry is related to pubertal hormones, pubertal status, and age in unique ways.
Collapse
Affiliation(s)
- Andrea Pelletier-Baldelli
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Margaret A Sheridan
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marc D Rudolph
- Sticht Center on Aging, Wake Forest School of Medicine, Wake Forest, NC, USA
| | - Tory Eisenlohr-Moul
- Department of Psychiatry, University of Illinois Chicago College of Medicine, Chicago, IL, USA
| | - Sophia Martin
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ellora M Srabani
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Matteo Giletta
- Department of Developmental, Personality and Social Psychology, Ghent University, Ghent, Belgium
| | - Paul D Hastings
- Department of Psychology, University of California Davis, Davis, CA, USA
| | - Matthew K Nock
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Karen D Rudolph
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Mitchell J Prinstein
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adam Bryant Miller
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; RTI International, Research Triangle Park, NC, USA
| |
Collapse
|
17
|
Zhuo L, Jin Z, Xie K, Li S, Lin F, Zhang J, Li L. Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions. Neuroimage 2024; 289:120552. [PMID: 38387742 DOI: 10.1016/j.neuroimage.2024.120552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.
Collapse
Affiliation(s)
- Lei Zhuo
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
| | - Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Simeng Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Feng Lin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Ling Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
| |
Collapse
|
18
|
Kurkela K, Ritchey M. Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.31.555768. [PMID: 38464053 PMCID: PMC10925185 DOI: 10.1101/2023.08.31.555768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network ("DMN-C") that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant's whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.
Collapse
Affiliation(s)
- Kyle Kurkela
- Department of Psychology and Neuroscience, Boston College
| | | |
Collapse
|
19
|
Greenwood PB, DeSerisy M, Koe E, Rodriguez E, Salas L, Perera FP, Herbstman J, Pagliaccio D, Margolis AE. Combined and sequential exposure to prenatal second hand smoke and postnatal maternal distress is associated with cingulo-opercular global efficiency and attention problems in school-age children. Neurotoxicol Teratol 2024; 102:107338. [PMID: 38431065 DOI: 10.1016/j.ntt.2024.107338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Prenatal exposure to secondhand (environmental) tobacco smoke (SHS) is associated with adverse neurodevelopmental outcomes, including altered functional activation of cognitive control brain circuitry and increased attention problems in children. Exposure to SHS is more common among Black youth who are also disproportionately exposed to socioeconomic disadvantage and concomitant maternal distress. We examine the combined effects of exposure to prenatal SHS and postnatal maternal distress on the global efficiency (GE) of the brain's cingulo-opercular (CO) and fronto-parietal control (FP) networks in childhood, as well as associated attention problems. METHODS Thirty-two children of non-smoking mothers followed in a prospective longitudinal birth cohort at the Columbia Center for Children's Environmental Health (CCCEH) completed magnetic resonance imaging (MRI) at ages 7-9 years old. GE scores were extracted from general connectivity data collected while children completed the Simon Spatial Incompatibility functional magnetic resonance imaging (fMRI) task. Prenatal SHS was measured using maternal urinary cotinine from the third trimester; postnatal maternal distress was assessed at child age 5 using the Psychiatric Epidemiology Research Interview (PERI-D). The Child Behavior Checklist (CBCL) measured Attention and Attention Deficit Hyperactivity Disorder (ADHD) problems at ages 7-9. Linear regressions examined the interaction between prenatal SHS and postnatal maternal distress on the GE of the CO or FP networks, as well as associations between exposure-related network alterations and attention problems. All models controlled for age, sex, maternal education at prenatal visit, race/ethnicity, global brain correlation, and mean head motion. RESULTS The prenatal SHS by postnatal maternal distress interaction term associated with the GE of the CO network (β = 0.673, Bu = 0.042, t(22) = 2.427, p = .024, D = 1.42, 95% CI [0.006, 0.079], but not the FP network (β = 0.138, Bu = 0.006, t(22) = 0.434, p = .668, 95% CI [-0.022, 0.033]). Higher GE of the CO network was associated with more attention problems (β = 0.472, Bu = 43.076, t(23) = 2.780, p = .011, D = 1.74, n = 31, 95% CI [11.024, 75.128], n = 31) and ADHD risk (β = 0.436, Bu = 21.961, t(29) = 2.567, p = .018, D = 1.81, 95% CI [4.219, 39.703], n = 30). CONCLUSIONS These preliminary findings suggest that sequential prenatal SHS exposure and postnatal maternal distress could alter the efficiency of the CO network and increase risk for downstream attention problems and ADHD. These findings are consistent with prior studies showing that prenatal SHS exposure is associated with altered function of brain regions that support cognitive control and with ADHD problems. Our model also identifies postnatal maternal distress as a significant moderator of this association. These data highlight the combined neurotoxic effects of exposure to prenatal SHS and postnatal maternal distress. Critically, such exposures are disproportionately distributed among youth from minoritized groups, pointing to potential pathways to known mental health disparities.
Collapse
Affiliation(s)
- Paige B Greenwood
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA; Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Mariah DeSerisy
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA; Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Emily Koe
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Elizabeth Rodriguez
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Leilani Salas
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Frederica P Perera
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Julie Herbstman
- Department of Environmental Health Sciences and Columbia Center for Children's Environmental Health, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - David Pagliaccio
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA
| | - Amy E Margolis
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA; Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA.
| |
Collapse
|
20
|
Harp NR, Nielsen AN, Schultz DH, Neta M. In the face of ambiguity: intrinsic brain organization in development predicts one's bias toward positivity or negativity. Cereb Cortex 2024; 34:bhae102. [PMID: 38494885 PMCID: PMC10945044 DOI: 10.1093/cercor/bhae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/19/2024] Open
Abstract
Exacerbated negativity bias, including in responses to ambiguity, represents a common phenotype of internalizing disorders. Individuals differ in their propensity toward positive or negative appraisals of ambiguity. This variability constitutes one's valence bias, a stable construct linked to mental health. Evidence suggests an initial negativity in response to ambiguity that updates via regulatory processes to support a more positive bias. Previous work implicates the amygdala and prefrontal cortex, and regions of the cingulo-opercular system, in this regulatory process. Nonetheless, the neurodevelopmental origins of valence bias remain unclear. The current study tests whether intrinsic brain organization predicts valence bias among 119 children and adolescents (6 to 17 years). Using whole-brain resting-state functional connectivity, a machine-learning model predicted valence bias (r = 0.20, P = 0.03), as did a model restricted to amygdala and cingulo-opercular system features (r = 0.19, P = 0.04). Disrupting connectivity revealed additional intra-system (e.g. fronto-parietal) and inter-system (e.g. amygdala to cingulo-opercular) connectivity important for prediction. The results highlight top-down control systems and bottom-up perceptual processes that influence valence bias in development. Thus, intrinsic brain organization informs the neurodevelopmental origins of valence bias, and directs future work aimed at explicating related internalizing symptomology.
Collapse
Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, 300 George Street, New Haven, CT 06511, United States
| | - Ashley N Nielsen
- Department of Neurology, Washington University, 660 S. Euclid Ave., St. Louis, MO 63110, United States
| | - Douglas H Schultz
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
| |
Collapse
|
21
|
Rai S, Graff K, Tansey R, Bray S. How do tasks impact the reliability of fMRI functional connectivity? Hum Brain Mapp 2024; 45:e26535. [PMID: 38348730 PMCID: PMC10884875 DOI: 10.1002/hbm.26535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 02/24/2024] Open
Abstract
While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.
Collapse
Affiliation(s)
- Shefali Rai
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Kirk Graff
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Ryann Tansey
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Signe Bray
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
| |
Collapse
|
22
|
Butler ER, Samia N, White S, Gratton C, Nusslock R. Neuroimmune mechanisms connecting violence with internalizing symptoms: A high-dimensional multimodal mediation analysis. Hum Brain Mapp 2024; 45:e26615. [PMID: 38339956 DOI: 10.1002/hbm.26615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/27/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms (ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ ,95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.
Collapse
Affiliation(s)
- Ellyn R Butler
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - Noelle Samia
- Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA
| | - Stuart White
- Nebraska Children and Families Foundation, Lincoln, Nebraska, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Institute for Policy Research, Northwestern University, Evanston, Illinois, USA
| |
Collapse
|
23
|
Zugman A, Ringlein GV, Finn ES, Lewis KM, Berman E, Silverman WK, Lebowitz ER, Pine DS, Winkler AM. Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.29.24301959. [PMID: 38352528 PMCID: PMC10862993 DOI: 10.1101/2024.01.29.24301959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Background Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have major impact. However, existing clinical models are weakly predictive. The current study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. Methods Two datasets were studied: (A) one consisted of n=54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n=15 subjects treated for 8 weeks. Connectome Predictive Modeling (CPM) was used to predict treatment response, as assessed with the PARS; additionally we investigated models using anatomical features, instead of functional connectivity. The main analysis included network edges positively correlated with treatment outcome, and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r and mean absolute error (MAE). Outcomes The main model showed a mean absolute error of approximately 3.5 (95%CI: [3.1-3.8]) points a R2 of 0.08 [-0.14 - 0.26] and r of 0.38 [0.24 - 0.511]. When testing this model in the left-out sample (B) the results were similar, with a MAE of 3.4 [2.8 - 4.7], R2-0.65 [-2.29 - 0.16] and r of 0.4 [0.24 - 0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. Interpretation The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, the current study does not support extensive use of CPM to predict outcome in pediatric anxiety.
Collapse
Affiliation(s)
- Andre Zugman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Grace V. Ringlein
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Emily S. Finn
- Psychological and Brain Sciences, Dartmouth College, 3 Maynard St, Hanover, NH, 03755, USA
| | - Krystal M. Lewis
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Erin Berman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Wendy K. Silverman
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Eli R. Lebowitz
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Daniel S. Pine
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Anderson M. Winkler
- Division of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, 1 West University Blvd, Brownsville, TX 78520, USA
| |
Collapse
|
24
|
Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
Collapse
Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
| |
Collapse
|
25
|
Schulz MA, Bzdok D, Haufe S, Haynes JD, Ritter K. Performance reserves in brain-imaging-based phenotype prediction. Cell Rep 2024; 43:113597. [PMID: 38159275 PMCID: PMC11215805 DOI: 10.1016/j.celrep.2023.113597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 07/03/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024] Open
Abstract
This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.
Collapse
Affiliation(s)
- Marc-Andre Schulz
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Stefan Haufe
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Technische Universität Berlin, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| |
Collapse
|
26
|
Neal EG, Schimmel S, George Z, Monsour M, Alayli A, Lockard G, Piper K, Maciver S, Vale FL, Bezchlibnyk YB. No change in network connectivity measurements between separate rsfMRI acquisition times. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1342161. [PMID: 38292021 PMCID: PMC10823025 DOI: 10.3389/fnetp.2024.1342161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
The role of resting state functional MRI (rsfMRI) is increasing in the field of epilepsy surgery because it is possible to interpolate network connectivity patterns across the brain with a high degree of spatial resolution. Prior studies have shown that by rsfMRI with scalp electroencephalography (EEG), an epileptogenic network can be modeled and visualized with characteristic patterns of connectivity that are relevant to both seizure-related and neuropsychological outcomes after surgery. The aim of this study is to show that a 5-min acquisition time provides reproducible results related to the relevant connectivity metrics when compared to a separately acquired 5-min scan. Fourteen separate rsfMRI sessions from ten different patients were used for comparison, comprised of patients with temporal lobe epilepsy both pre- and post-operation. Results showed that there was no significant difference in any of the connectivity metrics when comparing both 5-min scans to each other. These data support the continued use of a 5-min scan for epileptogenic network modeling in future studies because the inter-scan variability is sufficiently low as not to alter the output metrics characterizing the network connectivity.
Collapse
Affiliation(s)
- Elliot G. Neal
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Samantha Schimmel
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Zeegan George
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Molly Monsour
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Adam Alayli
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Gavin Lockard
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Keaton Piper
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| | - Stephanie Maciver
- Department of Neurology, Advent Health Tampa, Tampa, FL, United States
| | - Fernando L. Vale
- Department of Neurosurgery, Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Yarema B. Bezchlibnyk
- Department of Neurosurgery and Brain Repair, University of South Florida Morsani College of Medicine, Tampa, FL, United States
| |
Collapse
|
27
|
Mallaroni P, Mason NL, Kloft L, Reckweg JT, van Oorsouw K, Toennes SW, Tolle HM, Amico E, Ramaekers JG. Shared functional connectome fingerprints following ritualistic ayahuasca intake. Neuroimage 2024; 285:120480. [PMID: 38061689 DOI: 10.1016/j.neuroimage.2023.120480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 01/13/2024] Open
Abstract
The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example of how individualised connectivity markers can be used to trace a subject's FC across altered states of consciousness.
Collapse
Affiliation(s)
- Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands.
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Lilian Kloft
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Johannes T Reckweg
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Kim van Oorsouw
- Department of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Stefan W Toennes
- Institute of Legal Medicine, University Hospital, Goethe University, Frankfurt/Main, Germany
| | | | | | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| |
Collapse
|
28
|
Knodt AR, Elliott ML, Whitman ET, Winn A, Addae A, Ireland D, Poulton R, Ramrakha S, Caspi A, Moffitt TE, Hariri AR. Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient. Hum Brain Mapp 2023; 44:6399-6417. [PMID: 37851700 PMCID: PMC10681655 DOI: 10.1002/hbm.26517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.
Collapse
Affiliation(s)
- Annchen R. Knodt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Maxwell L. Elliott
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Ethan T. Whitman
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Alex Winn
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Angela Addae
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Avshalom Caspi
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Terrie E. Moffitt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Ahmad R. Hariri
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| |
Collapse
|
29
|
Hoy N, Lynch SJ, Waszczuk MA, Reppermund S, Mewton L. Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population. Neurosci Biobehav Rev 2023; 155:105431. [PMID: 37898444 DOI: 10.1016/j.neubiorev.2023.105431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/26/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.
Collapse
Affiliation(s)
- Nicholas Hoy
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia.
| | - Samantha J Lynch
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Department of Psychiatry, Université de Montréal, Montreal, Canada; Research Centre, CHU Sainte-Justine, Montreal, Canada
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, United States
| | - Simone Reppermund
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia; Department of Developmental Disability Neuropsychiatry, University of New South Wales, Sydney, Australia
| | - Louise Mewton
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia
| |
Collapse
|
30
|
Purcell J, Wiley R, Won J, Callow D, Weiss L, Alfini A, Wei Y, Carson Smith J. Increased neural differentiation after a single session of aerobic exercise in older adults. Neurobiol Aging 2023; 132:67-84. [PMID: 37742442 DOI: 10.1016/j.neurobiolaging.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023]
Abstract
Aging is associated with decreased cognitive function. One theory posits that this decline is in part due to multiple neural systems becoming dedifferentiated in older adults. Exercise is known to improve cognition in older adults, even after only a single session. We hypothesized that one mechanism of improvement is a redifferentiation of neural systems. We used a within-participant, cross-over design involving 2 sessions: either 30 minutes of aerobic exercise or 30 minutes of seated rest (n = 32; ages 55-81 years). Both functional Magnetic Resonance Imaging (fMRI) and Stroop performance were acquired soon after exercise and rest. We quantified neural differentiation via general heterogeneity regression. There were 3 prominent findings following the exercise. First, participants were better at reducing Stroop interference. Second, while there was greater neural differentiation within the hippocampal formation and cerebellum, there was lower neural differentiation within frontal cortices. Third, this greater neural differentiation in the cerebellum and temporal lobe was more pronounced in the older ages. These data suggest that exercise can induce greater neural differentiation in healthy aging.
Collapse
Affiliation(s)
- Jeremy Purcell
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
| | - Robert Wiley
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA
| | - Junyeon Won
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Dallas, Dallas, TX, USA
| | - Daniel Callow
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Lauren Weiss
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Alfonso Alfini
- National Center on Sleep Disorders Research, Division of Lung Diseases, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Yi Wei
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
| | - J Carson Smith
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA.
| |
Collapse
|
31
|
Kulkarni AP, Hwang G, Cook CJ, Mohanty R, Guliani A, Nair VA, Bendlin BB, Meyerand E, Prabhakaran V. Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study. Hum Brain Mapp 2023; 44:5238-5293. [PMID: 36537283 PMCID: PMC10543121 DOI: 10.1002/hbm.25947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 09/07/2023] Open
Abstract
We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].
Collapse
Affiliation(s)
- Arman P. Kulkarni
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Gyujoon Hwang
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Cole J. Cook
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Akhil Guliani
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Elizabeth Meyerand
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| |
Collapse
|
32
|
Lakhani DA, Sabsevitz DS, Chaichana KL, Quiñones-Hinojosa A, Middlebrooks EH. Current State of Functional MRI in the Presurgical Planning of Brain Tumors. Radiol Imaging Cancer 2023; 5:e230078. [PMID: 37861422 DOI: 10.1148/rycan.230078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Surgical resection of brain tumors is challenging because of the delicate balance between maximizing tumor removal and preserving vital brain functions. Functional MRI (fMRI) offers noninvasive preoperative mapping of widely distributed brain areas and is increasingly used in presurgical functional mapping. However, its impact on survival and functional outcomes is still not well-supported by evidence. Task-based fMRI (tb-fMRI) maps blood oxygen level-dependent (BOLD) signal changes during specific tasks, while resting-state fMRI (rs-fMRI) examines spontaneous brain activity. rs-fMRI may be useful for patients who cannot perform tasks, but its reliability is affected by tumor-induced changes, challenges in data processing, and noise. Validation studies comparing fMRI with direct cortical stimulation (DCS) show variable concordance, particularly for cognitive functions such as language; however, concordance for tb-fMRI is generally greater than that for rs-fMRI. Preoperative fMRI, in combination with MRI tractography and intraoperative DCS, may result in improved survival and extent of resection and reduced functional deficits. fMRI has the potential to guide surgical planning and help identify targets for intraoperative mapping, but there is currently limited prospective evidence of its impact on patient outcomes. This review describes the current state of fMRI for preoperative assessment in patients undergoing brain tumor resection. Keywords: MR-Functional Imaging, CNS, Brain/Brain Stem, Anatomy, Oncology, Functional MRI, Functional Anatomy, Task-based, Resting State, Surgical Planning, Brain Tumor © RSNA, 2023.
Collapse
Affiliation(s)
- Dhairya A Lakhani
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - David S Sabsevitz
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Kaisorn L Chaichana
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Alfredo Quiñones-Hinojosa
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Erik H Middlebrooks
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| |
Collapse
|
33
|
Meng Q, Zhu Y, Yuan Y, Ni R, Yang L, Liu J, Bu J. Dual-site beta tACS over rIFG and M1 enhances response inhibition: A parallel multiple control and replication study. Int J Clin Health Psychol 2023; 23:100411. [PMID: 37731603 PMCID: PMC10507441 DOI: 10.1016/j.ijchp.2023.100411] [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: 07/26/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023] Open
Abstract
Response inhibition is a core component of cognitive control. Past electrophysiology and neuroimaging studies have identified beta oscillations and inhibitory control cortical regions correlated with response inhibition, including the right inferior frontal gyrus (rIFG) and primary motor cortex (M1). Hence, increasing beta activity in multiple brain regions is a potential way to enhance response inhibition. Here, a novel dual-site transcranial alternating current stimulation (tACS) method was used to modulate beta activity over the rIFG-M1 network in a sample of 115 (excluding 2 participants) with multiple control groups and a replicated experimental design. In Experiment 1, 70 healthy participants were randomly assigned to three dual-site beta-tACS groups, including in-phase, anti-phase or sham stimulation. During and after stimulation, participants were required to complete the stop-signal task, and electroencephalography (EEG) was collected before and after stimulation. The Barratt Impulsiveness Scale was completed before the experiment to evaluate participants' impulsiveness. In addition, we conducted an active control experiment with a sample size of 20 to exclude the potential effects of the dual-site tACS "return" electrode. To validate the behavioural findings of Experiment 1, 25 healthy participants took part in Experiment 2 and were randomized into two groups, including in-phase and sham stimulation groups. We found that compared to the sham group, in-phase but not anti-phase beta-tACS significantly improved both response inhibition performance and beta synchronization of the inhibitory control network in Experiment 1. Furthermore, the increased beta synchronization was correlated with enhanced response inhibition. In an independent sample of Experiment 2, the enhanced response inhibition performance observed in the in-phase group was replicated. After combining the data from the above two experiments, the time dynamics analysis revealed that the in-phase beta-tACS effect occurred in the post-stimulation period but not the stimulation period. The state-dependence analysis showed that individuals with poorer baseline response inhibition or higher attentional impulsiveness had greater improvement in response inhibition for the in-phase group. These findings strongly support that response inhibition in healthy adults can be improved by in-phase dual-site beta-tACS of the rIFG-M1 network, and provide a new potential treatment targets of synchronized cortical network activity for patients with clinically deficient response inhibition.
Collapse
Affiliation(s)
- Qiujian Meng
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Ying Zhu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Ye Yuan
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Rui Ni
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Li Yang
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Jiafang Liu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Junjie Bu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| |
Collapse
|
34
|
Vogt KM, Ibinson JW, Burlew AC, Smith CT, Aizenstein HJ, Fiez JA. Brain connectivity under light sedation with midazolam and ketamine during task performance and the periodic experience of pain: Examining concordance between different approaches for seed-based connectivity analysis. Brain Imaging Behav 2023; 17:519-529. [PMID: 37166623 PMCID: PMC10543548 DOI: 10.1007/s11682-023-00782-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2023] [Indexed: 05/12/2023]
Abstract
This work focused on functional connectivity changes under midazolam and ketamine sedation during performance of a memory task, with the periodic experience of pain. To maximize ability to compare to previous and future work, we performed secondary region of interest (ROI)-to-ROI functional connectivity analyses on these data, using two granularities of scale for ROIs. These findings are compared to the results of a previous seed-to-voxel analysis methodology, employed in the primary analysis. Healthy adult volunteers participated in this randomized crossover 3 T functional MRI study under no drug, followed by subanesthetic doses of midazolam or ketamine achieving minimal sedation. Periodic painful stimulation was delivered while subjects repeatedly performed a memory-encoding task. Atlas-based and network-level ROIs were used from within Conn Toolbox (ver 18). Timing of experimental task events was regressed from the data to assess drug-induced changes in background connectivity, using ROI-to-ROI methodology. Compared to saline, ROI-to-ROI connectivity changes under ketamine did not survive correction for multiple comparisons, thus data presented is from 16 subjects in a paired analysis between saline and midazolam. In both ROI-to-ROI analyses, the predominant direction of change was towards increased connectivity under midazolam, compared to saline. These connectivity increases occurred between functionally-distinct brain areas, with a posterior-predominant spatial distribution that included many long-range connectivity changes. During performance of an experimental task that involved periodic painful stimulation, compared to saline, low-dose midazolam was associated with robust increases in functional connectivity. This finding was concordant across different seed-based analyses for midazolam, but not ketamine. The neuroimaging drug trial from which this data was drawn was pre-registered (NCT-02515890) prior to enrollment of the first subject.
Collapse
Affiliation(s)
- Keith M Vogt
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
| | - James W Ibinson
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA
- Department of Anesthesiology, Surgical Service Line, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alex C Burlew
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - C Tyler Smith
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA
| | - Howard J Aizenstein
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Julie A Fiez
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
35
|
Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
Collapse
Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| |
Collapse
|
36
|
Pizzagalli D, Whitton A, Treadway M, Rutherford A, Kumar P, Ironside M, Kaiser R, Ren B, Dan R. Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome. RESEARCH SQUARE 2023:rs.3.rs-3168186. [PMID: 37841877 PMCID: PMC10571608 DOI: 10.21203/rs.3.rs-3168186/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.
Collapse
Affiliation(s)
| | - Alexis Whitton
- Black Dog Institute, University of New South Wales, Sydney
| | | | | | | | | | | | - Boyu Ren
- McLean Hospital / Harvard Medical School
| | - Rotem Dan
- McLean Hospital / Harvard Medical School
| |
Collapse
|
37
|
Agarwal SM, Dissanayake J, Agid O, Bowie C, Brierley N, Chintoh A, De Luca V, Diaconescu A, Gerretsen P, Graff-Guerrero A, Hawco C, Herman Y, Hill S, Hum K, Husain MO, Kennedy JL, Kiang M, Kidd S, Kozloff N, Maslej M, Mueller DJ, Naeem F, Neufeld N, Remington G, Rotenberg M, Selby P, Siddiqui I, Szacun-Shimizu K, Tiwari AK, Thirunavukkarasu S, Wang W, Yu J, Zai CC, Zipursky R, Hahn M, Foussias G. Characterization and prediction of individual functional outcome trajectories in schizophrenia spectrum disorders (PREDICTS study): Study protocol. PLoS One 2023; 18:e0288354. [PMID: 37733693 PMCID: PMC10513234 DOI: 10.1371/journal.pone.0288354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/23/2023] [Indexed: 09/23/2023] Open
Abstract
Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual's functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
Collapse
Affiliation(s)
- Sri Mahavir Agarwal
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Banting and Best Diabetes Centre (BBDC), University of Toronto, Toronto, Canada
| | - Joel Dissanayake
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ofer Agid
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Christopher Bowie
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Noah Brierley
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Araba Chintoh
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Vincenzo De Luca
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Andreea Diaconescu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Philip Gerretsen
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ariel Graff-Guerrero
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Colin Hawco
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Yarissa Herman
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Sean Hill
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Kathryn Hum
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Muhammad Omair Husain
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - James L. Kennedy
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Michael Kiang
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Sean Kidd
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Nicole Kozloff
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Marta Maslej
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Daniel J. Mueller
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Farooq Naeem
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Nicholas Neufeld
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Gary Remington
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Martin Rotenberg
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Peter Selby
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ishraq Siddiqui
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Kate Szacun-Shimizu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Arun K. Tiwari
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | | | - Wei Wang
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Joanna Yu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Clement C. Zai
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Robert Zipursky
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Margaret Hahn
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Banting and Best Diabetes Centre (BBDC), University of Toronto, Toronto, Canada
| | - George Foussias
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| |
Collapse
|
38
|
Li Y, Ma X, Sunderraman R, Ji S, Kundu S. Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence. Hum Brain Mapp 2023; 44:4772-4791. [PMID: 37466292 PMCID: PMC10400788 DOI: 10.1002/hbm.26415] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/08/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.
Collapse
Affiliation(s)
- Yang Li
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Xin Ma
- Department of BiostatisticsColumbia UniversityNew YorkNew YorkUSA
| | - Raj Sunderraman
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Shihao Ji
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Suprateek Kundu
- Department of BiostatisticsThe University of Texas at MD Anderson Cancer CenterHoustonTexasUSA
| |
Collapse
|
39
|
Passiatore R, Antonucci LA, DeRamus TP, Fazio L, Stolfa G, Sportelli L, Kikidis GC, Blasi G, Chen Q, Dukart J, Goldman AL, Mattay VS, Popolizio T, Rampino A, Sambataro F, Selvaggi P, Ulrich W, Weinberger DR, Bertolino A, Calhoun VD, Pergola G. Changes in patterns of age-related network connectivity are associated with risk for schizophrenia. Proc Natl Acad Sci U S A 2023; 120:e2221533120. [PMID: 37527347 PMCID: PMC10410767 DOI: 10.1073/pnas.2221533120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/24/2023] [Indexed: 08/03/2023] Open
Abstract
Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.
Collapse
Affiliation(s)
- Roberta Passiatore
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
- Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428Jülich, Germany
| | - Linda A. Antonucci
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
| | - Thomas P. DeRamus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
| | - Leonardo Fazio
- Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010Casamassima, Italy
| | - Giuseppe Stolfa
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
| | - Leonardo Sportelli
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Gianluca C. Kikidis
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Giuseppe Blasi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225Düsseldorf, Germany
| | - Aaron L. Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287Baltimore, MD
| | - Teresa Popolizio
- Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013San Giovanni Rotondo, Foggia, Italy
| | - Antonio Rampino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Fabio Sambataro
- Section of Psychiatry, Department of Neuroscience, University of Padova, 35121Padua, Italy
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - William Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Apulian Network on Risk for Psychosis
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121Foggia, Italy
- Department of Clinical and Experimental Medicine, University of Foggia, 71122Foggia, Italy
- Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123Andria, Italy
- Department of Mental Health, Azienda Sanitaria Locale Bari, 70132Bari, Italy
- Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100Brindisi, Italy
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287Baltimore, MD
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205Baltimore, MD
- Department of Neuroscience, Johns Hopkins University School of Medicine, 21287Baltimore, MD
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287Baltimore, MD
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205Baltimore, MD
| |
Collapse
|
40
|
Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Med Image Anal 2023; 88:102864. [PMID: 37352650 PMCID: PMC10526726 DOI: 10.1016/j.media.2023.102864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/10/2023] [Accepted: 05/31/2023] [Indexed: 06/25/2023]
Abstract
Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).
Collapse
Affiliation(s)
| | - Amin Karbasi
- Computer Science Department, Yale University, New Haven, CT, USA; Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Raimundo Rodriguez
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Brendan Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Michael Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Wei Dai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Angeliki Pollatou
- Developing Brain Institute, Children's National Hospital, Washington DC, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| |
Collapse
|
41
|
Phillips RD, Walsh EC, Zürcher NR, Lalush DS, Kinard JL, Tseng CE, Cernasov PM, Kan D, Cummings K, Kelley L, Campbell D, Dillon DG, Pizzagalli DA, Izquierdo-Garcia D, Hooker JM, Smoski MJ, Dichter GS. Striatal dopamine in anhedonia: A simultaneous [ 11C]raclopride positron emission tomography and functional magnetic resonance imaging investigation. Psychiatry Res Neuroimaging 2023; 333:111660. [PMID: 37301129 PMCID: PMC10594643 DOI: 10.1016/j.pscychresns.2023.111660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 04/21/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Anhedonia is hypothesized to be associated with blunted mesocorticolimbic dopamine (DA) functioning in samples with major depressive disorder. The purpose of this study was to examine linkages between striatal DA, reward circuitry functioning, anhedonia, and, in an exploratory fashion, self-reported stress, in a transdiagnostic anhedonic sample. METHODS Participants with (n = 25) and without (n = 12) clinically impairing anhedonia completed a reward-processing task during simultaneous positron emission tomography and magnetic resonance (PET-MR) imaging with [11C]raclopride, a DA D2/D3 receptor antagonist that selectively binds to striatal DA receptors. RESULTS Relative to controls, the anhedonia group exhibited decreased task-related DA release in the left putamen, caudate, and nucleus accumbens and right putamen and pallidum. There were no group differences in task-related brain activation (fMRI) during reward processing after correcting for multiple comparisons. General functional connectivity (GFC) findings revealed blunted fMRI connectivity between PET-derived striatal seeds and target regions in the anhedonia group. Associations were identified between anhedonia severity and the magnitude of task-related DA release to rewards in the left putamen, but not mesocorticolimbic GFC. CONCLUSIONS Results provide evidence for reduced striatal DA functioning during reward processing and blunted mesocorticolimbic network functional connectivity in a transdiagnostic sample with clinically significant anhedonia.
Collapse
Affiliation(s)
- Rachel D Phillips
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States.
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Nicole R Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - David S Lalush
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC, United States
| | - Jessica L Kinard
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, Chapel Hill, NC, United States
| | - Chieh-En Tseng
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Paul M Cernasov
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Delia Kan
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, Chapel Hill, NC, United States
| | - Kaitlin Cummings
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Lisalynn Kelley
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, United States
| | - David Campbell
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, United States
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, United States
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, United States
| | - David Izquierdo-Garcia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Jacob M Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Moria J Smoski
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, United States
| | - Gabriel S Dichter
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States; Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, Chapel Hill, NC, United States
| |
Collapse
|
42
|
Lima Santos JP, Jia-Richards M, Kontos AP, Collins MW, Versace A. Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6274. [PMID: 37444121 PMCID: PMC10341732 DOI: 10.3390/ijerph20136274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/16/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.
Collapse
Affiliation(s)
- João Paulo Lima Santos
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
| | - Meilin Jia-Richards
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
| | - Anthony P. Kontos
- Department of Orthopaedic Surgery, UPMC Sports Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (A.P.K.); (M.W.C.)
| | - Michael W. Collins
- Department of Orthopaedic Surgery, UPMC Sports Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (A.P.K.); (M.W.C.)
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
| |
Collapse
|
43
|
Kotoula V, Evans JW, Punturieri CE, Zarate CA. Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression. FRONTIERS IN NEUROIMAGING 2023; 2:1110258. [PMID: 37554642 PMCID: PMC10406217 DOI: 10.3389/fnimg.2023.1110258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/12/2023] [Indexed: 08/10/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.
Collapse
|
44
|
Whitman ET, Knodt AR, Elliott ML, Abraham WC, Cheyne K, Hogan S, Ireland D, Keenan R, Leung JH, Melzer TR, Poulton R, Purdy SC, Ramrakha S, Thorne PR, Caspi A, Moffitt TE, Hariri AR. Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities. Cereb Cortex 2023; 33:8218-8231. [PMID: 37015900 PMCID: PMC10321095 DOI: 10.1093/cercor/bhad109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 04/06/2023] Open
Abstract
Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.
Collapse
Affiliation(s)
- Ethan T Whitman
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
| | - Maxwell L Elliott
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Kirsten Cheyne
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Sean Hogan
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Ross Keenan
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Christchurch Radiology Group, Christchurch 8014, New Zealand
| | - Joan H Leung
- School of Psychology, University of Auckland, Auckland 1142, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
| | - Tracy R Melzer
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Department of Medicine, University of Otago, Christchurch 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Suzanne C Purdy
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- School of Psychology, University of Auckland, Auckland 1142, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Peter R Thorne
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
- School of Population Health, University of Auckland, Auckland 1142, New Zealand
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27710, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London SE5 8AF, UK
- PROMENTA, Department of Psychology, University of Oslo, NO-0316 Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27710, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London SE5 8AF, UK
- PROMENTA, Department of Psychology, University of Oslo, NO-0316 Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
| |
Collapse
|
45
|
Bukhari H, Su C, Dhamala E, Gu Z, Jamison K, Kuceyeski A. Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Hum Brain Mapp 2023; 44:3541-3554. [PMID: 37042411 PMCID: PMC10203814 DOI: 10.1002/hbm.26296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/10/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023] Open
Abstract
Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
Collapse
Affiliation(s)
- Hussain Bukhari
- Department of NeuroscienceWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chang Su
- Department of BiostatisticsYale UniversityNew HavenConnecticutUSA
| | - Elvisha Dhamala
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Zijin Gu
- Department of Electrical and Computer EngineeringCornell UniversityIthacaNew YorkUSA
| | - Keith Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| |
Collapse
|
46
|
Population modeling with machine learning can enhance measures of mental health - Open-data replication. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
|
47
|
Norbury A, Seeley SH, Perez-Rodriguez MM, Feder A. Functional neuroimaging of resilience to trauma: convergent evidence and challenges for future research. Psychol Med 2023; 53:3293-3305. [PMID: 37264949 DOI: 10.1017/s0033291723001162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Resilience is broadly defined as the ability to adapt successfully following stressful life events. Here, we review functional MRI studies that investigated key psychological factors that have been consistently linked to resilience to severe adversity and trauma exposure. These domains include emotion regulation (including cognitive reappraisal), reward responsivity, and cognitive control. Further, we briefly review functional imaging evidence related to emerging areas of study that may potentially facilitate resilience: namely social cognition, active coping, and successful fear extinction. Finally, we also touch upon ongoing issues in neuroimaging study design that will need to be addressed to enable us to harness insight from such studies to improve treatments for - or, ideally, guard against the development of - debilitating post-traumatic stress syndromes.
Collapse
Affiliation(s)
- Agnes Norbury
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Queen Square Institute of Neurology and Mental Health Neuroscience Department, Applied Computational Psychiatry Lab, Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Saren H Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
48
|
Fan Y, Wang R, Yi C, Zhou L, Wu Y. Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification. iScience 2023; 26:106575. [PMID: 37250302 PMCID: PMC10214405 DOI: 10.1016/j.isci.2023.106575] [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: 02/16/2022] [Revised: 08/23/2022] [Accepted: 03/29/2023] [Indexed: 05/31/2023] Open
Abstract
The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.
Collapse
Affiliation(s)
- Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Rong Wang
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710049, China
| | - Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Lv Zhou
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| |
Collapse
|
49
|
Clancy KJ, Devignes Q, Kumar P, May V, Hammack SE, Akman E, Casteen EJ, Pernia CD, Jobson SA, Lewis MW, Daskalakis NP, Carlezon WA, Ressler KJ, Rauch SL, Rosso IM. Circulating PACAP levels are associated with increased amygdala-default mode network resting-state connectivity in posttraumatic stress disorder. Neuropsychopharmacology 2023:10.1038/s41386-023-01593-5. [PMID: 37161077 PMCID: PMC10267202 DOI: 10.1038/s41386-023-01593-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 05/11/2023]
Abstract
The pituitary adenylate cyclase-activating polypeptide (PACAP) system is implicated in posttraumatic stress disorder (PTSD) and related amygdala-mediated arousal and threat reactivity. PTSD is characterized by increased amygdala reactivity to threat and, more recently, aberrant intrinsic connectivity of the amygdala with large-scale resting state networks, specifically the default mode network (DMN). While the influence of PACAP on amygdala reactivity has been described, its association with intrinsic amygdala connectivity remains unknown. To fill this gap, we examined functional connectivity of resting-state functional magnetic resonance imaging (fMRI) in eighty-nine trauma-exposed adults (69 female) screened for PTSD symptoms to examine the association between blood-borne (circulating) PACAP levels and amygdala-DMN connectivity. Higher circulating PACAP levels were associated with increased amygdala connectivity with posterior DMN regions, including the posterior cingulate cortex/precuneus (PCC/Precun) and left angular gyrus (lANG). Consistent with prior work, this effect was seen in female, but not male, participants and the centromedial, but not basolateral, subregions of the amygdala. Clinical association analyses linked amygdala-PCC/Precun connectivity to anxious arousal symptoms, specifically exaggerated startle response. Taken together, our findings converge with previously demonstrated effects of PACAP on amygdala activity in PTSD-related processes and offer novel evidence for an association between PACAP and intrinsic amygdala connectivity patterns in PTSD. Moreover, these data provide preliminary evidence to motivate future work ascertaining the sex- and subregion-specificity of these effects. Such findings may enable novel mechanistic insights into neural circuit dysfunction in PTSD and how the PACAP system confers risk through a disruption of intrinsic resting-state network dynamics.
Collapse
Affiliation(s)
- Kevin J Clancy
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Quentin Devignes
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Poornima Kumar
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Victor May
- Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | | | - Eylül Akman
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Emily J Casteen
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Cameron D Pernia
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sydney A Jobson
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Michael W Lewis
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Nikolaos P Daskalakis
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William A Carlezon
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kerry J Ressler
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Scott L Rauch
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
50
|
Nakuci J, Wasylyshyn N, Cieslak M, Elliott JC, Bansal K, Giesbrecht B, Grafton ST, Vettel JM, Garcia JO, Muldoon SF. Within-subject reproducibility varies in multi-modal, longitudinal brain networks. Sci Rep 2023; 13:6699. [PMID: 37095180 PMCID: PMC10126005 DOI: 10.1038/s41598-023-33441-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
Collapse
Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, 14260, USA.
| | - Nick Wasylyshyn
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - James C Elliott
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Kanika Bansal
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Jean M Vettel
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Javier O Garcia
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah F Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
| |
Collapse
|