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Hackney BC, Pyles JA, Grossman ED. A quantitative comparison of atlas parcellations on the human superior temporal sulcus. Brain Res 2024; 1842:149119. [PMID: 38986829 DOI: 10.1016/j.brainres.2024.149119] [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: 04/05/2024] [Revised: 06/19/2024] [Accepted: 07/07/2024] [Indexed: 07/12/2024]
Abstract
The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.
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Affiliation(s)
- Brandon C Hackney
- Department of Cognitive Sciences, University of California, Irvine, 2201 Social & Behavioral Sciences Gateway, Irvine, CA 92697, United States.
| | - John A Pyles
- Department of Psychology, Center for Human Neuroscience, University of Washington, 119 Guthrie Hall, Seattle, WA 98195, United States
| | - Emily D Grossman
- Department of Cognitive Sciences, University of California, Irvine, 2201 Social & Behavioral Sciences Gateway, Irvine, CA 92697, United States
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2
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Hare MM, Barber A, Shaffer SA, Deligiannidis KM. Bidirectional associations between perinatal allopregnanolone and depression severity with postpartum gray matter volume in adult women. Acta Psychiatr Scand 2024; 150:404-415. [PMID: 38923502 PMCID: PMC11444908 DOI: 10.1111/acps.13723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 04/30/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Perinatal depression (PND) is a debilitating condition affecting maternal well-being and child development. Allopregnanolone (ALLO) is important to perinatal neuroplasticity, however its relationship with depression severity and postpartum structural brain volume is unknown. METHOD We examined perinatal temporal dynamics and bidirectional associations between ALLO and depression severity and the association between these variables and postpartum gray matter volume, using a random intercept cross-lagged panel model. RESULTS We identified a unidirectional predictive relationship between PND severity and ALLO concentration, suggesting greater depression severity early in the perinatal period may contribute to subsequent changes in ALLO concentration (β = 0.26, p = 0.009), while variations in ALLO levels during the perinatal period influences the development and severity of depressive symptoms later in the postpartum period (β = 0.38, p = 0.007). Antepartum depression severity (Visit 2, β = 0.35, p = 0.004), ALLO concentration (Visit 2, β = 0.37, p = 0.001), and postpartum depression severity (Visit 3, β = 0.39, p = 0.031), each predicted the right anterior cingulate volume. Antepartum ALLO concentration (Visit 2, β = 0.29, p = 0.001) predicted left suborbital sulcus volume. Antepartum depression severity (Visit 1, β = 0.39, p = 0.006 and Visit 2, β = 0.48, p < 0.001) predicted the right straight gyrus volume. Postpartum depression severity (Visit 3, β = 0.36, p = 0.001) predicted left middle-posterior cingulate volume. CONCLUSION These results provide the first evidence of bidirectional associations between perinatal ALLO and depression severity with postpartum gray matter volume.
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Affiliation(s)
- Megan M Hare
- Center for Children and Families, Department of Psychology, Florida International University, Miami, Florida, USA
| | - Anita Barber
- Department of Psychiatry, Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
| | - Scott A Shaffer
- The Mass Spectrometry Facility, UMass Chan Medical School, Shrewsbury, Massachusetts, USA
| | - Kristina M Deligiannidis
- Department of Psychiatry, Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
- Departments of Psychiatry, Molecular Medicine and Obstetrics and Gynecology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
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3
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Labonte AK, Camacho MC, Moser J, Koirala S, Laumann TO, Marek S, Fair D, Sylvester CM. Precision Functional Mapping to Advance Developmental Psychiatry Research. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100370. [PMID: 39309212 PMCID: PMC11416589 DOI: 10.1016/j.bpsgos.2024.100370] [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: 05/17/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 09/25/2024] Open
Abstract
Many psychiatric conditions have their roots in early development. Individual differences in prenatal brain function (which is influenced by a combination of genetic risk and the prenatal environment) likely interact with individual differences in postnatal experience, resulting in substantial variation in brain functional organization and development in infancy. Neuroimaging has been a powerful tool for understanding typical and atypical brain function and holds promise for uncovering the neurodevelopmental basis of psychiatric illness; however, its clinical utility has been relatively limited thus far. A substantial challenge in this endeavor is the traditional approach of averaging brain data across groups despite individuals varying in their brain organization, which likely obscures important clinically relevant individual variation. Precision functional mapping (PFM) is a neuroimaging technique that allows the capture of individual-specific and highly reliable functional brain properties. Here, we discuss how PFM, through its focus on individuals, has provided novel insights for understanding brain organization across the life span and its promise in elucidating the neural basis of psychiatric disorders. We first summarize the extant literature on PFM in normative populations, followed by its limited utilization in studying psychiatric conditions in adults. We conclude by discussing the potential for infant PFM in advancing developmental precision psychiatry applications, given that many psychiatric disorders start during early infancy and are associated with changes in individual-specific functional neuroanatomy. By exploring the intersection of PFM, development, and psychiatric research, this article underscores the importance of individualized approaches in unraveling the complexities of brain function and improving clinical outcomes across development.
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Affiliation(s)
- Alyssa K. Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, Missouri
| | - M. Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Scott Marek
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota
- Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | - Chad M. Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri
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4
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Gracia-Tabuenca Z, Barbeau EB, Kousaie S, Chen JK, Chai X, Klein D. Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico-cerebellar pathway in early second language acquisition. Commun Biol 2024; 7:1298. [PMID: 39390147 PMCID: PMC11467263 DOI: 10.1038/s42003-024-06965-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 09/25/2024] [Indexed: 10/12/2024] Open
Abstract
Bilingualism has a profound impact on the structure and function of the brain, but it is not yet well understood how this experience influences brain functional organization. We examine a large sample (151 participants) of monolinguals and bilinguals with varied age of second language acquisition, who underwent resting-state functional magnetic brain imaging. Whole-brain network analyses reveal higher global efficiency in bilingual individuals than monolinguals, indicating enhanced functional integration in the bilingual brain. Moreover, the age at which the second language was acquired correlated with this increased efficiency, suggesting that earlier exposure to a second language has lasting positive effects on brain functional organization. Further investigation using the network-based statistics approach indicates that this effect is primarily driven by heightened functional connectivity between association networks and the cerebellum. These findings show that the timing of bilingual learning experience alters the brain functional organization at both global and local levels.
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Affiliation(s)
- Zeus Gracia-Tabuenca
- Department of Statistical Methods, University of Zaragoza, Zaragoza, Aragón, Spain.
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
| | - Elise B Barbeau
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Shanna Kousaie
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Jen-Kai Chen
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Denise Klein
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
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5
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Zhang J, Li H, Qu J, Liu X, Feng X, Fu X, Mei L. Language proficiency is associated with neural representational dimensionality of semantic concepts. BRAIN AND LANGUAGE 2024; 258:105485. [PMID: 39388908 DOI: 10.1016/j.bandl.2024.105485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 09/28/2024] [Accepted: 10/04/2024] [Indexed: 10/12/2024]
Abstract
Previous studies suggest that semantic concepts are characterized by high-dimensional neural representations and that language proficiency affects semantic processing. However, it is not clear whether language proficiency modulates the dimensional representations of semantic concepts at the neural level. To address this question, the present study adopted principal component analysis (PCA) and representational similarity analysis (RSA) to examine the differences in representational dimensionalities (RDs) and in semantic representations between words in highly proficient (Chinese) and less proficient (English) language. PCA results revealed that language proficiency increased the dimensions of lexical representations in the left inferior frontal gyrus, temporal pole, inferior temporal gyrus, supramarginal gyrus, angular gyrus, and fusiform gyrus. RSA results further showed that these regions represented semantic information and that higher semantic representations were observed in highly proficient language relative to less proficient language. These results suggest that language proficiency is associated with the neural representational dimensionality of semantic concepts.
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Affiliation(s)
- Jingxian Zhang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Huiling Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Jing Qu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Xiaoyu Liu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Xiaoxue Feng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Xin Fu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Leilei Mei
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, South China Normal University, Ministry of Education, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, 510631, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; School of Psychology, South China Normal University, Guangzhou 510631, China.
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6
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Xia H, Hou Y, Li Q, Chen A. A meta-analysis of cognitive flexibility in aging: Perspective from functional network and lateralization. Hum Brain Mapp 2024; 45:e70031. [PMID: 39360550 PMCID: PMC11447525 DOI: 10.1002/hbm.70031] [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: 04/16/2024] [Revised: 08/21/2024] [Accepted: 09/10/2024] [Indexed: 10/04/2024] Open
Abstract
Cognitive flexibility, the ability to switch between mental processes to generate appropriate behavioral responses, is reduced with typical aging. Previous studies have found that age-related declines in cognitive flexibility are often accompanied by variations in the activation of multiple regions. However, no meta-analyses have examined the relationship between cognitive flexibility in aging and age-related variations in activation within large-scale networks. Here, we conducted a meta-analysis employing multilevel kernel density analysis to identify regions with different activity patterns between age groups, and determined how these regions fall into functional networks. We also employed lateralization analysis to explore the spatial distribution of regions exhibiting group differences in activation. The permutation tests based on Monte Carlo simulation were used to determine the significance of the activation and lateralization results. The results showed that cognitive flexibility in aging was associated with both decreased and increased activation in several functional networks. Compared to young adults, older adults exhibited increased activation in the default mode, dorsal attention, ventral attention, and somatomotor networks, while displayed decreased activation in the visual network. Moreover, we found a global-level left lateralization for regions with decreased activation, but no lateralization for regions with higher activation in older adults. At the network level, the regions with decreased activation were left-lateralized, while the regions with increased activation showed varying lateralization patterns within different networks. To sum up, we found that networks that support various mental functions contribute to age-related variations in cognitive flexibility. Additionally, the aging brain exhibited network-dependent activation and lateralization patterns in response to tasks involving cognitive flexibility. We highlighted that the comprehensive meta-analysis in this study offered new insights into understanding cognitive flexibility in aging from a network perspective.
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Affiliation(s)
- Haishuo Xia
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Yongqing Hou
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Qing Li
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Antao Chen
- School of Psychology, Research Center for Exercise and Brain ScienceShanghai University of SportChina
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7
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Li J, Segel A, Feng X, Tu JC, Eck A, King KT, Adeyemo B, Karcher NR, Chen L, Eggebrecht AT, Wheelock MD. Network-level enrichment provides a framework for biological interpretation of machine learning results. Netw Neurosci 2024; 8:762-790. [PMID: 39355443 PMCID: PMC11349033 DOI: 10.1162/netn_a_00383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/15/2024] [Indexed: 10/03/2024] Open
Abstract
Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.
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Affiliation(s)
- Jiaqi Li
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Ari Segel
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Xinyang Feng
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Andy Eck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Kelsey T King
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University in St. Louis, MO, USA
| | - Nicole R Karcher
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Likai Chen
- Department of Statistics and Data Science, Washington University in St. Louis, MO, USA
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
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8
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Thomas E, Juliano A, Owens M, Cupertino RB, Mackey S, Hermosillo R, Miranda-Dominguez O, Conan G, Ahmed M, Fair DA, Graham AM, Goode NJ, Kandjoze UP, Potter A, Garavan H, Albaugh MD. Amygdala connectivity is associated with withdrawn/depressed behavior in a large sample of children from the Adolescent Brain Cognitive Development (ABCD) Study®. Psychiatry Res Neuroimaging 2024; 344:111877. [PMID: 39232266 DOI: 10.1016/j.pscychresns.2024.111877] [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: 06/13/2024] [Revised: 07/23/2024] [Accepted: 08/17/2024] [Indexed: 09/06/2024]
Abstract
Many psychopathologies tied to internalizing symptomatology emerge during adolescence, therefore identifying neural markers of internalizing behavior in childhood may allow for early intervention. We utilized data from the Adolescent Brain and Cognitive Development (ABCD) Study® to evaluate associations between cortico-amygdalar functional connectivity, polygenic risk for depression (PRSD), traumatic events experienced, internalizing behavior, and internalizing subscales: withdrawn/depressed behavior, somatic complaints, and anxious/depressed behaviors. Data from 6371 children (ages 9-11) were used to analyze amygdala resting-state fMRI connectivity to Gordon parcellation based whole-brain regions of interest (ROIs). Internalizing behaviors were measured using the parent-reported Child Behavior Checklist. Linear mixed-effects models were used to identify patterns of cortico-amygdalar connectivity associated with internalizing behaviors. Results indicated left amygdala connections to auditory, frontoparietal network (FPN), and dorsal attention network (DAN) ROIs were significantly associated with withdrawn/depressed symptomatology. Connections relevant for withdrawn/depressed behavior were linked to social behaviors. Specifically, amygdala connections to DAN were associated with social anxiety, social impairment, and social problems. Additionally, an amygdala connection to the FPN ROI and the auditory network ROI was associated with social anxiety and social problems, respectively. Therefore, it may be important to account for social behaviors when looking for brain correlates of depression.
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Affiliation(s)
- Elina Thomas
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA; Department of Psychology, Earlham College, 801 W National Rd, Richmond, IN 47374, USA.
| | - Anthony Juliano
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
| | - Max Owens
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
| | - Renata B Cupertino
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
| | - Robert Hermosillo
- Department of Pediatrics, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55313, USA
| | - Oscar Miranda-Dominguez
- Department of Pediatrics, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55313, USA
| | - Greg Conan
- Department of Pediatrics, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55313, USA
| | - Moosa Ahmed
- Department of Pediatrics, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55313, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55313, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Nicholas J Goode
- Department of Psychology, Earlham College, 801 W National Rd, Richmond, IN 47374, USA
| | - Uapingena P Kandjoze
- Department of Psychology, Earlham College, 801 W National Rd, Richmond, IN 47374, USA
| | - Alexi Potter
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
| | - Matthew D Albaugh
- Department of Psychiatry, University of Vermont Medical Center, 111 Colchester Avenue Burlington, VT, 05401, USA
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9
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Triana AM, Salmi J, Hayward NMEA, Saramäki J, Glerean E. Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity. PLoS Biol 2024; 22:e3002797. [PMID: 39378200 PMCID: PMC11460715 DOI: 10.1371/journal.pbio.3002797] [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: 04/14/2022] [Accepted: 08/08/2024] [Indexed: 10/10/2024] Open
Abstract
Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Juha Salmi
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
- Aalto Behavioral Laboratory, Aalto Neuroimaging, Aalto University, Espoo, Finland
- MAGICS, Aalto Studios, Aalto University, Espoo, Finland
- Unit of Psychology, Faculty of Education and Psychology, Oulu University, Oulu, Finland
| | | | - Jari Saramäki
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
- Advanced Magnetic Imaging Centre, Aalto University, Espoo, Finland
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10
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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. Neuroimage 2024; 299:120827. [PMID: 39245397 DOI: 10.1016/j.neuroimage.2024.120827] [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: 03/20/2024] [Revised: 08/02/2024] [Accepted: 08/31/2024] [Indexed: 09/10/2024] Open
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States.
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute for Behavioral Genetics, University of Colorado Boulder, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, United States
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States
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11
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Mitchell ME, Nugiel T. Puberty interacts with sleep and brain network organization to predict mental health. Front Hum Neurosci 2024; 18:1379945. [PMID: 39398321 PMCID: PMC11466844 DOI: 10.3389/fnhum.2024.1379945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 07/30/2024] [Indexed: 10/15/2024] Open
Abstract
Introduction Along with pubertal development, the transition to adolescence brings about increased risk for sleep disturbances and mental health problems. Functional connectivity of overlapping large-scale brain networks, such as increased connectivity between the default mode and dorsal attention networks, has been reported to relate to both sleep and mental health problems. Clarifying whether pubertal development interacts with sleep disturbances and functional brain networks to predict mental health may provide information to improve the timing and design of interventions targeting sleep disturbances in adolescents. Methods To examine how pubertal status and tempo relate to sleep disturbances and shape the relationship between sleep disturbances and mental health problems, we harnessed a large sample of children aged 10-14 years from the Adolescent Brain and Cognitive Development (ABCD) Study (N ~ 3,000-10,000). We used graph theoretical tools to probe how pubertal development concurrently interacts with sleep disturbances and brain network organization to predict mental health problems. Results We found that advanced pubertal status, but not pubertal tempo, predicted sleep disturbances; however, both pubertal status and tempo interact with sleep disturbances to predict mental health problems and engage in three-way interactions with sleep and brain network organization to predict mental health problems. Discussion Overall, this work suggests that less advanced pubertal status and slower tempo are risk factors for the strongest links between sleep disturbances, brain organization, and mental health problems. Further, our findings speak to the importance of accounting for interactions in the constellation of factors that surround complex behavioral and clinical syndromes, here internalizing and externalizing disorders, and provide new context to consider for targeted interventions.
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Affiliation(s)
- Mackenzie E. Mitchell
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tehila Nugiel
- Department of Psychology, Florida State University, Tallahassee, FL, United States
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12
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Kardan O, Jones N, Wheelock MD, Michael C, Angstadt M, Molloy MF, Cope LM, Martz MM, McCurry KL, Hardee JE, Rosenberg MD, Weigard AS, Hyde LW, Sripada C, Heitzeg MM. Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.26.615215. [PMID: 39386610 PMCID: PMC11463351 DOI: 10.1101/2024.09.26.615215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.
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13
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Elton A, Lewis B, Nixon SJ. The Effects of Adverse Life Events on Brain Development in the ABCD Study®: A Propensity-weighted Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24314355. [PMID: 39399053 PMCID: PMC11469365 DOI: 10.1101/2024.09.25.24314355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
Longitudinal studies of the effects of adversity on human brain development are complicated by the association of stressful events with confounding variables. To counter this bias, we apply a propensity-weighted analysis of the first two years of The Adolescent Brain Cognitive Development SM (ABCD) Study® data, employing a machine learning analysis weighted by individuals' propensity to experience adversity. Data included 338 resting-state functional connections from 7190 youth (46% female), divided into a training group (80%) and an independent testing group (20%). Propensity scores were computed using 390 variables to balance across two-year adverse life event exposures. Using elastic net regularization with and without inverse propensity weighting, we developed linear models in which changes in functional connectivity of brain connections during the two-year period served as predictors of the number of adverse events experienced during that same period. Haufe's method was applied to forward-transform the backward prediction models. We also tested whether brain changes associated with adverse events correlated with concomitant changes in internalizing or externalizing behaviors or to academic achievement. In the propensity-weighted analysis, brain development significantly predicted the number of adverse events experienced during that period in both the training group (ρ=0.14, p<0.001) and the independent testing group (ρ=0.10, p<0.001). The predictor indicated a general pattern of decreased functional connectivity between large-scale networks and subcortical brain regions, particularly for cingulo-opercular and sensorimotor networks. These network-to-subcortical functional connectivity decreases inversely associated with the development of internalizing symptoms, suggesting adverse events promoted adaptive brain changes that may buffer against stress-related psychopathology. However, these same functional connections were also associated with poorer grades at the two-year follow-up. Although cortical-subcortical brain developmental responses to adversity potentially shield against stress-induced mood and anxiety disorders, they may be detrimental to other domains such as academic success.
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14
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Chu N, Wang D, Qu S, Yan C, Luo G, Liu X, Hu X, Zhu J, Li X, Sun S, Hu B. Stable construction and analysis of MDD modular networks based on multi-center EEG data. Prog Neuropsychopharmacol Biol Psychiatry 2024; 136:111149. [PMID: 39303847 DOI: 10.1016/j.pnpbp.2024.111149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 09/12/2024] [Accepted: 09/15/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND The modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated. METHOD We used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands. RESULTS The combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis. CONCLUSION Our results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.
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Affiliation(s)
- Na Chu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Dixin Wang
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Xiping Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Jing Zhu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Shuting Sun
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China.
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15
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Gorham LS, Latham AR, Alexopoulos D, Kenley JK, Iannopollo E, Lean RE, Loseille D, Smyser TA, Neil JJ, Rogers CE, Smyser CD, Garcia K. Children born very preterm experience altered cortical expansion over the first decade of life. Brain Commun 2024; 6:fcae318. [PMID: 39329081 PMCID: PMC11426356 DOI: 10.1093/braincomms/fcae318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 08/09/2024] [Accepted: 09/16/2024] [Indexed: 09/28/2024] Open
Abstract
The brain develops rapidly from the final trimester of gestation through childhood, with cortical surface area expanding greatly in the first decade of life. However, it is unclear exactly where and how cortical surface area changes after birth, or how prematurity affects these developmental trajectories. Fifty-two very preterm (gestational age at birth = 26 ± 1.6 weeks) and 41 full-term (gestational age at birth = 39 ± 1.2 weeks) infants were scanned using structural magnetic resonance imaging at term-equivalent age and again at 9/10 years of age. Individual cortical surface reconstructions were extracted for each scan. Infant and 9/10 cortical surfaces were aligned using anatomically constrained Multimodal Surface Matching (aMSM), a technique that allows calculation of local expansion gradients across the cortical surface for each individual subject. At the neonatal time point, very preterm infants had significantly smaller surface area than their full-term peers (P < 0.001), but at the age 9/10-year time point, very preterm and full-term children had comparable surface area (P > 0.05). Across all subjects, cortical expansion by age 9/10 years was most pronounced in frontal, temporal, and supramarginal/inferior parietal junction areas, which are key association cortices (P Spin < 0.001). Very preterm children showed greater cortical surface area expansion between term-equivalent age and age 9/10 compared to their full-term peers in the medial and lateral frontal areas, precuneus, and middle temporal/banks of the superior sulcus junction (P < 0.05). Furthermore, within the very preterm group, expansion was highly variable within the orbitofrontal cortex and posterior regions of the brain. By mapping these patterns across the cortex, we identify differences in association cortices that are known to be important for executive functioning, emotion processing, and social cognition. Additional longitudinal work will be needed to understand if increased expansion in very preterm children is adaptive, or if differences persist into adulthood.
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Affiliation(s)
- Lisa S Gorham
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Aidan R Latham
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Emily Iannopollo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rachel E Lean
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David Loseille
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tara A Smyser
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kara Garcia
- Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Evansville, IN 46202, USA
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
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16
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Avram M, Fortea L, Wollner L, Coenen R, Korda A, Rogg H, Holze F, Vizeli P, Ley L, Radua J, Müller F, Liechti ME, Borgwardt S. Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine. Mol Psychiatry 2024:10.1038/s41380-024-02734-y. [PMID: 39261671 DOI: 10.1038/s41380-024-02734-y] [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: 01/16/2024] [Revised: 08/22/2024] [Accepted: 08/28/2024] [Indexed: 09/13/2024]
Abstract
Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.
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Affiliation(s)
- Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany.
| | - Lydia Fortea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain
| | - Lea Wollner
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Ricarda Coenen
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alexandra Korda
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Helena Rogg
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Friederike Holze
- Division of Clinical Pharmacology and Toxicology, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Patrick Vizeli
- Division of Clinical Pharmacology and Toxicology, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Laura Ley
- Division of Clinical Pharmacology and Toxicology, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Felix Müller
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Matthias E Liechti
- Division of Clinical Pharmacology and Toxicology, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
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17
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Tooley UA, Latham A, Kenley JK, Alexopoulos D, Smyser TA, Nielsen AN, Gorham L, Warner BB, Shimony JS, Neil JJ, Luby JL, Barch DM, Rogers CE, Smyser CD. Prenatal environment is associated with the pace of cortical network development over the first three years of life. Nat Commun 2024; 15:7932. [PMID: 39256419 PMCID: PMC11387486 DOI: 10.1038/s41467-024-52242-4] [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: 08/14/2023] [Accepted: 08/30/2024] [Indexed: 09/12/2024] Open
Abstract
Environmental influences on brain structure and function during early development have been well-characterized, but whether early environments are associated with the pace of brain development is not clear. In pre-registered analyses, we use flexible non-linear models to test the theory that prenatal disadvantage is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal disadvantage was assessed using a latent factor of socioeconomic disadvantage that included measures of mother's income-to-needs ratio, educational attainment, area deprivation index, insurance status, and nutrition. We find that prenatal disadvantage is associated with developmental increases in cortical network segregation, with neonates and toddlers with greater exposure to prenatal disadvantage showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between prenatal disadvantage and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. Disadvantage-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results shed light on associations between the early environment and trajectories of cortical development.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA.
| | - Aidan Latham
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Tara A Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Lisa Gorham
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joan L Luby
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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18
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Tu JC, Myers M, Li W, Li J, Wang X, Dierker D, Day TKM, Snyder AZ, Latham A, Kenley JK, Sobolewski CM, Wang Y, Labonte AK, Feczko E, Kardan O, Moore LA, Sylvester CM, Fair DA, Elison JT, Warner BB, Barch DM, Rogers CE, Luby JL, Smyser CD, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.612056. [PMID: 39314355 PMCID: PMC11419084 DOI: 10.1101/2024.09.09.612056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
The cerebral cortex comprises discrete cortical areas that form during development. Accurate area parcellation in neuroimaging studies enhances statistical power and comparability across studies. The formation of cortical areas is influenced by intrinsic embryonic patterning as well as extrinsic inputs, particularly through postnatal exposure. Given the substantial changes in brain volume, microstructure, and functional connectivity during the first years of life, we hypothesized that cortical areas in 1-to-3-year-olds would exhibit major differences from those in neonates and progressively resemble adults as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrated high reproducibility of these cortical regions across 1-to-3-year-olds in two independent datasets. The area boundaries in 1-to-3-year-olds were more similar to adults than neonates. While the age-specific group parcellation fitted better to the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provided connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.
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Affiliation(s)
| | - Michael Myers
- Department of Psychiatry, Washington University in St. Louis
| | - Wei Li
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Jiaqi Li
- Department of Mathematics and Statistics, Washington University in St. Louis
- Department of Statistics, University of Chicago
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Trevor K M Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | | | - Aidan Latham
- Department of Neurology, Washington University in St. Louis
| | | | - Chloe M Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Jed T Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | | | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St Louis
| | | | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis
- Department of Psychiatry, Washington University in St. Louis
- Department of Neurology, Washington University in St. Louis
- Department of Pediatrics, Washington University in St. Louis
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis
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Fan Y, White SR. Review of weighted exponential random graph models frameworks applied to neuroimaging. Stat Med 2024; 43:3881-3898. [PMID: 38932498 DOI: 10.1002/sim.10162] [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: 10/24/2023] [Revised: 05/15/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024]
Abstract
Neuro-imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non-robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi-Layered ERGM is currently the most suitable framework.
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Affiliation(s)
- Yefeng Fan
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Simon R White
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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20
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Alahmadi A. Functional Connectivity Profiles of Ten Sub-Regions within the Premotor and Supplementary Motor Areas: Insights into Neurophysiological Integration. Diagnostics (Basel) 2024; 14:1990. [PMID: 39272774 PMCID: PMC11394331 DOI: 10.3390/diagnostics14171990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 09/15/2024] Open
Abstract
Objectives: This study aimed to comprehensively investigate the functional connectivity of ten sub-regions within the premotor and supplementary motor areas (Right and Left Premotor 6d1, 6d2, 6d3, and Right and Left pre-Supplementary Motor (presma) and SMA). Using advanced magnetic resonance imaging (MRI), the objective was to understand the neurophysiological integrative characteristics of these regions by examining their connectivity with eight distinct functional brain networks. While previous studies have largely treated these areas as homogeneous entities, there is a significant gap in our understanding of the specific roles and connectivity profiles of their distinct sub-regions. The goal was to uncover the roles of these regions beyond conventional motor functions, contributing to a more holistic understanding of brain functioning. Methods: The study involved 198 healthy volunteers, with the primary methodology being functional connectivity analysis using advanced MRI techniques. Ten sub-regions within the premotor and supplementary motor areas served as seed regions, and their connectivity with eight distinct brain regional functional networks, including the Sensorimotor, Dorsal Attention, Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks, was investigated. This approach allowed for the exploration of synchronized activity between these critical brain areas, shedding light on their integrated functioning and relationships with other brain networks. Results: The study revealed a nuanced landscape of functional connectivity for the premotor and supplementary motor areas with the main functional brain networks. Despite their high functional connectedness within the motor network, these regions displayed diverse functional integrations with other networks. There was moderate connectivity with the Sensorimotor and Dorsal Attention networks, highlighting their roles in motor execution and attentional processes. However, connectivity with the Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks was generally low, indicating a primary focus on motor-related tasks. Conclusions: This study emphasized the multifaceted roles of the sub-regions of the premotor and supplementary motor areas. Beyond their crucial involvement in motor functions, these regions exhibited varied functional integrations with different brain networks. The observed disparities, especially in the Sensorimotor and Dorsal Attention networks, indicated a nuanced and specialized involvement of these regions in diverse cognitive functions. By delineating the specific connectivity profiles of these sub-regions, this study addresses the existing knowledge gap and suggests unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. The results suggested unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. This study underscores the importance of considering the broader neurophysiological landscape to comprehend the intricate roles of these brain areas, contributing to ongoing efforts in unravelling the complexities of brain function.
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Affiliation(s)
- Adnan Alahmadi
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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21
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Petersen SE, Seitzman BA, Nelson SM, Wig GS, Gordon EM. Principles of cortical areas and their implications for neuroimaging. Neuron 2024; 112:2837-2853. [PMID: 38834069 DOI: 10.1016/j.neuron.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.
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Affiliation(s)
- Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin A Seitzman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gagan S Wig
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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Rajesh A, Seider NA, Newbold DJ, Adeyemo B, Marek S, Greene DJ, Snyder AZ, Shimony JS, Laumann TO, Dosenbach NUF, Gordon EM. Structure-function coupling in highly sampled individual brains. Cereb Cortex 2024; 34:bhae361. [PMID: 39277800 DOI: 10.1093/cercor/bhae361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 09/17/2024] Open
Abstract
Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 min of diffusion-weighted MRI for SC and 360 min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.
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Affiliation(s)
- Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
| | - Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Dillan J Newbold
- Department of Neurology, New York Langone Medical Center, 550 First Avenue, New York, NY, 10016, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92037, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neurology, New York Langone Medical Center, 550 First Avenue, New York, NY, 10016, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neuroscience, Washington University, 660 S. Euclid Ave.St. Louis, MO 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO 63130, USA
- Program in Occupational Therapy, Washington University, 4444 Forest Park Ave, St. Louis, MO 63108, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
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Li T, Chen J, Zhao B, Garden GA, Giovanello KS, Wu G, Zhu H. The Interaction Effects of Sex, Age, APOE and Common Health Risk Factors on Human Brain Functions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.05.24311482. [PMID: 39148839 PMCID: PMC11326347 DOI: 10.1101/2024.08.05.24311482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Importance Nonlinear changes in brain function during aging are shaped by a complex interplay of factors, including sex, age, genetics, and modifiable health risk factors. However, the combined effects and underlying mechanisms of these factors on brain functional connectivity remain poorly understood. Objective To comprehensively investigate the combined associations of sex, age, APOE genotypes, and ten common modifiable health risk factors with brain functional connectivities during aging. Design Setting and Participants This analysis used data from 36,630 UK Biobank participants, aged 44-81, who were assessed for sex, age, APOE genotypes, 10 health risk factors, and brain functional connectivities through resting-state functional magnetic resonance imaging. Main Outcomes and Measures Brain functional connectivities were evaluated through within- and between-network functional connectivities and connectivity strength. Associations between risk factors and brain functional connectivities, including their interaction effects, were analyzed. Results Hypertension, BMI, and education were the top three influential factors. Sex-specific effects were also observed in interactions involving APOE4 gene, smoking, alcohol consumption, diabetes, BMI, and education. Notably, a negative sex-excessive alcohol interaction showed a stronger negative effect on functional connectivities in males, particularly between the dorsal attention network and the language network, while moderate alcohol consumption appeared to have protective effects. A significant negative interaction between sex and APOE4 revealed a greater reduction in functional connectivity between the cingulo-opercular network and the posterior multimodal network in male APOE4 carriers. Additional findings included a negative age-BMI interaction between the visual and dorsal attention networks, and a positive age-hypertension interaction between the frontoparietal and default mode networks. Conclusions and Relevance The findings highlight significant sex disparities in the associations between age, the APOE-ε4 gene, modifiable health risk factors, and brain functional connectivity, emphasizing the necessity of jointly considering these factors to gain a deeper understanding of the complex processes underlying brain aging.
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Affiliation(s)
- Tengfei Li
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, the Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Statistics and Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Insititute for Developmental Disabilities, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Statistics and Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Departments of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Madden DJ, Merenstein JL, Mullin HA, Jain S, Rudolph MD, Cohen JR. Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance. Brain Struct Funct 2024; 229:1533-1559. [PMID: 38856933 PMCID: PMC11374505 DOI: 10.1007/s00429-024-02807-2] [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: 12/29/2023] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
| | - Hollie A Mullin
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- Department of Psychology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL, 32804, USA
| | - Marc D Rudolph
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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25
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Michael C, Taxali A, Angstadt M, Kardan O, Weigard A, Molloy MF, McCurry KL, Hyde LW, Heitzeg MM, Sripada C. Socioeconomic resources in youth are linked to divergent patterns of network integration/segregation across the brain's transmodal axis. PNAS NEXUS 2024; 3:pgae412. [PMID: 39323982 PMCID: PMC11423146 DOI: 10.1093/pnasnexus/pgae412] [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: 02/07/2024] [Accepted: 08/07/2024] [Indexed: 09/27/2024]
Abstract
Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully relate to intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neuroscientific meaning of these widespread neural differences remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage graph theory to precisely characterize multivariate and univariate associations between SER across household and neighborhood contexts and the intrinsic functional architecture of brain regions in 5,821 youth (9-10 years) from the Adolescent Brain Cognitive Development Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are associated with greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that topological associations with SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread associations between SER and brain organization. This study highlights both higher-order and somatomotor networks that are differentially implicated in environmental stress, disadvantage, and opportunity in youth.
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Affiliation(s)
- Cleanthis Michael
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Aman Taxali
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Omid Kardan
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander Weigard
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - M Fiona Molloy
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Luke W Hyde
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
- Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
| | - Mary M Heitzeg
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
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26
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Karcher NR, Sotiras A, Niendam TA, Walker EF, Jackson JJ, Barch DM. Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:939-947. [PMID: 38849031 PMCID: PMC11381151 DOI: 10.1016/j.bpsc.2024.05.009] [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: 03/06/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses were used to examine the most important baseline factors distinguishing persistent distressing PLEs. METHODS Using Adolescent Brain Cognitive Development (ABCD) Study data on PLEs from 3 time points (ages 9-13 years), we created the following groups: individuals with persistent distressing PLEs (n = 305), individuals with transient distressing PLEs (n = 374), and individuals with low-level PLEs demographically matched to either the persistent distressing PLEs group (n = 305) or the transient distressing PLEs group (n = 374). Random forest classification models were trained to distinguish persistent distressing PLEs from low-level PLEs, transient distressing PLEs from low-level PLEs, and persistent distressing PLEs from transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting-state functional connectivity], developmental milestone delays, internalizing symptoms, adverse childhood experiences). RESULTS The model distinguishing persistent distressing PLEs from low-level PLEs showed the highest accuracy (test sample accuracy = 69.33%; 95% CI, 61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood experiences, and cognitive functioning. Models for distinguishing persistent PLEs from transient distressing PLEs generally performed poorly. CONCLUSIONS Model performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood experiences were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.
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Affiliation(s)
- Nicole R Karcher
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri; Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Tara A Niendam
- Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, California
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, Georgia
| | - Joshua J Jackson
- Department of Psychological and Brain Sciences, Washington University in St Louis, St. Louis, Missouri
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St Louis, St. Louis, Missouri
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27
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Feilong M, Jiahui G, Gobbini MI, Haxby JV. A cortical surface template for human neuroscience. Nat Methods 2024; 21:1736-1742. [PMID: 39014074 PMCID: PMC11399089 DOI: 10.1038/s41592-024-02346-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/06/2024] [Indexed: 07/18/2024]
Abstract
Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly available high-quality structural scans of 1,031 brains-25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-participant correlations based on onavg than on other templates, and onavg only needs three-quarters as much data to achieve the same performance compared with other templates. The optimized sampling also reduces CPU time across algorithms by 1.3-22.4% due to less variation in the number of vertices in each searchlight.
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Affiliation(s)
- Ma Feilong
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, USA.
| | - Guo Jiahui
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, USA
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Maria Ida Gobbini
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - James V Haxby
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, USA.
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Balderston NL, Duprat RJ, Long H, Scully M, Deluisi JA, Figueroa-Gonzalez A, Teferi M, Sheline YI, Oathes DJ. Neuromodulatory transcranial magnetic stimulation (TMS) changes functional connectivity proportional to the electric-field induced by the TMS pulse. Clin Neurophysiol 2024; 165:16-25. [PMID: 38945031 PMCID: PMC11323191 DOI: 10.1016/j.clinph.2024.06.007] [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: 03/27/2023] [Revised: 04/15/2024] [Accepted: 06/09/2024] [Indexed: 07/02/2024]
Abstract
OBJECTIVE Transcranial magnetic stimulation (TMS) can efficiently and robustly modulate synaptic plasticity, but little is known about how TMS affects functional connectivity (rs-fMRI). Accordingly, this project characterized TMS-induced rsFC changes in depressed patients who received 3 days of left prefrontal intermittent theta burst stimulation (iTBS). METHODS rs-fMRI was collected from 16 subjects before and after iTBS. Correlation matrices were constructed from the cleaned rs-fMRI data. Electric-field models were conducted and used to predict pre-post changes in rs-fMRI. Site by orientation heatmaps were created for vectors centered on the stimulation site and a control site (contralateral motor cortex). RESULTS For the stimulation site, there was a clear relationship between both site and coil orientation, and connectivity changes. As distance from the stimulation site increased, prediction accuracy decreased. Similarly, as eccentricity from the optimal orientation increased, prediction accuracy decreased. The systematic effects described above were not apparent in the heatmap centered on the control site. CONCLUSIONS These results suggest that rs-fMRI following iTBS changes systematically as a function of the distribution of electrical energy delivered from the TMS pulse, as represented by the e-field model. SIGNIFICANCE This finding lays the groundwork for future studies to individualize TMS targeting based on how predicted rs-fMRI changes might impact psychiatric symptoms.
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Affiliation(s)
- Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA.
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Long
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Morgan Scully
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph A Deluisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Almaris Figueroa-Gonzalez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Marta Teferi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
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29
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Karoly HC, Kirk‐Provencher KT, Schacht JP, Gowin JL. Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies. Addict Biol 2024; 29:e13439. [PMID: 39317645 PMCID: PMC11421948 DOI: 10.1111/adb.13439] [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: 02/22/2024] [Revised: 08/29/2024] [Accepted: 09/01/2024] [Indexed: 09/26/2024]
Abstract
Alcohol exposure affects brain structure, but the extent to which its effects differ across development remains unclear. Several countries are considering changes to recommended guidelines for alcohol consumption, so high-quality evidence is needed. Many studies have been conducted among small samples, but recent efforts have been made to acquire large samples to characterize alcohol's effects on the brain on a population level. Several large-scale consortia have acquired such samples, but this evidence has not been synthesized across the lifespan. We conducted a systematic review of large-scale neuroimaging studies examining effects of alcohol exposure on brain structure at multiple developmental stages. We included studies with an alcohol-exposed sample of at least N = 100 from the following consortia: ABCD, ENIGMA, NCANDA, IMAGEN, Framingham Offspring Study, HCP and UK BioBank. Twenty-seven studies were included, examining prenatal (N = 1), adolescent (N = 9), low-to-moderate-level adult (N = 11) and heavy adult (N = 7) exposure. Prenatal exposure was associated with greater brain volume at ages 9-10, but contemporaneous alcohol consumption during adolescence and adulthood was associated with smaller volume/thickness. Both low-to-moderate consumption and heavy consumption were characterized by smaller volume and thickness in frontal, temporal and parietal regions, and reductions in insula, cingulate and subcortical structures. Adolescent consumption had similar effects, with less consistent evidence for smaller cingulate, insula and subcortical volume. In sum, prenatal exposure was associated with larger volume, while adolescent and adult alcohol exposure was associated with smaller volume and thickness, suggesting that regional patterns of effects of alcohol are similar in adolescence and adulthood.
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Affiliation(s)
- Hollis C. Karoly
- Department of PsychologyColorado State UniversityFort CollinsColoradoUSA
| | - Katelyn T. Kirk‐Provencher
- Department of Radiology, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Joseph P. Schacht
- Department of Psychiatry, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Joshua L. Gowin
- Department of Radiology, School of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
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Norton SA, Gorelik AJ, Paul SE, Johnson EC, Baranger DA, Siudzinski JL, Li ZA, Bondy E, Modi H, Karcher NR, Hershey T, Hatoum AS, Agrawal A, Bogdan R. A Phenome-Wide Association Study (PheWAS) of Genetic Risk for C-Reactive Protein in Children of European Ancestry: Results From the ABCD Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.30.24312857. [PMID: 39252928 PMCID: PMC11383484 DOI: 10.1101/2024.08.30.24312857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
BACKGROUND C-reactive protein (CRP) is a moderately heritable marker of systemic inflammation that is associated with adverse physical and mental health outcomes. Identifying factors associated with genetic liability to elevated CRP in childhood may inform our understanding of variability in CRP that could be targeted to prevent and/or delay the onset of related health outcomes. METHODS We conducted a phenome-wide association study (PheWAS) of genetic risk for elevated CRP (i.e. CRP polygenic risk score [PRS]) among children genetically similar to European ancestry reference populations (median analytic n = 5,509) from the Adolescent Brain and Cognitive Development℠ (ABCD) Study. Associations between CRP PRS and 2,377 psychosocial and neuroimaging phenotypes were estimated using independent mixed effects models. Post hoc analyses examined whether: (1) covarying for measured body mass index (BMI) or removing the shared genetic architecture between CRP and BMI altered phenotypic associations, (2) sex moderated CRP PRS associations, and (3) associations are unconfounded by assortative mating or passive gene-environment correlations (using a within-family analyses). Multiple testing was adjusted for using Bonferroni and false discovery rate (FDR) correction. RESULTS Nine phenotypes were positively associated with CRP PRS after multiple testing correction: five weight- and eating-related phenotypes (e.g. BMI, overeating), three phenotypes related to caregiver somatic problems (e.g. caregiver somatic complaints), as well as weekday video watching (all ps = 1.2 × 10-7 - 2.5 × 10-4, all p FDR s = 0.0002 - 0.05). No neuroimaging phenotypes were associated with CRP PRS (all ps = 0.0003 - 0.998; all p FDR s = 0.08 - 0.998) after correction for multiple testing. Eating and weight-related phenotypes remained associated with CRP PRS in within-family analyses. Covarying for BMI resulted in largely consistent results, and sex did not moderate any CRP PRS associations. Removing the shared genetic variance between CRP and BMI attenuated all relationships; associations with weekday video watching, caregiver somatic problems and caregiver report that the child is overweight remained significant while associations with waist circumference, weight, and caregiver report that child overeats did not. DISCUSSION Genetic liability to elevated CRP is associated with higher weight, eating, and weekday video watching during childhood as well as caregiver somatic problems. These associations were consistent with direct genetic effects (i.e., not solely due to confounding factors like passive gene-environment correlations) and were independent of measured BMI. The majority of associations with weight and eating phenotypes were attributable to shared genetic architecture between BMI and inflammation. The relationship between genetics and heightened inflammation in later life may be partially attributable to modifiable behaviors (e.g. weight and activity levels) that are expressed as early as childhood.
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Affiliation(s)
- Sara A Norton
- Washington University in St. Louis, Department of Psychological & Brain Sciences
| | - Aaron J Gorelik
- Washington University in St. Louis, Department of Psychological & Brain Sciences
| | - Sarah E Paul
- Washington University in St. Louis, Department of Psychological & Brain Sciences
| | - Emma C Johnson
- Washington University School of Medicine in St. Louis, Department of Psychiatry
| | - David Aa Baranger
- Washington University in St. Louis, Department of Psychological & Brain Sciences
| | - Jayne L Siudzinski
- Washington University in St. Louis, Department of Psychological & Brain Sciences
| | - Zhaolong Adrian Li
- Washington University School of Medicine in St. Louis, Department of Psychiatry
| | - Erin Bondy
- University of North Carolina School of Medicine, Department of Psychiatry
| | - Hailey Modi
- Washington University School of Medicine in St. Louis, Division of Biological and Biomedical Sciences
| | - Nicole R Karcher
- Washington University School of Medicine in St. Louis, Department of Psychiatry
| | - Tamara Hershey
- Washington University School of Medicine in St. Louis, Department of Psychiatry
- Washington University School of Medicine, Department of Radiology
| | - Alexander S Hatoum
- Washington University School of Medicine in St. Louis, Department of Psychiatry
| | - Arpana Agrawal
- Washington University School of Medicine in St. Louis, Department of Psychiatry
| | - Ryan Bogdan
- Washington University in St. Louis, Department of Psychological & Brain Sciences
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Farahani FV, Nebel MB, Wager TD, Lindquist MA. Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.27.609817. [PMID: 39253413 PMCID: PMC11383013 DOI: 10.1101/2024.08.27.609817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)-an analytic approach for handling functional misalignment-can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.
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Affiliation(s)
- Farzad V Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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Owen LLW, Manning JR. High-level cognition is supported by information-rich but compressible brain activity patterns. Proc Natl Acad Sci U S A 2024; 121:e2400082121. [PMID: 39178232 PMCID: PMC11363287 DOI: 10.1073/pnas.2400082121] [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: 01/03/2024] [Accepted: 07/08/2024] [Indexed: 08/25/2024] Open
Abstract
To efficiently yet reliably represent and process information, our brains need to produce information-rich signals that differentiate between moments or cognitive states, while also being robust to noise or corruption. For many, though not all, natural systems, these two properties are often inversely related: More information-rich signals are less robust, and vice versa. Here, we examined how these properties change with ongoing cognitive demands. To this end, we applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. We considered two primary aspects of the neural data recorded in these different experimental conditions. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower engagement.
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Affiliation(s)
- Lucy L. W. Owen
- Department of Psychiatry and Human Behavior, Carney Institute for Brain Sciences, Brown University, Providence, RI02906
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH03755
- Department of Computer Science, University of Montana, Missoula, MT59812
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH03755
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Yang D, Svoboda AM, George TG, Mansfield PK, Wheelock MD, Schroeder ML, Rafferty SM, Sherafati A, Tripathy K, Burns-Yocum T, Forsen E, Pruett JR, Marrus NM, Culver JP, Constantino JN, Eggebrecht AT. Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography. Mol Autism 2024; 15:35. [PMID: 39175054 PMCID: PMC11342641 DOI: 10.1186/s13229-024-00614-4] [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: 05/24/2024] [Accepted: 07/31/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits. METHODS We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models. RESULTS We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits. LIMITATIONS Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism. CONCLUSIONS This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.
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Affiliation(s)
- Dalin Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Alexandra M Svoboda
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Tessa G George
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Patricia K Mansfield
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Medical Education, Saint Louis University School of Medicine, St. Louis, MO, 63104, USA
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO, 63130, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Mariel L Schroeder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Speech, Language, and Hearing Science, Purdue University, West Lafayette, IL, 47907, USA
| | - Sean M Rafferty
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Arefeh Sherafati
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO, 63130, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Kalyan Tripathy
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA
- University of Pittsburgh Medical Center, Western Psychiatric Hospital, Pittsburgh, PA, 15213, USA
| | - Tracy Burns-Yocum
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Evolytics, Parkville, MO, 64152, USA
| | - Elizabeth Forsen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Doctor of Medicine Program, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - John R Pruett
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Natasha M Marrus
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Joseph P Culver
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO, 63130, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO, 63130, USA
- Department of Electrical and System Engineering, Washington University School of Engineering, St. Louis, MO, 63112, USA
- Department Imaging Sciences Engineering, Washington University School of Engineering, St. Louis, MO, 63112, USA
| | - John N Constantino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Division of Behavioral and Mental Health, Children's Healthcare of Atlanta, Atlanta, GA, 30329, USA
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA.
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO, 63130, USA.
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO, 63130, USA.
- Department of Electrical and System Engineering, Washington University School of Engineering, St. Louis, MO, 63112, USA.
- Department Imaging Sciences Engineering, Washington University School of Engineering, St. Louis, MO, 63112, USA.
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Royer J, Kebets V, Piguet C, Chen J, Ooi LQR, Kirschner M, Siffredi V, Misic B, Yeo BTT, Bernhardt BC. MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.02.530821. [PMID: 39185226 PMCID: PMC11343159 DOI: 10.1101/2023.03.02.530821] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.
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Affiliation(s)
- Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Camille Piguet
- Young Adult Unit, Psychiatric Specialities Division, Geneva University Hospitals and Department of Psychiatry, Faculty of Medicine, University of Geneva, Switzerland
- Adolescent Unit, Division of General Paediatric, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals
| | - Jianzhong Chen
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Matthias Kirschner
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Vanessa Siffredi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme, National University Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Menu I, Ji L, Bhatia T, Duffy M, Hendrix CL, Thomason ME. Beyond average outcomes: A latent profile analysis of diverse developmental trajectories in preterm and early term-born children from the Adolescent Brain Cognitive Development study. Child Dev 2024. [PMID: 39136075 DOI: 10.1111/cdev.14143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2024]
Abstract
Preterm birth poses a major public health challenge, with significant and heterogeneous developmental impacts. Latent profile analysis was applied to the National Institutes of Health Toolbox performance of 1891 healthy prematurely born children from the Adolescent Brain and Cognitive Development study (970 boys, 921 girls; 10.00 ± 0.61 years; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 57.0% White, 10.4% Other). Three distinct neurocognitive profiles emerged: consistently performing above the norm (19.7%), mixed scores (41.0%), and consistently performing below the norm (39.3%). These profiles were associated with lasting cognitive, neural, behavioral, and academic differences. These findings underscore the importance of recognizing diverse developmental trajectories in prematurely born children, advocating for personalized diagnosis and intervention to enhance care strategies and long-term outcomes for this heterogeneous population.
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Affiliation(s)
- Iris Menu
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
| | - Lanxin Ji
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
| | - Tanya Bhatia
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
| | - Mark Duffy
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
| | - Cassandra L Hendrix
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
| | - Moriah E Thomason
- Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, New York, USA
- Department of Population Health, NYU Langone Health, New York, New York, USA
- Neuroscience Institute, NYU Langone Health, New York, New York, USA
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Karnadipa T, Chong B, Shim V, Fernandez J, Lin DJ, Wang A. Mapping stroke outcomes: A review of brain connectivity atlases. J Neuroimaging 2024. [PMID: 39133035 DOI: 10.1111/jon.13228] [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: 07/08/2024] [Revised: 07/25/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024] Open
Abstract
The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.
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Affiliation(s)
- Triana Karnadipa
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Medicine, The University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - David J Lin
- Centre for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
- Centre for Co-Created Ageing Research, The University of Auckland, Auckland, New Zealand
- Medical Imaging Research Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
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Derman D, Pham DD, Mejia AF, Ferradal SL. Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.24.550218. [PMID: 39149306 PMCID: PMC11326129 DOI: 10.1101/2023.07.24.550218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.
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Affiliation(s)
- Diego Derman
- Department of Intelligent Systems Engineering, Indiana University, USA
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Rutherford S, Lasagna CA, Blain SD, Marquand AF, Wolfers T, Tso IF. Social Cognition and Functional Connectivity in Early and Chronic Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00212-X. [PMID: 39117275 DOI: 10.1016/j.bpsc.2024.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Individuals with schizophrenia (SZ) experience impairments in social cognition that contribute to poor functional outcomes. However, mechanisms of social cognitive dysfunction in SZ remain poorly understood, which impedes the design of novel interventions to improve outcomes. This pre-registered project (https://doi.org/10.17605/OSF.IO/JH5FC) examines the representation of social cognition in the brain's functional architecture across early and chronic SZ. METHODS The study contains two parts: a confirmatory and an exploratory portion. In the confirmatory portion, we identified resting-state connectivity disruptions evident in early and chronic SZ. We performed a connectivity analysis using regions associated with social cognitive dysfunction in early and chronic SZ to test whether aberrant connectivity observed in chronic SZ (N=47; HC=52) was also present in early SZ (N=71, HC=47). In the exploratory portion, we assessed the out-of-sample generalizability and precision of predictive models of social cognition. We used machine learning to predict social cognition and established generalizability with out-of-sample testing and confound control. RESULTS Results reveal decreases between left inferior frontal gyrus and intraparietal sulcus in early and chronic SZ, which are significantly associated with social and general cognition and global functioning in chronic SZ and with general cognition and global functioning in early SZ. Predictive modeling reveals the importance of out-of-sample evaluation and confound control. CONCLUSION This work provides insights into the functional architecture in early and chronic SZ and suggests that IFG-IPS connectivity could be a prognostic biomarker of social impairments and a target for future interventions (e.g. neuromodulation) focused on improved social functioning.
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Affiliation(s)
- Saige Rutherford
- Department of Cognitive Neuroscience, RadboudUMC, Nijmegen, the Netherlands; Donders Institute for Cognition, Brain, Behavior, Nijmegen, the Netherlands; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States.
| | - Carly A Lasagna
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, United States
| | - Scott D Blain
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States; Department of Psychiatry, the Ohio State University, Columbus, Ohio, United States
| | - Andre F Marquand
- Department of Cognitive Neuroscience, RadboudUMC, Nijmegen, the Netherlands; Donders Institute for Cognition, Brain, Behavior, Nijmegen, the Netherlands
| | - Thomas Wolfers
- Department of Psychiatry, University of Tübingen, Tübingen, Germany; German Centre for Mental Health, University of Tübingen, Germany
| | - Ivy F Tso
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States; Department of Psychiatry, the Ohio State University, Columbus, Ohio, United States
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Jirsaraie RJ, Gatavins MM, Pines AR, Kandala S, Bijsterbosch JD, Marek S, Bogdan R, Barch DM, Sotiras A. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry 2024:10.1038/s41380-024-02682-7. [PMID: 39107582 DOI: 10.1038/s41380-024-02682-7] [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: 11/13/2023] [Revised: 07/13/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.
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Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M Gatavins
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam R Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sridhar Kandala
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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40
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Ruffle JK, Watkins H, Gray RJ, Hyare H, Thiebaut de Schotten M, Nachev P. Compressed representation of brain genetic transcription. Hum Brain Mapp 2024; 45:e26795. [PMID: 39045881 PMCID: PMC11267301 DOI: 10.1002/hbm.26795] [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: 02/01/2024] [Revised: 06/17/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.
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Affiliation(s)
- James K. Ruffle
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Henry Watkins
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Robert J. Gray
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College LondonLondonUK
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives‐UMR 5293, CNRS, CEA, University of BordeauxBordeauxFrance
- Brain Connectivity and Behaviour LaboratoryParisFrance
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College LondonLondonUK
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41
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Zhang H, Cao P, Mak HKF, Hui ES. The structural-functional-connectivity coupling of the aging brain. GeroScience 2024; 46:3875-3887. [PMID: 38443539 PMCID: PMC11226573 DOI: 10.1007/s11357-024-01106-2] [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: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024] Open
Abstract
Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.
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Affiliation(s)
- Hui Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Henry K F Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
- Alzheimer's Disease Research Network, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.
- CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong, Hong Kong, China.
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Siegel JS, Subramanian S, Perry D, Kay BP, Gordon EM, Laumann TO, Reneau TR, Metcalf NV, Chacko RV, Gratton C, Horan C, Krimmel SR, Shimony JS, Schweiger JA, Wong DF, Bender DA, Scheidter KM, Whiting FI, Padawer-Curry JA, Shinohara RT, Chen Y, Moser J, Yacoub E, Nelson SM, Vizioli L, Fair DA, Lenze EJ, Carhart-Harris R, Raison CL, Raichle ME, Snyder AZ, Nicol GE, Dosenbach NUF. Psilocybin desynchronizes the human brain. Nature 2024; 632:131-138. [PMID: 39020167 PMCID: PMC11291293 DOI: 10.1038/s41586-024-07624-5] [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: 10/24/2023] [Accepted: 05/29/2024] [Indexed: 07/19/2024]
Abstract
A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.
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Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Subha Subramanian
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Demetrius Perry
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - T Rick Reneau
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Ravi V Chacko
- Department of Emergency Medicine, Advocate Christ Health Care, Oak Lawn, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | | | - Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Julie A Schweiger
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - David A Bender
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Forrest I Whiting
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Jonah A Padawer-Curry
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Robin Carhart-Harris
- Department of Neurology, University of California, San Francisco, CA, USA
- Centre for Psychedelic Research, Imperial College London, London, UK
| | - Charles L Raison
- Usona Institute, Fitchburg, WI, USA
- Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Marcus E Raichle
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Ginger E Nicol
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
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43
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Wang X, Krieger-Redwood K, Cui Y, Smallwood J, Du Y, Jefferies E. Macroscale brain states support the control of semantic cognition. Commun Biol 2024; 7:926. [PMID: 39090387 PMCID: PMC11294576 DOI: 10.1038/s42003-024-06630-7] [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: 03/29/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty - global association and feature matching judgments - which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain's dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
| | | | - Yanni Cui
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | | | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
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Ling Q, Liu A, Li Y, McKeown MJ, Chen X. fMRI-based spatio-temporal parcellations of the human brain. Curr Opin Neurol 2024; 37:369-380. [PMID: 38804205 DOI: 10.1097/wco.0000000000001280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
PURPOSE OF REVIEW Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. RECENT FINDINGS Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.
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Affiliation(s)
- Qinrui Ling
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Aiping Liu
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Yu Li
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Martin J McKeown
- Department of Medicine, University of British Columbia, Vancouver, Vancouver V6T2B5, Canada
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
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45
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Zundel CG, Ely S, Brokamp C, Strawn JR, Jovanovic T, Ryan P, Marusak HA. Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence. Brain Connect 2024; 14:307-318. [PMID: 38814823 PMCID: PMC11387001 DOI: 10.1089/brain.2023.0072] [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: 06/01/2024] Open
Abstract
Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.
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Affiliation(s)
- Clara G Zundel
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | - Samantha Ely
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA
| | - Cole Brokamp
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jeffrey R Strawn
- Anxiety Disorders Research Program, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan, USA
| | - Patrick Ryan
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan, USA
- Department of Pharmacology, Wayne State University, Detroit, Michigan, USA
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46
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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47
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Alavash M, Obleser J. Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior. J Neurosci 2024; 44:e2322232024. [PMID: 38839303 PMCID: PMC11293451 DOI: 10.1523/jneurosci.2322-23.2024] [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: 12/11/2023] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 06/07/2024] Open
Abstract
Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.
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Affiliation(s)
- Mohsen Alavash
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, Lübeck 23562, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, Lübeck 23562, Germany
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48
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Beck D, Whitmore L, MacSweeney N, Brieant A, Karl V, de Lange AMG, Westlye LT, Mills KL, Tamnes CK. Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation. Biol Psychiatry 2024:S0006-3223(24)01486-0. [PMID: 39084501 DOI: 10.1016/j.biopsych.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 07/16/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND Different types of early-life adversity (ELA) have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge. METHODS This study investigates the neural correlates of 10 robust dimensions of ELA identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development Study. Brain age models were trained, validated, and tested separately on T1-weighted (n = 9524), diffusion tensor (n = 8834), and resting-state functional (n = 8233) magnetic resonance imaging data from two time points (mean age = 10.7 years, SD = 1.2, age range = 8.9-13.8 years). RESULTS Bayesian multilevel modeling supported distinct associations between different types of ELA exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support and lack of caregiver supervision, were associated with lower brain age gaps, i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socioeconomic disadvantage and neighborhood safety were associated with higher brain age gaps, i.e., older-looking brains. CONCLUSIONS The findings suggest that dimensions of ELA are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.
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Affiliation(s)
- Dani Beck
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lucy Whitmore
- Department of Psychology, University of Oregon, Eugene, Oregon
| | - Niamh MacSweeney
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
| | - Alexis Brieant
- Department of Psychological Science, University of Vermont, Burlington, Vermont
| | - Valerie Karl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, Oregon
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
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49
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Dhamala E, Bassett DS, Yeo T, Holmes AJ. Functional brain networks are associated with both sex and gender in children. SCIENCE ADVANCES 2024; 10:eadn4202. [PMID: 38996031 PMCID: PMC11244548 DOI: 10.1126/sciadv.adn4202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 06/12/2024] [Indexed: 07/14/2024]
Abstract
Sex and gender are associated with human behavior throughout the life span and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are uniquely reflected in the intrinsic functional connectivity of the brain. Somatomotor, visual, control, and limbic networks are preferentially associated with sex, while network correlates of gender are more distributed throughout the cortex. These results suggest that sex and gender are irreducible to one another not only in society but also in biology.
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Affiliation(s)
- Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, USA
| | - Dani S. Bassett
- University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Thomas Yeo
- Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Avram J. Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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50
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Cotter DL, Morrel J, Sukumaran K, Cardenas-Iniguez C, Schwartz J, Herting MM. Prenatal and childhood air pollution exposure, cellular immune biomarkers, and brain connectivity in early adolescents. Brain Behav Immun Health 2024; 38:100799. [PMID: 39021436 PMCID: PMC11252082 DOI: 10.1016/j.bbih.2024.100799] [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: 11/28/2023] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®. Methods Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately. Results Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox. Conclusions We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.
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Affiliation(s)
- Devyn L. Cotter
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica Morrel
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kirthana Sukumaran
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Megan M. Herting
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Children's Hospital Los Angeles, Los Angeles, CA, USA
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