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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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2
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Schintu S, Gotts SJ, Freedberg M, Shomstein S, Wassermann EM. Effective connectivity underlying neural and behavioral components of prism adaptation. Front Psychol 2022; 13:915260. [PMID: 36118425 PMCID: PMC9479732 DOI: 10.3389/fpsyg.2022.915260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/04/2022] [Indexed: 11/13/2022] Open
Abstract
Prism adaptation (PA) is a form of visuomotor training that produces both sensorimotor and cognitive aftereffects depending on the direction of the visual displacement. Recently, a neural framework explaining both types of PA-induced aftereffects has been proposed, but direct evidence for it is lacking. We employed Structural Equation Modeling (SEM), a form of effective connectivity analysis, to establish directionality among connected nodes of the brain network thought to subserve PA. The findings reveal two distinct network branches: (1) a loop involving connections from the parietal cortices to the right parahippocampal gyrus, and (2) a branch linking the lateral premotor cortex to the parahippocampal gyrus via the cerebellum. Like the sensorimotor aftereffects, the first branch exhibited qualitatively different modulations for left versus right PA, and critically, changes in these connections were correlated with the magnitude of the sensorimotor aftereffects. Like the cognitive aftereffects, changes in the second branch were qualitatively similar for left and right PA, with greater change for left PA and a trend correlation with cognitive aftereffects. These results provide direct evidence that PA is supported by two functionally distinct subnetworks, a parietal–temporal network responsible for sensorimotor aftereffects and a fronto-cerebellar network responsible for cognitive aftereffects.
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Affiliation(s)
- Selene Schintu
- Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
- Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, United States
- Center for Mind/Brain Sciences-CIMeC, University of Trento, Rovereto, Trentino, Italy
- *Correspondence: Selene Schintu,
| | - Stephen J. Gotts
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
| | - Michael Freedberg
- Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX, United States
| | - Sarah Shomstein
- Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, United States
| | - Eric M. Wassermann
- Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
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3
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Gotts SJ, Milleville SC, Martin A. Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming. Commun Biol 2021; 4:487. [PMID: 33879819 PMCID: PMC8058068 DOI: 10.1038/s42003-021-02002-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 03/18/2021] [Indexed: 11/15/2022] Open
Abstract
Stimulus identification commonly improves with repetition over long delays ("repetition priming"), whereas neural activity commonly decreases ("repetition suppression"). Multiple models have been proposed to explain this brain-behavior relationship, predicting alterations in functional and/or effective connectivity (Synchrony and Predictive Coding models), in the latency of neural responses (Facilitation model), and in the relative similarity of neural representations (Sharpening model). Here, we test these predictions with fMRI during overt and covert naming of repeated and novel objects. While we find partial support for predictions of the Facilitation and Sharpening models in the left fusiform gyrus and left frontal cortex, the data were most consistent with the Synchrony model, with increased coupling between right temporoparietal and anterior cingulate cortex for repeated objects that correlated with priming magnitude across participants. Increased coupling and repetition suppression varied independently, each explaining unique variance in priming and requiring modifications of all current models.
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Affiliation(s)
- Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Shawn C Milleville
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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4
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Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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5
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Havlicek M, Ivanov D, Roebroeck A, Uludağ K. Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model. Front Neurosci 2017; 11:616. [PMID: 29249925 PMCID: PMC5715391 DOI: 10.3389/fnins.2017.00616] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 10/23/2017] [Indexed: 12/12/2022] Open
Abstract
Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative hemodynamic model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated the generative model's ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of the generative model to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV, and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting the generative model to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, the generative model that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.
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Affiliation(s)
- Martin Havlicek
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Kamil Uludağ
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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6
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Scheuer H, Alarcón G, Demeter DV, Earl E, Fair DA, Nagel BJ. Reduced fronto-amygdalar connectivity in adolescence is associated with increased depression symptoms over time. Psychiatry Res 2017; 266:35-41. [PMID: 28577433 PMCID: PMC5583022 DOI: 10.1016/j.pscychresns.2017.05.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 05/23/2017] [Accepted: 05/23/2017] [Indexed: 12/25/2022]
Abstract
Depression is common among adolescents, affecting greater than 12% of youth in a given year. Studies have shown aberrant amygdala connectivity in depressed adolescents, compared with controls; however, no studies have examined whether these abnormalities precede and heighten risk for depressive symptom expression. This study used resting state functional connectivity (RSFC) magnetic resonance imaging to examine neurobiological markers of escalating depression symptoms in adolescents (ages 12-16 years; free from psychopathology at baseline). Of a large sample of adolescents, 18 showed ≥ 1 S.D. increase in depression scale t-scores over time ("escalators"; time to escalation ranging from 6 to 54 months in follow up) and were matched and compared to 19 youth showing stable CDI scores over time ("controls"). Whole-brain analyses on baseline RSFC data using an amygdala seed region-of-interest (ROI) showed that controls had greater RSFC, relative to escalators, between the right amygdala and left inferior frontal and supramarginal gyrus and right mid-cingulate cortex. Additionally, relative to escalators, control youth had less RSFC between the left amygdala and cerebellum. Findings suggest a possible neurobiological marker of increasing depressive symptoms during adolescence, characterized in part by reduced fronto-limbic connectivity, suggesting a premorbid deficiency in top-down emotional regulation.
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Affiliation(s)
- Hannah Scheuer
- Department of Psychiatry at Oregon Health & Science University, Portland, OR, USA
| | - Gabriela Alarcón
- Department of Behavioral Neuroscience at Oregon Health & Science University, Portland, OR, USA
| | - Damion V Demeter
- Department of Behavioral Neuroscience at Oregon Health & Science University, Portland, OR, USA
| | - Eric Earl
- Department of Behavioral Neuroscience at Oregon Health & Science University, Portland, OR, USA
| | - Damien A Fair
- Department of Psychiatry at Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience at Oregon Health & Science University, Portland, OR, USA
| | - Bonnie J Nagel
- Department of Psychiatry at Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience at Oregon Health & Science University, Portland, OR, USA.
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7
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Jakab A, Pogledic I, Schwartz E, Gruber G, Mitter C, Brugger PC, Langs G, Schöpf V, Kasprian G, Prayer D. Fetal Cerebral Magnetic Resonance Imaging Beyond Morphology. Semin Ultrasound CT MR 2015; 36:465-75. [DOI: 10.1053/j.sult.2015.06.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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8
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Molenaar PCM, Beltz AM, Gates KM, Wilson SJ. State space modeling of time-varying contemporaneous and lagged relations in connectivity maps. Neuroimage 2015; 125:791-802. [PMID: 26546863 DOI: 10.1016/j.neuroimage.2015.10.088] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 10/27/2015] [Accepted: 10/31/2015] [Indexed: 01/07/2023] Open
Abstract
Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample.
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Affiliation(s)
- Peter C M Molenaar
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA; Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Adriene M Beltz
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
| | - Kathleen M Gates
- Department of Psychology, University of North Carolina, Chapel Hill, NC 27559, USA
| | - Stephen J Wilson
- Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA
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9
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Alarcón G, Cservenka A, Rudolph MD, Fair DA, Nagel BJ. Developmental sex differences in resting state functional connectivity of amygdala sub-regions. Neuroimage 2015; 115:235-44. [PMID: 25887261 DOI: 10.1016/j.neuroimage.2015.04.013] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 03/10/2015] [Accepted: 04/07/2015] [Indexed: 10/23/2022] Open
Abstract
During adolescence, considerable social and biological changes occur that interact with functional brain maturation, some of which are sex-specific. The amygdala is one brain area that has displayed sexual dimorphism, specifically in socio-affective (superficial amygdala [SFA]), stress (centromedial amygdala [CMA]), and learning and memory (basolateral amygdala [BLA]) processing. The amygdala has also been implicated in mood and anxiety disorders which display sex-specific features, most prominently observed during adolescence. Using functional magnetic resonance imaging (fMRI), the present study examined the interaction of age and sex on resting state functional connectivity (RSFC) of amygdala sub-regions, BLA and SFA, in a sample of healthy adolescents between the ages 10 and 16 years (n = 122, 71 boys). Whole-brain, voxel-wise partial correlation analyses were conducted to determine RSFC of bilateral BLA and SFA seed regions, created using the Eickhoff-Zilles maximum probability maps based on cytoarchitectonic mapping and FMRIB's Integrated Registration and Segmentation Tool (FIRST). Monte Carlo simulation was implemented to correct for multiple comparisons (threshold of 53 contiguous voxels with a z-value ≥ 2.25). Results indicated that with increasing age, there was a corresponding decrease in RSFC between both amygdala sub-regions and parieto-occipital cortices, with a concurrent increase in RSFC with medial prefrontal cortex (mPFC). Specifically, boys and girls demonstrated increased coupling of mPFC and left and right SFA with age, respectively; however, neither sex showed increased connectivity between mPFC and BLA, which could indicate relative immaturity of fronto-limbic networks that is similar across sex. A dissociation in connectivity between BLA- and SFA-parieto-occipital RSFC emerged, in which girls had weaker negative RSFC between SFA and parieto-occipital regions and boys had weaker negative RSFC of BLA and parieto-occipital regions with increased age, both standing in contrast to adult patterns of amygdala sub-regional RSFC. The present findings suggest relative immaturity of amygdala sub-regional RSFC with parieto-occipital cortices during adolescence, with unique patterns in both sexes that may support memory and socio-affective processing in boys and girls, respectively. Understanding the underlying normative functional architecture of brain networks associated with the amygdala during adolescence may better inform future research of the neural features associated with increased risk for internalizing psychopathology.
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Affiliation(s)
- Gabriela Alarcón
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Anita Cservenka
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - Marc D Rudolph
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Bonnie J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA.
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10
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Entz L, Tóth E, Keller CJ, Bickel S, Groppe DM, Fabó D, Kozák LR, Erőss L, Ulbert I, Mehta AD. Evoked effective connectivity of the human neocortex. Hum Brain Mapp 2014; 35:5736-53. [PMID: 25044884 DOI: 10.1002/hbm.22581] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2014] [Revised: 06/04/2014] [Accepted: 06/27/2014] [Indexed: 10/25/2022] Open
Abstract
The role of cortical connectivity in brain function and pathology is increasingly being recognized. While in vivo magnetic resonance imaging studies have provided important insights into anatomical and functional connectivity, these methodologies are limited in their ability to detect electrophysiological activity and the causal relationships that underlie effective connectivity. Here, we describe results of cortico-cortical evoked potential (CCEP) mapping using single pulse electrical stimulation in 25 patients undergoing seizure monitoring with subdural electrode arrays. Mapping was performed by stimulating adjacent electrode pairs and recording CCEPs from the remainder of the electrode array. CCEPs reliably revealed functional networks and showed an inverse relationship to distance between sites. Coregistration to Brodmann areas (BA) permitted group analysis. Connections were frequently directional with 43% of early responses and 50% of late responses of connections reflecting relative dominance of incoming or outgoing connections. The most consistent connections were seen as outgoing from motor cortex, BA6-BA9, somatosensory (SS) cortex, anterior cingulate cortex, and Broca's area. Network topology revealed motor, SS, and premotor cortices along with BA9 and BA10 and language areas to serve as hubs for cortical connections. BA20 and BA39 demonstrated the most consistent dominance of outdegree connections, while BA5, BA7, auditory cortex, and anterior cingulum demonstrated relatively greater indegree. This multicenter, large-scale, directional study of local and long-range cortical connectivity using direct recordings from awake, humans will aid the interpretation of noninvasive functional connectome studies.
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Affiliation(s)
- László Entz
- Department of Neurosurgery, Hofstra North Shore LIJ School of Medicine and Feinstein Institute of Medical Research, Manhasset, New York, 11030; Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, 1132, Hungary; Department of Functional Neurosurgery and Department of Epilepsy, National Institute of Clinical Neuroscience, Budapest, 1145, Hungary; Péter Pázmány Catholic University, Faculty of Information Technology and Bionics, Budapest, 1083, Hungary
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11
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Taylor PA, Saad ZS. FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox. Brain Connect 2014; 3:523-35. [PMID: 23980912 DOI: 10.1089/brain.2013.0154] [Citation(s) in RCA: 155] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.
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Affiliation(s)
- Paul A Taylor
- 1 African Institute for Mathematical Sciences , Muizenberg, Western Cape, South Africa
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12
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Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem. J Neurosci 2014; 33:19212-22. [PMID: 24305817 DOI: 10.1523/jneurosci.2649-13.2013] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Cortical and subcortical networks have been identified that are commonly associated with attention and task engagement, along with theories regarding their functional interaction. However, a link between these systems has not yet been demonstrated in healthy humans, primarily because of data acquisition and analysis limitations. We recorded simultaneous EEG-fMRI while subjects performed auditory and visual oddball tasks and used these data to investigate the BOLD correlates of single-trial EEG variability at latencies spanning the trial. We focused on variability along task-relevant dimensions in the EEG for identical stimuli and then combined auditory and visual data at the subject level to spatially and temporally localize brain regions involved in endogenous attentional modulations. Specifically, we found that anterior cingulate cortex (ACC) correlates strongly with both early and late EEG components, whereas brainstem, right middle frontal gyrus (rMFG), and right orbitofrontal cortex (rOFC) correlate significantly only with late components. By orthogonalizing with respect to event-related activity, we found that variability in insula and temporoparietal junction is reflected in reaction time variability, rOFC and brainstem correlate with residual EEG variability, and ACC and rMFG are significantly correlated with both. To investigate interactions between these correlates of temporally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data. We found strong evidence for reciprocal effective connections between the brainstem and cortical regions. Our results support the adaptive gain theory of locus ceruleus-norepinephrine (LC-NE) function and the proposed functional relationship between the LC-NE system, right-hemisphere ventral attention network, and P300 EEG response.
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Smith JF, Braun AR, Alexander GE, Chen K, Horwitz B. Separating lexical-semantic access from other mnemonic processes in picture-name verification. Front Psychol 2013; 4:706. [PMID: 24130539 PMCID: PMC3795327 DOI: 10.3389/fpsyg.2013.00706] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 09/16/2013] [Indexed: 11/13/2022] Open
Abstract
We present a novel paradigm to identify shared and unique brain regions underlying non-semantic, non-phonological, abstract, audio-visual (AV) memory vs. naming using a longitudinal functional magnetic resonance imaging experiment. Participants were trained to associate novel AV stimulus pairs containing hidden linguistic content. Half of the stimulus pairs were distorted images of animals and sine-wave speech versions of the animal's name. Images and sounds were distorted in such a way as to make their linguistic content easily recognizable only after being made aware of its existence. Memory for the pairings was tested by presenting an AV pair and asking participants to verify if the two stimuli formed a learned pairing. After memory testing, the hidden linguistic content was revealed and participants were tested again on their recollection of the pairings in this linguistically informed state. Once informed, the AV verification task could be performed by naming the picture. There was substantial overlap between the regions involved in recognition of non-linguistic sensory memory and naming, suggesting a strong relation between them. Contrasts between sessions identified left angular gyrus and middle temporal gyrus as key additional players in the naming network. Left inferior frontal regions participated in both naming and non-linguistic AV memory suggesting the region is responsible for AV memory independent of phonological content contrary to previous proposals. Functional connectivity between angular gyrus and left inferior frontal gyrus and left middle temporal gyrus increased when performing the AV task as naming. The results are consistent with the hypothesis that, at the spatial resolution of fMRI, the regions that facilitate non-linguistic AV associations are a subset of those that facilitate naming though reorganized into distinct networks.
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Affiliation(s)
- Jason F Smith
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health Bethesda, MD, USA
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Bailey T. Beyond DSM: the role of auditory processing in attention and its disorders. APPLIED NEUROPSYCHOLOGY-CHILD 2013; 1:112-20. [PMID: 23428298 DOI: 10.1080/21622965.2012.703890] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
This article reviews and synthesizes recent research regarding auditory processing, attention, and their roles in generating both adaptive and maladaptive behavioral responses. Research in these areas is beginning to converge on the role of polymorphisms associated with catecholamine metabolism and transport, particularly the neurotransmitter dopamine. The synthesis offered in this article appears to be the first to argue that genetic differences in dopamine metabolism may be the common factor in four disparate disorders that are often observed to be comorbid, i.e., attention-deficit hyperactivity disorder, auditory processing disorders, developmental language disorders, and reading disorders.
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Affiliation(s)
- Teresa Bailey
- Department of Research, Athena Academy, Palo Alto, CA, USA.
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Smith JF, Chen K, Pillai AS, Horwitz B. Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models. Front Neurosci 2013; 7:70. [PMID: 23717258 PMCID: PMC3653105 DOI: 10.3389/fnins.2013.00070] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 04/16/2013] [Indexed: 11/13/2022] Open
Abstract
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define "effective connectivity" using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.
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Affiliation(s)
- Jason F. Smith
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of HealthBethesda, MD, USA
| | - Kewei Chen
- Positron Emission Tomography Center and Banner Alzheimer's Disease Institute, Banner Good Samaritan Medical CenterPhoenix, AZ, USA
- Department of Mathematics and Statistics, Arizona State UniversityTempe, AZ, USA
- Arizona Alzheimer's Disease ConsortiumPhoenix, AZ, USA
| | - Ajay S. Pillai
- Human Motor Control Section, National Institute on Neurological Disorders and Stroke, National Institutes of HealthBethesda, MD, USA
| | - Barry Horwitz
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of HealthBethesda, MD, USA
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Efficient posterior probability mapping using Savage-Dickey ratios. PLoS One 2013; 8:e59655. [PMID: 23533640 PMCID: PMC3606143 DOI: 10.1371/journal.pone.0059655] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 02/19/2013] [Indexed: 12/02/2022] Open
Abstract
Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a Bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a Bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner.
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Turner R, Lohmann G. New concepts in brain networks. Front Syst Neurosci 2012; 6:56. [PMID: 22907995 PMCID: PMC3415675 DOI: 10.3389/fnsys.2012.00056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 07/18/2012] [Indexed: 11/21/2022] Open
Affiliation(s)
- Robert Turner
- Department of Neurophysics, Max-Planck-Institute for Human Cognitive and Brain Sciences Leipzig, Germany
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Hagmann P, Grant PE, Fair DA. MR connectomics: a conceptual framework for studying the developing brain. Front Syst Neurosci 2012; 6:43. [PMID: 22707934 PMCID: PMC3374479 DOI: 10.3389/fnsys.2012.00043] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2011] [Accepted: 05/08/2012] [Indexed: 12/25/2022] Open
Abstract
THE COMBINATION OF ADVANCED NEUROIMAGING TECHNIQUES AND MAJOR DEVELOPMENTS IN COMPLEX NETWORK SCIENCE, HAVE GIVEN BIRTH TO A NEW FRAMEWORK FOR STUDYING THE BRAIN: "connectomics." This framework provides the ability to describe and study the brain as a dynamic network and to explore how the coordination and integration of information processing may occur. In recent years this framework has been used to investigate the developing brain and has shed light on many dynamic changes occurring from infancy through adulthood. The aim of this article is to review this work and to discuss what we have learned from it. We will also use this body of work to highlight key technical aspects that are necessary in general for successful connectome analysis using today's advanced neuroimaging techniques. We look to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research.
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Affiliation(s)
- Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)Lausanne, Switzerland
- Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne (EPFL)Lausanne, Switzerland
| | - Patricia E. Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Children's Hospital Boston, BostonMA, USA
- Division of Newborn Medicine and Department of Radiology, Children's Hospital Boston, BostonMA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, MGH-Harvard, BostonMA, USA
| | - Damien A. Fair
- Department of Psychiatry, Oregon Health and Science University, PortlandOR, USA
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Cho S, Metcalfe AWS, Young CB, Ryali S, Geary DC, Menon V. Hippocampal-prefrontal engagement and dynamic causal interactions in the maturation of children's fact retrieval. J Cogn Neurosci 2012; 24:1849-66. [PMID: 22621262 DOI: 10.1162/jocn_a_00246] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Children's gains in problem-solving skills during the elementary school years are characterized by shifts in the mix of problem-solving approaches, with inefficient procedural strategies being gradually replaced with direct retrieval of domain-relevant facts. We used a well-established procedure for strategy assessment during arithmetic problem solving to investigate the neural basis of this critical transition. We indexed behavioral strategy use by focusing on the retrieval frequency and examined changes in brain activity and connectivity associated with retrieval fluency during arithmetic problem solving in second- and third-grade (7- to 9-year-old) children. Children with higher retrieval fluency showed elevated signal in the right hippocampus, parahippocampal gyrus (PHG), lingual gyrus (LG), fusiform gyrus (FG), left ventrolateral PFC (VLPFC), bilateral dorsolateral PFC (DLPFC), and posterior angular gyrus. Critically, these effects were not confounded by individual differences in problem-solving speed or accuracy. Psychophysiological interaction analysis revealed significant effective connectivity of the right hippocampus with bilateral VLPFC and DLPFC during arithmetic problem solving. Dynamic causal modeling analysis revealed strong bidirectional interactions between the hippocampus and the left VLPFC and DLPFC. Furthermore, causal influences from the left VLPFC to the hippocampus served as the main top-down component, whereas causal influences from the hippocampus to the left DLPFC served as the main bottom-up component of this retrieval network. Our study highlights the contribution of hippocampal-prefrontal circuits to the early development of retrieval fluency in arithmetic problem solving and provides a novel framework for studying dynamic developmental processes that accompany children's development of problem-solving skills.
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