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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | | | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
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2
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Foldi CJ, Morris MJ, Oldfield BJ. Executive function in obesity and anorexia nervosa: Opposite ends of a spectrum of disordered feeding behaviour? Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110395. [PMID: 34217755 DOI: 10.1016/j.pnpbp.2021.110395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/26/2021] [Accepted: 06/29/2021] [Indexed: 02/02/2023]
Abstract
Higher-order executive functions such as decision-making, cognitive flexibility and behavioural control are critical to adaptive success in all aspects of life, including the maintenance of a healthy body weight by regulating food intake. Performance on tasks designed to assess these aspects of cognition is impaired in individuals with obesity and anorexia nervosa (AN); conditions at either end of a spectrum of body weight disturbance. While the conceptualisation of obesity and AN as mirror images of each other makes some sense from a metabolic point of view, whether or not these conditions also reflect opposing states of executive function is less clear. Here, we review evidence from neurocognitive and neuroimaging studies to compare the direction and extent of executive dysfunction in subjects with obesity and AN and how these are underpinned by changes in structure and function of subregions of the prefrontal cortex (PFC). Both conditions of extreme body weight disturbance are associated with impaired decision-making and cognitive inflexibility, however, impulsive behaviour presents in opposing directions; obesity being associated with reduced behavioural control and AN being associated with elevated control over behaviour with respect to food and feeding. Accordingly, the subregions of the PFC that guide inhibitory control and valuation of action outcomes (dorsolateral prefrontal cortex and orbitofrontal cortex) show opposite patterns of activation in subjects with obesity compared to those with AN, whereas the subregions implicated in cognitive and behavioural flexibility (ventromedial prefrontal cortex and anterior cingulate cortex) show alterations in the same direction in both conditions but with differential extent of dysfunction. We synthesise these findings in the context of the utility of animal models of obesity and AN to interrogate the detail of the neurobiological contributions to cognition in patient populations and the utility of such detail to inform future treatment strategies that specifically target executive dysfunction.
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Affiliation(s)
- Claire J Foldi
- Department of Physiology, Monash University, 26 Innovation Walk, Clayton 3800, Australia; Monash Biomedicine Discovery Institute, 23 Innovation Walk, Clayton 3800, Australia.
| | - Margaret J Morris
- School of Medical Sciences, UNSW Sydney, High Street, Randwick 2052, Australia
| | - Brian J Oldfield
- Department of Physiology, Monash University, 26 Innovation Walk, Clayton 3800, Australia; Monash Biomedicine Discovery Institute, 23 Innovation Walk, Clayton 3800, Australia
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3
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Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. Neuroimage 2020; 221:117126. [PMID: 32673748 DOI: 10.1016/j.neuroimage.2020.117126] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/12/2020] [Accepted: 06/29/2020] [Indexed: 02/04/2023] Open
Abstract
Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.
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Affiliation(s)
- Kamalaker Dadi
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France.
| | - Gaël Varoquaux
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France
| | | | | | | | | | - Arthur Mensch
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France; ENS, DMA, 45 Rue D'Ulm, 75005, Paris, France
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4
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John M, Wu Y, Narayan M, John A, Ikuta T, Ferbinteanu J. Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E617. [PMID: 33286389 PMCID: PMC7517153 DOI: 10.3390/e22060617] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 05/30/2020] [Accepted: 05/30/2020] [Indexed: 12/18/2022]
Abstract
Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.
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Affiliation(s)
- Majnu John
- Center for Psychiatric Neuroscience, Feinstein Institute of Medical Research, Manhasset, NY 11030, USA
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY 11004, USA
- Department of Mathematics, Hofstra University, Hempstead, NY 11549, USA;
| | - Yihren Wu
- Department of Mathematics, Hofstra University, Hempstead, NY 11549, USA;
| | - Manjari Narayan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Paolo Alto, CA 94305, USA;
| | - Aparna John
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA;
| | - Toshikazu Ikuta
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS 38677, USA;
| | - Janina Ferbinteanu
- Departments of Physiology and Pharmacology and of Neurology, State University of New York Downstate Medical Center, Brooklyn, NY 11203, USA
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5
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You do not have to act to be impulsive: Brain resting-state activity predicts performance and impulsivity on the Balloon Analogue Risk Task. Behav Brain Res 2020; 379:112395. [DOI: 10.1016/j.bbr.2019.112395] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 10/19/2019] [Accepted: 11/27/2019] [Indexed: 01/08/2023]
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6
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Larsen B, Verstynen TD, Yeh FC, Luna B. Developmental Changes in the Integration of Affective and Cognitive Corticostriatal Pathways are Associated with Reward-Driven Behavior. Cereb Cortex 2019; 28:2834-2845. [PMID: 29106535 DOI: 10.1093/cercor/bhx162] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Indexed: 01/30/2023] Open
Abstract
The relative influence of affective and cognitive processes on behavior is increasingly understood to transform through development, from adolescence into adulthood, but the neuroanatomical mechanisms underlying this change are not well understood. We analyzed diffusion magnetic resonance imaging in 115 10- to 28-year-old participants to identify convergent corticostriatal projections from cortical systems involved in affect and cognitive control and determined the age-related differences in their relative structural integrity. Results indicate that the relative integrity of affective projections, in relation to projections from cognitive control systems, decreases with age and is positively associated with reward-driven task performance. Together, these findings provide new evidence that developmental differences in the integration of corticostriatal networks involved in affect and cognitive control underlie known developmental decreases in the propensity for reward-driven behavior into adulthood.
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Affiliation(s)
- Bart Larsen
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Timothy D Verstynen
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Fang-Cheng Yeh
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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7
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Bansal K, Garcia JO, Tompson SH, Verstynen T, Vettel JM, Muldoon SF. Cognitive chimera states in human brain networks. SCIENCE ADVANCES 2019; 5:eaau8535. [PMID: 30949576 PMCID: PMC6447382 DOI: 10.1126/sciadv.aau8535] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 02/13/2019] [Indexed: 05/10/2023]
Abstract
The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.
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Affiliation(s)
- Kanika Bansal
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
- Mathematics Department, University at Buffalo, SUNY, Buffalo, NY 14260, USA
| | - Javier O. Garcia
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Steven H. Tompson
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jean M. Vettel
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Sarah F. Muldoon
- Mathematics Department, University at Buffalo, SUNY, Buffalo, NY 14260, USA
- CDSE Program and Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY 14260, USA
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8
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fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 2018; 16:111-116. [PMID: 30532080 PMCID: PMC6319393 DOI: 10.1038/s41592-018-0235-4] [Citation(s) in RCA: 1371] [Impact Index Per Article: 228.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022]
Abstract
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.
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9
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Hung Y, Gaillard SL, Yarmak P, Arsalidou M. Dissociations of cognitive inhibition, response inhibition, and emotional interference: Voxelwise ALE meta-analyses of fMRI studies. Hum Brain Mapp 2018; 39:4065-4082. [PMID: 29923271 DOI: 10.1002/hbm.24232] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 05/14/2018] [Accepted: 05/15/2018] [Indexed: 12/19/2022] Open
Abstract
Inhibitory control is the stopping of a mental process with or without intention, conceptualized as mental suppression of competing information because of limited cognitive capacity. Inhibitory control dysfunction is a core characteristic of many major psychiatric disorders. Inhibition is generally thought to involve the prefrontal cortex; however, a single inhibitory mechanism is insufficient for interpreting the heterogeneous nature of human cognition. It remains unclear whether different dimensions of inhibitory processes-specifically cognitive inhibition, response inhibition, and emotional interference-rely on dissociated neural systems. We conducted systematic meta-analyses of fMRI studies in the BrainMap database supplemented by PubMed using whole-brain activation likelihood estimation. A total of 66 study experiments including 1,447 participants and 987 foci revealed that while the left anterior insula was concordant in all inhibitory dimensions, cognitive inhibition reliably activated specific dorsal frontal inhibitory system, engaging dorsal anterior cingulate, dorsolateral prefrontal cortex, and parietal areas, whereas emotional interference reliably implicated a ventral inhibitory system, involving the ventral surface of the inferior frontal gyrus and the amygdala. Response inhibition showed concordant clusters in the fronto-striatal system, including the dorsal anterior cingulate region and extended supplementary motor areas, the dorsal and ventral lateral prefrontal cortex, basal ganglia, midbrain regions, and parietal regions. We provide an empirically derived dimensional model of inhibition characterizing neural systems underlying different aspects of inhibitory mechanisms. This study offers a fundamental framework to advance current understanding of inhibition and provides new insights for future clinical research into disorders with different types of inhibition-related dysfunctions.
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Affiliation(s)
- Yuwen Hung
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Schuyler L Gaillard
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Pavel Yarmak
- Psychology and Neuroscience, University of Toronto, Toronto, Ontario, Canada
| | - Marie Arsalidou
- Department of Psychology, National Research University Higher School of Economics, Moscow, Russian Federation.,Department of Psychology, York University, Toronto, Ontario, Canada
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10
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Powell MA, Garcia JO, Yeh FC, Vettel JM, Verstynen T. Local connectome phenotypes predict social, health, and cognitive factors. Netw Neurosci 2018; 2:86-105. [PMID: 29911679 PMCID: PMC5989992 DOI: 10.1162/netn_a_00031] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 10/08/2017] [Indexed: 12/13/2022] Open
Abstract
The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and it reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This intersubject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how the local connectome has both the sensitivity and the specificity to be used as a phenotypic marker for subject-specific attributes.
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Affiliation(s)
- Michael A Powell
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | - Javier O Garcia
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jean M Vettel
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Timothy Verstynen
- Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA
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11
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Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A 2018. [PMID: 29382744 DOI: 10.1073/pnas.1715766115.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.
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12
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Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A 2018; 115:E1598-E1607. [PMID: 29382744 DOI: 10.1073/pnas.1715766115] [Citation(s) in RCA: 294] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.
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13
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Smittenaar P, Kurth-Nelson Z, Mohammadi S, Weiskopf N, Dolan RJ. Local striatal reward signals can be predicted from corticostriatal connectivity. Neuroimage 2017; 159:9-17. [PMID: 28736307 PMCID: PMC5678290 DOI: 10.1016/j.neuroimage.2017.07.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Revised: 05/24/2017] [Accepted: 07/19/2017] [Indexed: 12/23/2022] Open
Abstract
A defining feature of the basal ganglia is their anatomical organization into multiple cortico-striatal loops. A central tenet of this architecture is the idea that local striatal function is determined by its precise connectivity with cortex, creating a functional topography that is mirrored within cortex and striatum. Here we formally test this idea using both human anatomical and functional imaging, specifically asking whether within striatal subregions one can predict between-voxel differences in functional signals based on between-voxel differences in corticostriatal connectivity. We show that corticostriatal connectivity profiles predict local variation in reward signals in bilateral caudate nucleus and putamen, expected value signals in bilateral caudate nucleus, and response effector activity in bilateral putamen. These data reveal that, even within individual striatal regions, local variability in corticostriatal anatomical connectivity predicts functional differentiation. We use diffusion imaging to predict striatal reinforcement learning BOLD responses. Structural cortico-striatal connectivity can explain intra-regional BOLD variability. These results suggest a fine functional parcellation based on afferent connectivity.
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Affiliation(s)
- Peter Smittenaar
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, UK.
| | - Zeb Kurth-Nelson
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, UK; Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, London, WC1B 5EH, UK
| | - Siawoosh Mohammadi
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, UK; Department of Systems Neuroscience, Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, UK; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, UK; Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, London, WC1B 5EH, UK
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14
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Zsuga J, Biro K, Tajti G, Szilasi ME, Papp C, Juhasz B, Gesztelyi R. 'Proactive' use of cue-context congruence for building reinforcement learning's reward function. BMC Neurosci 2016; 17:70. [PMID: 27793098 PMCID: PMC5086043 DOI: 10.1186/s12868-016-0302-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 10/14/2016] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. RESULTS In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. CONCLUSIONS Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.
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Affiliation(s)
- Judit Zsuga
- Department of Health Systems Management and Quality Management for Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary.
| | - Klara Biro
- Department of Health Systems Management and Quality Management for Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Gabor Tajti
- Department of Health Systems Management and Quality Management for Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Magdolna Emma Szilasi
- Department of Pharmacology, Faculty of Pharmacy, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Csaba Papp
- Department of Health Systems Management and Quality Management for Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Bela Juhasz
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
| | - Rudolf Gesztelyi
- Department of Pharmacology, Faculty of Pharmacy, University of Debrecen, Debrecen, Nagyerdei krt. 98, 4032, Hungary
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15
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Barredo J, Verstynen TD, Badre D. Organization of cortico-cortical pathways supporting memory retrieval across subregions of the left ventrolateral prefrontal cortex. J Neurophysiol 2016; 116:920-37. [PMID: 27281745 DOI: 10.1152/jn.00157.2016] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/02/2016] [Indexed: 12/14/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) evidence indicates that different subregions of ventrolateral prefrontal cortex (VLPFC) participate in distinct cortical networks. These networks have been shown to support separable cognitive functions: anterior VLPFC [inferior frontal gyrus (IFG) pars orbitalis] functionally correlates with a ventral fronto-temporal network associated with top-down influences on memory retrieval, while mid-VLPFC (IFG pars triangularis) functionally correlates with a dorsal fronto-parietal network associated with postretrieval control processes. However, it is not known to what extent subregional differences in network affiliation and function are driven by differences in the organization of underlying white matter pathways. We used high-angular-resolution diffusion spectrum imaging and functional connectivity analysis in unanesthetized humans to address whether the organization of white matter connectivity differs between subregions of VLPFC. Our results demonstrate a ventral-dorsal division within IFG. Ventral IFG as a whole connects broadly to lateral temporal cortex. Although several different individual white matter tracts form connections between ventral IFG and lateral temporal cortex, functional connectivity analysis of fMRI data indicates that these are part of the same ventral functional network. By contrast, across subdivisions, dorsal IFG was connected with the midfrontal gyrus and correlated as a separate dorsal functional network. These qualitative differences in white matter organization within larger macroanatomical subregions of VLPFC support prior functional distinctions among these regions observed in task-based and functional connectivity fMRI studies. These results are consistent with the proposal that anatomical connectivity is a crucial determinant of systems-level functional organization of frontal cortex and the brain in general.
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Affiliation(s)
- Jennifer Barredo
- Department of Neuroscience, Brown University, Providence, Rhode Island; Brown Institute for Brain Sciences, Brown University, Providence, Rhode Island;
| | - Timothy D Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania; and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - David Badre
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island; Brown Institute for Brain Sciences, Brown University, Providence, Rhode Island
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16
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Dunovan K, Verstynen T. Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning. Front Neurosci 2016; 10:106. [PMID: 27047328 PMCID: PMC4805593 DOI: 10.3389/fnins.2016.00106] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 03/04/2016] [Indexed: 11/13/2022] Open
Abstract
The flexibility of behavioral control is a testament to the brain's capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning.
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Affiliation(s)
- Kyle Dunovan
- Department of Psychology, University of PittsburghPittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon UniversityPittsburgh, PA, USA
| | - Timothy Verstynen
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon UniversityPittsburgh, PA, USA; Department of Psychology, Carnegie Mellon UniversityPittsburgh, PA, USA
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17
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Deng Y, Wang Y, Ding X, Tang YY. The relevance of fractional amplitude of low-frequency fluctuation to interference effect. Behav Brain Res 2016; 296:401-407. [DOI: 10.1016/j.bbr.2015.08.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 07/25/2015] [Accepted: 08/17/2015] [Indexed: 12/20/2022]
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18
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Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum. J Neurosci 2015; 35:3865-78. [PMID: 25740516 DOI: 10.1523/jneurosci.2636-14.2015] [Citation(s) in RCA: 147] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Modification of spatial attention via reinforcement learning (Lee and Shomstein, 2013) requires the integration of reward, attention, and executive processes. Corticostriatal pathways are an ideal neural substrate for this integration because these projections exhibit a globally parallel (Alexander et al., 1986), but locally overlapping (Haber, 2003), topographical organization. Here we explore whether there are unique striatal regions that exhibit convergent anatomical connections from orbitofrontal cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. Deterministic fiber tractography on diffusion spectrum imaging data from neurologically healthy adults (N = 60) was used to map frontostriatal and parietostriatal projections. In general, projections from cortex were organized according to both a medial-lateral and a rostral-caudal gradient along the striatal nuclei. Within rostral aspects of the striatum, we identified two bilateral convergence zones (one in the caudate nucleus and another in the putamen) that consisted of voxels with unique projections from orbitofrontal cortex, dorsolateral prefrontal cortex, and parietal regions. The distributed cortical connectivity of these striatal convergence zones was confirmed with follow-up functional connectivity analysis from resting state fMRI data, in which a high percentage of structurally connected voxels also showed significant functional connectivity. The specificity of this convergent architecture to these regions of the rostral striatum was validated against control analysis of connectivity within the motor putamen. These results delineate a neurologically plausible network of converging corticostriatal projections that may support the integration of reward, executive control, and spatial attention that occurs during spatial reinforcement learning.
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