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Journal Article•DOI•

Increased Functional Selectivity over Development in Rostrolateral Prefrontal Cortex

Carter Wendelken1, Elizabeth D. O'Hare, Kirstie Whitaker, Emilio Ferrer, Silvia A. Bunge •
Helen Wills Neuroscience Institute1
23 Nov 2011-The Journal of Neuroscience (Society for Neuroscience)-Vol. 31, Iss: 47, pp 17260-17268
TL;DR: Examination of cortical thickness revealed that increased functional selectivity in RLPFC could be partly accounted for by cortical thinning in IPL, and fMRI results provide evidence for increasedfunctional selectivity across ages 6–18 years in R LPFC and IPL.
Abstract: Relational reasoning, or the ability to identify and consider relationships between multiple mental representations, is a fundamental component of high-level cognition (Robin and Holyoak, 1995). The capacity to reason with relations enables abstract thought and may be at the core of what makes human cognition unique (Penn et al., 2008). This capacity improves throughout childhood and adolescence (Ferrer et al., 2009). Here, we sought to better understand the neural mechanisms that support its emergence. We have hypothesized previously, based on fMRI research in adults, that (1) inferior parietal lobe (IPL) plays a central role in representing relationships between mental representations (first-order relations) and (2) rostrolateral prefrontal cortex (RLPFC) integrates inputs from IPL to build second-order relational structures (i.e., relations between relations). In the present study, we examined fMRI and cortical thickness data from 85 children and adolescents (ages 6-18 years). Participants performed a relational matching task in which they viewed arrays of four visual stimuli and determined whether two stimuli shared a particular feature (a first-order relational judgment) or whether two pairs of stimuli matched according to the same feature (a second-order relational judgment). fMRI results provide evidence for increased functional selectivity across ages 6-18 years in RLPFC and IPL. Specifically, young children engaged RLPFC and IPL indiscriminately for first-order and second-order relational judgments, and activation for first-order relations diminished with age whereas activation for second-order relations stayed elevated. Examination of cortical thickness revealed that increased functional selectivity in RLPFC could be partly accounted for by cortical thinning in IPL.
Citations
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Journal Article•DOI•
Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder

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Justin T. Baker1, Justin T. Baker2, Avram J. Holmes1, Grace A. Masters2, B.T. Thomas Yeo3, B.T. Thomas Yeo1, Fenna M. Krienen1, Randy L. Buckner, Dost Öngür2, Dost Öngür1 •
Harvard University1, McLean Hospital2, National University of Singapore3
01 Feb 2014-JAMA Psychiatry
TL;DR: The study results support the view that cortical information processing is disrupted in psychosis and provides new evidence that disruptions within the frontoparietal control network may be a shared feature across both schizophrenia and affective psychosis.
Abstract: Importance Psychotic disorders (including schizophrenia, schizoaffective disorder, and psychotic bipolar disorder) are devastating illnesses characterized by breakdown in the integration of information processing. Recent advances in neuroimaging allow for the estimation of brain networks on the basis of intrinsic functional connectivity, but the specific network abnormalities in psychotic disorders are poorly understood. Objective To compare intrinsic functional connectivity across the cerebral cortex in patients with schizophrenia spectrum disorders or psychotic bipolar disorder and healthy controls. Design, Setting, and Participants We studied 100 patients from an academic psychiatric hospital (28 patients with schizophrenia, 32 patients with schizoaffective disorder, and 40 patients with bipolar disorder with psychosis) and 100 healthy controls matched for age, sex, race, handedness, and scan quality from December 2009 to October 2011. Main Outcomes and Measures Functional connectivity profiles across 122 regions that covered the entire cerebral cortex. Results Relative to the healthy controls, individuals with a psychotic illness had disruption across several brain networks, with preferential reductions in functional connectivity within the frontoparietal control network ( P Conclusions and Relevance Our study results support the view that cortical information processing is disrupted in psychosis and provides new evidence that disruptions within the frontoparietal control network may be a shared feature across both schizophrenia and affective psychosis.

335 citations

Journal Article•DOI•
Brain Development During the Preschool Years

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Timothy T. Brown1, Terry L. Jernigan•
University of California, San Diego1
25 Sep 2012-Neuropsychology Review
TL;DR: This article reviews human brain development during the preschool years, sampling scientific evidence from a variety of sources and discusses multimodal and multidimensional imaging approaches, which are believed to be critical for increasing understanding of brain development and its relationship to cognitive and behavioral growth in the preschoolyears and beyond.
Abstract: The preschool years represent a time of expansive mental growth, with the initial expression of many psychological abilities that will continue to be refined into young adulthood. Likewise, brain development during this age is characterized by its "blossoming" nature, showing some of its most dynamic and elaborative anatomical and physiological changes. In this article, we review human brain development during the preschool years, sampling scientific evidence from a variety of sources. First, we cover neurobiological foundations of early postnatal development, explaining some of the primary mechanisms seen at a larger scale within neuroimaging studies. Next, we review evidence from both structural and functional imaging studies, which now accounts for a large portion of our current understanding of typical brain development. Within anatomical imaging, we focus on studies of developing brain morphology and tissue properties, including diffusivity of white matter fiber tracts. We also present new data on changes during the preschool years in cortical area, thickness, and volume. Physiological brain development is then reviewed, touching on influential results from several different functional imaging and recording modalities in the preschool and early school-age years, including positron emission tomography (PET), electroencephalography (EEG) and event-related potentials (ERP), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS). Here, more space is devoted to explaining some of the key methodological factors that are required for interpretation. We end with a section on multimodal and multidimensional imaging approaches, which we believe will be critical for increasing our understanding of brain development and its relationship to cognitive and behavioral growth in the preschool years and beyond.

283 citations

Cites background from "Increased Functional Selectivity ov..."

  • ...…Holland et al. 2007; Schlaggar et al. 2002; Turkeltaub et al. 2003), executive functions (Bunge et al. 2002; Dumontheil and Klingberg 2011; Somerville et al. 2010; Wendelken et al. 2011b), and even social, moral, and emotional cognition (Decety et al. 2011; McRae et al. 2012; Pfeifer et al. 2009)....

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  • ...School-age studies have looked at developmental changes in attention (Vaidya et al. 2011; Velanova et al. 2008; Wendelken et al. 2011a), memory (Ghetti et al. 2010; Nelson et al. 2000; Thomas et al. 2004), reading, language, and semantics (Booth et al. 1999; Brown et al. 2005; Chou et al. 2006;…...

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Journal Article•DOI•
Human-Specific Transcriptional Networks in the Brain

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Genevieve Konopka1, Tara Friedrich1, Jeremy Davis-Turak1, Kellen D. Winden1, Michael C. Oldham2, Fuying Gao1, Leslie Chen1, Guang-Zhong Wang3, Rui Luo1, Todd M. Preuss4, Daniel H. Geschwind •
University of California, Los Angeles1, University of California, San Francisco2, University of Texas Southwestern Medical Center3, Yerkes National Primate Research Center4
23 Aug 2012-Neuron
TL;DR: Comparisons of the transcriptome of human, chimpanzee, and macaque telencephalon reveal a predominance of genes differentially expressed within human frontal lobe and a striking increase in transcriptional complexity specific to the human lineage in the frontal lobe.

239 citations

Cites background from "Increased Functional Selectivity ov..."

  • ..., 2011), has a protracted course of development extending into adolescence and beyond (Dumontheil et al., 2008; Rakic and Yakovlev, 1968; Wendelken et al., 2011), and appears to be affected in diseases that affect higher-order cognition, including autism and schizophrenia (see the review of Dumontheil et al....

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  • ..., 2001, 2011), is involved in higherorder cognitive functions (including mental multitasking, social cognition, and planning andmanipulation of abstract representations) (Dumontheil et al., 2008; Wendelken et al., 2011), has a protracted course of development extending into adolescence and beyond (Dumontheil et al....

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  • ...…et al., 2011), has a protracted course of development extending into adolescence and beyond (Dumontheil et al., 2008; Rakic and Yakovlev, 1968; Wendelken et al., 2011), and appears to be affected in diseases that affect higher-order cognition, including autism and schizophrenia (see the…...

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  • ...…functions (including mental multitasking, social cognition, and planning andmanipulation of abstract representations) (Dumontheil et al., 2008; Wendelken et al., 2011), has a protracted course of development extending into adolescence and beyond (Dumontheil et al., 2008; Rakic and Yakovlev,…...

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Journal Article•DOI•
Through Thick and Thin: a Need to Reconcile Contradictory Results on Trajectories in Human Cortical Development.

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Kristine B. Walhovd1, Anders M. Fjell1, Jay N. Giedd, Anders M. Dale, Timothy T. Brown2 •
University of Oslo1, University of California, San Diego2
11 Jan 2016-Cerebral Cortex
TL;DR: This work reviews inconsistencies in brain maturation trajectories, and recommends strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods.
Abstract: Understanding how brain development normally proceeds is a premise of understanding neurodevelopmental disorders. This has sparked a wealth of magnetic resonance imaging (MRI) studies. Unfortunately, they are in marked disagreement on how the cerebral cortex matures. While cortical thickness increases for the first 8-9 years of life have repeatedly been reported, others find continuous cortical thinning from early childhood, at least from age 3 or 4 years. We review these inconsistencies, and discuss possible reasons, including the use of different scanners, recording parameters and analysis tools, and possible effects of variables such as head motion. When tested on the same subsample, 2 popular thickness estimation methods (CIVET and FreeSurfer) both yielded a continuous thickness decrease from 3 years. Importantly, MRI-derived measures of cortical development are merely our best current approximations, hence the term "apparent cortical thickness" may be preferable. We recommend strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods. As neurodevelopmental origins of early- and late-onset disease are increasingly recognized, resolving inconsistencies in brain maturation trajectories is important.

213 citations

Cites background from "Increased Functional Selectivity ov..."

  • ...2013) years and up to 6 (Wendelken et al. 2011; Mutlu et al. 2013) or 7 years (Mills et al....

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  • ...135/201 7–23 Long FS ROI Linear decrease most ROIs, few quadratic, or cubic, mostly U-shaped 16 Wendelken et al. (2011), J Neurosci....

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  • ...These studies sampled children from as low as age 3 (Zielinski et al. 2014) or 4 (Nguyen et al. 2013) years and up to 6 (Wendelken et al. 2011; Mutlu et al. 2013) or 7 years (Mills et al. 2014; Wierenga et al. 2014), which should be sufficient to detect early peaks in cortical thickness....

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Journal Article•DOI•
The Role of Fronto-Parietal and Fronto-Striatal Networks in the Development of Working Memory: A Longitudinal Study

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Fahimeh Darki1, Torkel Klingberg1•
Karolinska Institutet1
01 Jun 2015-Cerebral Cortex
TL;DR: A dynamic of neural networks underlying WM development is shown in which cortical activity and structure relate to current capacity, while white matter tracts and caudate activity predict future WM capacity.
Abstract: The increase in working memory (WM) capacity is an important partof cognitive development during childhood and adolescence. Cross-sectional analyses have associated this development with higheractivity, thinner cortex, and white matter maturation in fronto-parie-tal networks. However, there is still a lack of longitudinal datashowing the dynamics of this development and the role of subcorti-cal structures. We included 89 individuals, aged 6–25 years, whowere scanned1–3times at 2-year intervals.Functional magneticres-onance imaging (fMRI) was used to identify activated areas insuperior frontal, intraparietal cortices, and caudate nucleus duringperformance on a visuo-spatial WM task. Probabilistic tractographydetermined the anatomical pathways between these regions. In thecross-sectional analysis, WM capacity correlated with activity infrontal and parietal regions, cortical thickness in parietal cortex, andwhite matter structure [both fractional anisotropy (FA) and whitematter volume] of fronto-parietal and fronto-striatal tracts. However,in the longitudinal analysis, FA in white matter tracts and activity incaudate predicted future WM capacity. The results show a dynamicof neural networks underlying WM development in which corticalactivity and structure relate to current capacity, while white mattertracts and caudate activity predictfuture WM capacity.Keywords: caudate nucleus, cortical thickness, development, DTI, fMRI,working memoryIntroductionWorking memory (WM) capacity increases during childhoodand adolescence, which is important for academic performanceand cognition (Gathercoleetal.2003;Dumontheiland Klingberg2012). Impaired WM capacity is associated with several devel-opmental neuropsychiatric and learning disorders, includingattention-deficit/hyperactivity disorder (ADHD; Nigg 2001;Westerbergetal.2004; Martinussen etal.2005) and dyscalculia(McLean and Hitch 1999; Camos 2008; Szucs et al. 2013).It is therefore important to characterize the neural basis of thisdevelopment.WM capacity in children and adolescents is positively corre-lated with brain activity, most consistently localized to theintraparietal cortex, superior frontal sulcus, and dorsolateralprefrontal cortex (Klingberg et al. 2002; Kwon et al. 2002;Crone et al. 2006; Scherf et al. 2006; Olesen et al. 2007).Measures of cortical thinning in the frontal and parietal cortexare also correlated with WM capacity (Tamnes et al. 2010;Ostby et al. 2011; Tamnes et al. 2013) and also with reasoningability, which is an ability highly correlated with WM (Sowellet al. 2004; Shaw et al. 2006; Tamnes et al. 2011; Wendelkenet al. 2011). Furthermore, white matter maturation of the path-ways between the parietal and frontal cortex, including thesuperior longitudinal fasciculus, correlate with WM capacityduring childhood and adolescence (Olesen, Nagy, et al. 2003;Nagyetal. 2004; Ostbyetal. 2011; Vestergaard et al. 2011).These studies thus suggest that improvements in WMcapacity are associated with a gradual maturation of white andgray matter in a fronto-parietal network. It is not clear,however, how these changes are related to each other, and ifcertain changes cause the later changes in WM performance.The role of the striatum for development is also unclear. Thecaudate nucleus is activated during performance of WM tasksin nonhuman primates (Levy et al. 1997), children (Klingberget al. 2002; Ziermans et al. 2012), and adults (Postle et al.2000), but its role in development is unclear.One reason why the dynamics of WM development has notbeen clarified is that most of these studies have been cross-sectional, correlating the current cognitive ability with currentstructure or activity. An exception here is the study by Ullmanet al. (2014), who used a longitudinal design and a multivariateanalysis to show that there were differences between multi-variate models correlating with current cognitive capacity andthe models predicting the change of capacity over the next 2years. Since this study was multivariate, it was not designed tospecifytheroleofanatomicallydefinedregionsornetworks.In the current study, we first identi ed the regions ofinterest(ROIs) based on the main effect of WM during development(Dumontheil et al. 2011; Ziermans et al. 2012) for a group of89 individuals, aged 6–25 years, who were scanned 1–3 timesat 2-year intervals. Using diffusion tensor imaging (DTI), wethen traced the white matter tracts connecting these regions.Measures of blood-oxygen-level dependent (BOLD) contrastand cortical thickness were extracted from functionally definedROIs. White matter volume and fractional anisotropy (FA)were also measured along the fronto-parietal and front-striatalwhite matter pathways. The brain measures together withvisuo-spatial WM capacity were assessed for 3 time points with2-year intervals and used to characterize the relationshipbetween brain and WM during development.Methods

164 citations

Cites background from "Increased Functional Selectivity ov..."

  • ...…in the frontal and parietal cortex are also correlated with WM capacity (Tamnes et al. 2010; Østby et al. 2011; Tamnes et al. 2013) and also with reasoning ability, which is an ability highly correlated with WM (Sowell et al. 2004; Shaw et al. 2006; Tamnes et al. 2011; Wendelken et al. 2011)....

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References
More filters
Journal Article•DOI•
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain

[...]

Nathalie Tzourio-Mazoyer1, B. Landeau1, D. Papathanassiou1, Fabrice Crivello1, Octave Etard1, Nicolas Delcroix1, Bernard Mazoyer2, Marc Joliot1 •
University of Paris1, Institut Universitaire de France2
01 Jan 2002-NeuroImage
TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.

13,678 citations

"Increased Functional Selectivity ov..." refers methods in this paper

  • ...Each region was defined anatomically using templates from the automated anatomical labeling set (Tzourio-Mazoyer et al., 2002) and was further restricted to voxels within the anatomical region that demonstrated task-related activation across all participants (first-order null or second-order null,…...

    [...]

Journal Article•DOI•
Cortical surface-based analysis. I. Segmentation and surface reconstruction

[...]

Anders M. Dale1, Bruce Fischl1, Martin I. Sereno2•
Harvard University1, University of California, San Diego2
01 Feb 1999-NeuroImage
TL;DR: A set of automated procedures for obtaining accurate reconstructions of the cortical surface are described, which have been applied to data from more than 100 subjects, requiring little or no manual intervention.

9,599 citations

"Increased Functional Selectivity ov..." refers background in this paper

  • ...…(6) automated topology correction (Ségonne et al., 2007), and (7) surface deformation after intensity gradients to optimally place the gray/white and gray/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999)....

    [...]

Journal Article•DOI•
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

[...]

Bruce Fischl1, David H. Salat1, Evelina Busa1, Marilyn S. Albert1, Megan E. Dieterich, Christian Haselgrove, Andre van der Kouwe1, Ronald J. Killiany2, David N. Kennedy, Shuna Klaveness, Albert Montillo3, Nikos Makris, Bruce R. Rosen1, Anders M. Dale1 •
Harvard University1, Boston University2, University of Pennsylvania3
31 Jan 2002-Neuron
TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.

7,120 citations

Journal Article•DOI•
Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System

[...]

Bruce Fischl1, Martin I. Sereno2, Anders M. Dale1•
Harvard University1, University of California, San Diego2
01 Feb 1999-NeuroImage
TL;DR: A set of procedures for modifying the representation of the cortical surface to inflate it so that activity buried inside sulci may be visualized, cut and flatten an entire hemisphere, and transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system are designed.

5,816 citations

"Increased Functional Selectivity ov..." refers methods in this paper

  • ...Thickness measures were mapped onto the inflated surface of each participant’s reconstructed brain (Fischl et al., 1999), enabling the visualization of data across the entire cortical surface, independent of cortical folding....

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  • ...Maps were smoothed with a 10 mm Gaussian kernel, and non-rigid high-dimensional spherical averaging was used to align cortical folding patterns across participants (Fischl et al., 1999), allowing for the creation of average surface models while accounting for cortical sulcal variability across participants....

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  • ...…smoothed with a 10 mm Gaussian kernel, and non-rigid high-dimensional spherical averaging was used to align cortical folding patterns across participants (Fischl et al., 1999), allowing for the creation of average surface models while accounting for cortical sulcal variability across participants....

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Journal Article•DOI•
Measuring the thickness of the human cerebral cortex from magnetic resonance images

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Bruce Fischl1, Anders M. Dale1•
Harvard University1
26 Sep 2000-Proceedings of the National Academy of Sciences of the United States of America
TL;DR: An automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy is presented.
Abstract: Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.

5,171 citations

"Increased Functional Selectivity ov..." refers methods in this paper

  • ...Details of these procedures have been described previously (Fischl and Dale, 2000)....

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  • ...Cortical thickness was calculated as the closest distance from the gray/ white boundary to the gray/CSF boundary at each vertex on the tessellated surface (Fischl and Dale, 2000)....

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