Reconfiguration of brain network architecture to support executive control in aging
暂无分享,去创建一个
Mark D'Esposito | Gary R. Turner | M. D’Esposito | G. Turner | C. Gallen | Areeba Adnan | Courtney L. Gallen | Areeba Adnan
[1] Linda Geerligs,et al. Flexible connectivity in the aging brain revealed by task modulations , 2014, Human brain mapping.
[2] Arthur W. Toga,et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.
[3] Keiichi Onoda,et al. Small-worldness and modularity of the resting-state functional brain network decrease with aging , 2013, Neuroscience Letters.
[4] Thomas E. Nichols,et al. Rank-order versus mean based statistics for neuroimaging , 2007, NeuroImage.
[5] R. Knight,et al. Prefrontal cortex regulates inhibition and excitation in distributed neural networks. , 1999, Acta psychologica.
[6] S. Rombouts,et al. Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.
[7] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[8] Taraz G. Lee,et al. The Dynamic Nature of Top-Down Signals Originating from Prefrontal Cortex: A Combined fMRI–TMS Study , 2012, The Journal of Neuroscience.
[9] Jonathan D. Power,et al. Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.
[10] G. Busatto,et al. Resting-state functional connectivity in normal brain aging , 2013, Neuroscience & Biobehavioral Reviews.
[11] Pamela K. Smith,et al. Models of visuospatial and verbal memory across the adult life span. , 2002, Psychology and aging.
[12] M E J Newman,et al. Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[13] D. Pandya,et al. Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study. , 2005, Cerebral cortex.
[14] Ruibin Zhang,et al. Reconfiguration of the Brain Functional Network Associated with Visual Task Demands , 2015, PloS one.
[15] R. Nathan Spreng,et al. Prefrontal Engagement and Reduced Default Network Suppression Co-occur and Are Dynamically Coupled in Older Adults: The Default–Executive Coupling Hypothesis of Aging , 2015, Journal of Cognitive Neuroscience.
[16] N. Maurits,et al. A Brain-Wide Study of Age-Related Changes in Functional Connectivity. , 2015, Cerebral cortex.
[17] C. Grady. Cognitive Neuroscience of Aging , 2008, Annals of the New York Academy of Sciences.
[18] A. Meyer-Lindenberg,et al. Neurophysiological correlates of age-related changes in working memory capacity , 2006, Neuroscience Letters.
[19] Leon Danon,et al. Comparing community structure identification , 2005, cond-mat/0505245.
[20] Yong He,et al. Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads. , 2016, Cerebral cortex.
[21] Mark W. Woolrich,et al. Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.
[22] R. Knight,et al. Prefrontal modulation of visual processing in humans , 2000, Nature Neuroscience.
[23] Timothy O. Laumann,et al. Functional Network Organization of the Human Brain , 2011, Neuron.
[24] R. N. Spreng,et al. Executive functions and neurocognitive aging: dissociable patterns of brain activity , 2012, Neurobiology of Aging.
[25] R W Cox,et al. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.
[26] A. Nobre,et al. Top-down modulation: bridging selective attention and working memory , 2012, Trends in Cognitive Sciences.
[27] Denise C. Park,et al. Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.
[28] D. Madden,et al. Disconnected aging: Cerebral white matter integrity and age-related differences in cognition , 2014, Neuroscience.
[29] R. Guimerà,et al. Functional cartography of complex metabolic networks , 2005, Nature.
[30] N. Birbaumer,et al. The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..
[31] Timothy O. Laumann,et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.
[32] Edward T. Bullmore,et al. Modular and Hierarchically Modular Organization of Brain Networks , 2010, Front. Neurosci..
[33] Jeffrey W. Cooney,et al. Top-down suppression deficit underlies working memory impairment in normal aging , 2005, Nature Neuroscience.
[34] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[35] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[36] R. Guimerà,et al. Classes of complex networks defined by role-to-role connectivity profiles. , 2007, Nature physics.
[37] Manfred G Kitzbichler,et al. Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks , 2011, The Journal of Neuroscience.
[38] Daniel Rueckert,et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.
[39] Mark D'Esposito,et al. Training of goal-directed attention regulation enhances control over neural processing for individuals with brain injury. , 2011, Brain : a journal of neurology.
[40] Edward T. Bullmore,et al. Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.
[41] J. Fuster,et al. Functional interactions between inferotemporal and prefrontal cortex in a cognitive task , 1985, Brain Research.
[42] P. Goldman-Rakic,et al. Dorsolateral prefrontal lesions and oculomotor delayed-response performance: evidence for mnemonic "scotomas" , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[43] Sepideh Sadaghiani,et al. Ongoing dynamics in large-scale functional connectivity predict perception , 2015, Proceedings of the National Academy of Sciences.
[44] Adam Gazzaley,et al. In Brief , 2011, Nature Reviews Neuroscience.
[45] Yong He,et al. Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI , 2011, NeuroImage.
[46] M. D’Esposito,et al. Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging , 2003, Nature Reviews Neuroscience.
[47] Edward T. Bullmore,et al. Neuroinformatics Original Research Article , 2022 .
[48] M. Kerszberg,et al. A Neuronal Model of a Global Workspace in Effortful Cognitive Tasks , 2001 .
[49] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[50] Yong Hu,et al. Brain resting-state functional MRI connectivity: Morphological foundation and plasticity , 2014, NeuroImage.
[51] L. Myers,et al. Spearman Correlation Coefficients, Differences between , 2004 .
[52] C. Grady. The cognitive neuroscience of ageing , 2012, Nature Reviews Neuroscience.
[53] Jacob Cohen,et al. Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .
[54] M E J Newman,et al. Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[55] Mert R. Sabuncu,et al. The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.
[56] Mark D'Esposito,et al. Searching for “the Top” in Top-Down Control , 2005, Neuron.
[57] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[58] Justin L. Vincent,et al. Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.
[59] P. Reuter-Lorenz,et al. Neurocognitive Aging and the Compensation Hypothesis , 2008 .
[60] R. N. Spreng,et al. Reliable differences in brain activity between young and old adults: A quantitative meta-analysis across multiple cognitive domains , 2010, Neuroscience & Biobehavioral Reviews.
[61] B. Sahakian,et al. Default Mode Dynamics for Global Functional Integration , 2015, The Journal of Neuroscience.
[62] P. Goldman-Rakic,et al. Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task , 1993, Nature.
[63] Simon B. Eickhoff,et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.
[64] R. Knight,et al. Contribution of Human Prefrontal Cortex to Delay Performance , 1998, Journal of Cognitive Neuroscience.
[65] Paul J. Laurienti,et al. Changes in global and regional modularity associated with increasing working memory load , 2014, Front. Hum. Neurosci..