Conundra about the Angular Gyrus (AG)
Jeffrey R. Binder and Rutvik H.Desai, The neurobiology of semantic memory, Trends in Cognitive Science, 2011.
Gina F. Humphreys et al.,Establishing task- and modality-dependent dissociations between the semanticand default mode networks, PNAS, 2015. doi: 10.1073/pnas.1422760112.
Humphreys GF, Lambon Ralph MA, Fusion and Fission of Cognitive Functions in the Human Parietal Cortex, Cereb Cortex. 2015 Oct;25(10):3547-60. doi: 10.1093/cercor/bhu198. Epub 2014 Sep 9.
Humphreys GF et al., Time course of semantic processes during sentence comprehension: an fMRI study. Neuroimage. 2007 Jul 1;36(3):924-32. Epub 2007 Apr 10.
Mohamed L. Seghier et al., Functional Subdivisions in the Left Angular Gyrus Where the Semantic System Meets and Diverges from the Default Network, Journal of Neuroscience 15 December 2010, 30 (50) 16809-16817; DOI: https://doi.org/10.1523/JNEUROSCI.3377-10.2010
Mohamed L. Seghier and Cathy J. Price, Functional heterogeneity within the default network during semantic processing and speech production, Front. Psychol., 13 August 2012
Matthew N. DeSalvo et al., Task-dependent reorganization of functional connectivity networks during visual semantic decision making, Brain and Behavior 2014; 4(6): 877–885
Jeffrey R. Binder et al., Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies, Cerebral Cortex December 2009;19:2767--2796
William W. Graves et al., Neural Systems for Reading Aloud: A Multiparametric Approach, Cerebral Cortex August 2010;20:1799--1815
doi:10.1093/cercor/bhp245
Wirth et al., Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis.Neuroimage. 2011 Feb 14;54(4):3057-66. doi: 10.1016/j.neuroimage.2010.10.039. Epub 2010 Oct 20.
Irit Shapira-Lichter et al., Portraying the unique contribution of the default mode network to internally driven mnemonic processes, PNAS March 26, 2013 110 (13) 4950-4955; https://doi.org/10.1073/pnas.1209888110
ATL(Anterior Temporal Lobe) and Semantic Processing
Benno Gesierich, Jorge Jovicich, Marianna Riello, Michela Adriani, Alessia Monti, Valentina Brentari, Simon D. Robinson, Stephen M. Wilson, Scott L. Fairhall, and Maria Luisa Gorno-Tempini. Distinct Neural Substrates for Semantic Knowledge and Naming in the Temporoparietal Network, Cerebral Cortex October 2012;22:2217-2226, doi:10.1093/cercor/bhr286
Matthew A. Lambon Ralph, Neurocognitive insights on conceptual knowledge and its breakdown, Philosophical Transactions of the Royal Society B: Biological Sciences, vol 369, no. 1634, 20120392. DOI: 10.1098/rstb.2012.0392
Ying Zhao et al., Left Anterior Temporal Lobe and Bilateral Anterior Cingulate Cortex Are Semantic Hub Regions: Evidence from Behavior-Nodal Degree Mapping in Brain-Damaged Patients, The Journal of Neuroscience, January 4, 2017 • 37(1):141–151 • 141
(Perirhinal Cortex)
Alex Clarke and Lorraine K. Tyler, Object-Specific Semantic Coding in Human Perirhinal Cortex, The Journal of Neuroscience, April 2, 2014 • 34(14):4766–4775
R. R. Davies, Kim S. Graham, John H. Xuereb, Guy B. Williams and John R. Hodges, The human perirhinal cortex and semantic memory, European Journal of Neuroscience, Vol. 20, pp. 2441–2446, 2004
Sasa L. Kivisaari, Lorraine K. Tyler, Andreas U. Monsch, and Kirsten I. Taylor, Medial perirhinal cortex disambiguates confusable objects, Brain 2012: 135; 3757–3769, doi:10.1093/brain/aws277
Amy Rose Price, Michael F. Bonner, Jonathan E. Peelle, and Murray Grossman, Neural coding of fine-grained object knowledge in perirhinal cortex, bioRxiv, https://t.co/TNWq666CZM #biorxiv_neursci
Paul Wright, Billi Randall,Alex Clarke, Lorraine K.Tyler,The perirhinal cortex and conceptual processing: Effects of feature-based statistics following damage to the anterior temporal lobes, Neuropsychologia, Volume 76, September 2015, Pages 192-207
(Uncinate Fasciculus)
Hugues Duffau, Peggy Gatignol,Sylvie Moritz-Gasser, Emmanuel Mandonnet, Is the left uncinate fasciculus essential for language? A cerebral stimulation study,J Neurol (2009) 256:382?389 DOI 10.1007/s00415-009-0053-9
Costanza Papagno, Naming and the Role of the Uncinate Fasciculus in Language Function, Curr Neurol Neurosci Rep (2011) 11:553–559, DOI 10.1007/s11910-011-0219-6
Rebecca J. Von Der Heide, Laura M. Skipper, Elizabeth Klobusicky and Ingrid R. Olson, Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis, Brain 2013: 136; 1692–1707, doi:10.1093/brain/awt094
Ultra-high field fMRI (>7T)
Kevin M. Aquino et al., Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher-order cognitive tasks: An inter-sensory task directing attention between visual and somatosensory domains, Human Brain Mapping, 2018, DOI: 10.1002/hbm.24450
Federico De Martino et al., Whole brain high-resolution functional imaging at ultra high magnetic fields: an application to the analysis of resting state networks, Neuroimage, 2011, doi:10.1016/j.neuroimage.2011.05.008.
Birte U. Forstmann et al., Multi-modal ultra-high resolution structural 7-Tesla MRI data repository, Nature Scientific Data, 2014
Adam M. Goodman et al., Neural Correlates of Consumer Buying Motivations: A 7T functional Magnetic Resonance Imaging (fMRI) Study, frontiers in Neuroscience, 2017
Krzysztof Gorgolewski et al., A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures, Nature Scientific Data, 2018
Joanne R. Hale et al., Comparison of functional connectivity in default mode and sensorimotor networks at 3 and 7T, Magn Reson Mater Phy, 2010.
Valentin G. Kemper et al., High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4 T, NeuroImage, 2018
Laurel S. Morris et al., Ultra-high field MRI reveals mood-related circuit disturbances in depression: a comparison between 3-Tesla and 7-Tesla, Translational Psychiatry, 2019.
Michael Peer et al., Brain system for mental orientation in space, time, and person, PNAS, 2015
Slavatore Torrisi et al., Statistical power comparisons at 3T and 7T with a GO/NOGO task. NeuroImage, 2018
An T. Vu et al., Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment, Cogn Neuropsychol. 2016
Goodman et al., Neural Correlates of Consumer Buying Motivations: A 7T functional Magnetic Resonance Imaging (fMRI) Study, Front Neurosci. 2017; 11: 512.
echo planar imaging with keyhole (EPIK)
Seong Dae Yun et al., Evaluating the Utility of EPIK in a Finger Tapping fMRI Experiment using BOLD Detection and Effective Connectivity, 2019.
Seong Dae Yun ey al., Parallel imaging acceleration of EPIK for reduced image distortions in fMRI, 2013.
M. Zaitsev et al., Shared k-Space Echo Planar Imaging With Keyhole, 2001.
Brain and AI, Multi-Voxel (Multi-Variate) Pattern Analysis (MVPA)
Francisco Pereira,Tom Mitchell, and Matthew Botvinick, Machine learning classifiers and fMRI: a tutorial overview, Neuroimage. 2009 Mar; 45(1 Suppl): S199–S209. doi: 10.1016/j.neuroimage.2008.11.007
Andrew J. Anderson, Douwe Kiela, Stephen Clark, Massimo Poesio, Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns, ACL 2017
(Read also D. Kiela and L. Bottou. 2014. Learning image embeddings using convolutional neural networks for improved multi-modal semantics. In Proceedings of EMNLP, pages 36–45, Doha, Qatar.)
Alexander G. Huth, Wendy A. de Heer, Thomas L. Griffiths, Frédéric E. Theunissen, & Jack L. Gallant, Natural speech reveals the semantic maps that tile human cerebral cortex, Nature, 2016, doi:10.1038/nature17637
(Read also
Lawrence W. Barsalou, What doessemantic tiling of the cortex tell us about semantics?, Neuropsychologia,Volume 105, October 2017, Pages 18-38)
Brain and Deep Learning
N. Kriegeskorte. 2015. Deep neural networks: A new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1:417–446.
Brain and Topic Mapping
Russell Poldrack et al., Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping, PLOS Computational Biology, 2012
Functional Connectivity
--Papers of our interest
Susan Whitfield-Gabrieli and Alfonso Nieto-Castanon, Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks, BRAIN CONNECTIVITY Volume 2, Number 3, 2012, Mary Ann Liebert, Inc. DOI: 10.1089/brain.2012.0073
Luke J. Hearne, Jason B. Mattingley, Luca Cocchi,Functional brain networks related to individual differences in human intelligence at rest, Scientific Reports 6:32328 DOI: 10.1038/srep32328
Yan Tao, Bing Liu, Xiaolong Zhang, Jin Li, Wen Qin, Chunshui Yu and Tianzi Jiang ,The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety, Front Neuroanat. 2015; 9: 152, doi: 10.3389/fnana.2015.00152
Jeffrey R. Binder, Rutvik H. Desai, William W. Graves and Lisa L. Conant, Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies, Cerebral Cortex December 2009;19:2767--2796 doi:10.1093/cercor/bhp055
Buckner RL, Andrews-Hanna JR, Schacter DL., The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011.
Randy L. Buckner and Daniel C. Carroll, Self-projection and the brain, TRENDS in Cognitive Sciences Vol.11 No.2
Erez Simony et al., Dynamic reconfiguration of the default mode network during narrative comprehension, Nature Communications, 2016, DOI: 10.1038/ncomms12141
--(Cognitive impairment and rsfc)
Alireza Salami et al., Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging, PNAS December 9, 2014 111 (49) 17654-17659
Cameron J. Dunn, Shantel L Duffy, Ian B Hickie, Jim Lagopoulos, Simon J.G. Lewis, Sharon L. Naismith, James M. Shine, Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment, NeuroImage: Clinical 4 (2014) 473–480
Shengbing Pei et al., Fusion Analysis of Resting-State Networks and Its Application to Alzheimer’s Disease, TSINGHUA SCIENCE AND TECHNOLOGY , 2019
山口修平、小野田慶一、教育講演「安静時機能的MRIによる認知症早期診断」、高次脳機能研究34 (1):9~16, 2014
--Review articles of resting State functional connectivity (2010~Present)
Islam et al., A Survey of Graph Based Complex Brain Network Analysis Using Functional and Diffusional MRI, 2017
KA Smitha et al, Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks, 2017
Lee et al., Resting-State fMRI: A Review of Methods and Clinical Applications, 2013
van den Heuvel, Exploring the brain network: A review on resting-state fMRI functional connectivity, 2010
Cole et al., Advances and pitfalls in the analysis and interpretation of resting-state FMRI data, 2010
H,LV et al., Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know, 2018
--Task-based (Task-induced) functional connectivity and the other related areas
Gonzalez-Castillo et al., Task-based dynamic functional connectivity: Recent findings and open questions, 2018
Simoney et al., Dynamic reconfiguration of the default mode network during narrative comprehension, 2016
Eguiluz et al., Scale-Free Brain Functional Networks, 2005
Telesford et al., Reproducibility of graph metrics in fMRI networks, 2010
Danielle S. Bassett et al., Adaptive reconfiguration of fractal small-world human brain functional networks, PNAS, 2006
Richard F. Betzel, Danielle S. Bassett, Multi-scale brain networks, NeuroImage 160 (2017) 73–83
Michał Bola, Bernhard A. Sabel, Dynamic reorganization of brain functional networks during cognition, NeuroImage 114 (2015) 398–413
Michael W. Cole et al., Intrinsic and Task-Evoked Network Architectures of the Human Brain, Neuron, http://dx.doi.org/10.1016/j.neuron.2014.05.014
Emily S Finn et al., Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity, Nature Neuroscience, 2015
R. Matthew Hutchison et al., Dynamic functional connectivity: Promise, issues, and interpretations, NeuroImage 80 (2013) 360–378
Ankit N. Khambhati et al., Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration, NeuroImage 166 (2018) 385–399
Fenna M. Krienen et al., Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture, Phil. Trans. R. Soc. B 369: 20130526.
Timothy O. Laumann et al., Functional System and Areal Organization of a Highly Sampled Individual Human Brain, Neuron, 2015
Maarten Mennes et al., Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity, NeuroImage 50 (2010) 1690–1701
Jonathan D. Power et al., Evidence for Hubs in Human Functional Brain Networks, Neuron, Neuron 79, 798–813, August 21, 2013
Maria Giulia Pretia et al., The dynamic functional connectome: State-of-the-art and perspectives, NeuroImage 160 (2017) 41–54
Lubdha M. Shah et al., Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state, Brain Behaviour, 2016; 6(5)
James M. Shine et al., The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance, Neuron 92, 544–554 October 19, 2016
Diego Vidaurre et al., Discovering dynamic brain networks from big data in rest and task, NeuroImage 180 (2018) 646–656
--Cerebral stroke, Infarction and MRI (esp. functional connectivity)
Rosalia Dacosta-Aguayo et al., Impairment of functional integration of the default mode network correlates with cognitive outcome at three months after stroke, Hum Brain Mapp. 2015 Feb; 36(2): 577–590.
Golestani AM et al., Longitudinal evaluation of resting-state FMRI after acute stroke with hemiparesis, Neurorehabil Neural Repair. 2013 Feb;27(2):153-63. doi: 10.1177/1545968312457827. Epub 2012 Sep 20.
Anil Man Tuladhar et al., Default Mode Network Connectivity in Stroke Patients, PLoS One. 2013; 8(6): e66556.
Pineiro R et al., Altered hemodynamic responses in patients after subcortical stroke measured by functional MRI, Stroke. 2002 Jan;33(1):103-9.
--RSFC at 1.5 T ("1.5T" DMN fMRI )
Ludovica Griffanti et al., Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease
MRI acquisitions were performed using a 1.5 T Siemens Magnetom Avanto (Erlangen, Germany) scanner with an eight-channel head coil. Resting state fMRI, BOLD EPI images (TR/TE = 2500/30 ms; resolution = 3.1 × 3.1 × 2.5 mm3; matrix size = 64 × 64; number of axial slices = 39; number of volumes = 160; acquisition time 6 min and 40 s) were collected at rest. Subjects were instructed to keep their eyes closed, not to think about anything in particular, and not to fall asleep. T1-weighted 3D scans were also acquired (TR/TE = 1900/3.37 ms; resolution = 1 × 1 × 1 mm3; matrix size = 192 × 256; number of axial slices = 176) and used as anatomical references for fMRI analysis and for voxel-based morphometry (VBM) analysis. T2-weighted dual-echo turbo spin echo (TR/TE = 2920/22 ms, FoV = 240 × 180 mm, resolution = 0.75 × 0.75 × 4 mm3, number of axial slices = 25) and FLAIR (TR/TE = 9000/121 ms, FoV = 240 × 168 mm, in-plane resolution = 0.94 × 0.94 × 5 mm3, number of coronal slices = 24) images were also acquired to limit the risk of including subjects with concomitant vascular pathology (exclusion criteria: one or more macroscopic T2-weighted abnormalities located in the deep white matter (WM) or more than five abnormalities, maximum diameter < 5 mm, located in periventricular regions).
Rektor I et al., Association between the basal ganglia and large-scale brain networks in epilepsy.
Imaging was performed on a 1.5 T Siemens Magnetom Symphony scanner. We obtained 300 functional scans from each subject during one 15-min run. Functional images were acquired using a gradient echo echoplanar imaging (EPI) sequence with the following parameters: RT (scan repeat time) = 3,000 ms, TE = 40 ms, FOV = 220 mm, flip angle = 90, matrix size 64 * 64, slice thickness = 3.5 mm, 32 transversal slices per scan. Following functional measurements,
high-resolution anatomical T1-weighted images were acquired using a 3D sequence that served as a matrix for the functional imaging (160 sagittal slices, resolution 256 * 256 interpolated to 512 * 512, slice thickness = 1.17 mm, TR = 1,700 ms, TE = 3.96 ms, FOV = 246 mm, flip angle = 15).
E Isanova et al., Resting-state fMRI study of patients with fragile X syndrome,
The fMRI was performed on an Achieva (Philips) scanner with a magnetic field strength of 1.5 T by using an 8-channel head SENSE coil. FMRI data were acquired using an echo-planar imaging sequence with the following imaging parameters: matrix = 64×64, 35 slices, voxel size = 4×4×4 mm, repetition time TR = 3500 ms, echo time ТЕ = 50 ms). Reference anatomical images were obtained by T1-TFE (turbo field echo) sequence with the following imaging parameters: matrix = 256×256, 64 slices, voxel size = 1×1×3 mm in three orthogonal projections.
Tuomo Starck et al., Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing
Imaging was carried out during 2007 using a GE 1.5 T HDX scanner equipped with an 8-channel head coil employing parallel imaging with an acceleration factor of 2. During the resting state scan the participants were asked to lie still, stay relaxed and awake and look at a white cross on the middle of a dark-gray screen. Within the MRI session the resting state was scanned before any task-fMRI scans. BOLD fMRI scanning of 7.5 min consisted of 253 whole brain volumes of which the first three were discarded due to T1 equilibrium effects. Parameters of the GR-EPI scanning employing parallel imaging were TR 1.8 s, TE 40 ms, flip angle 90°, FOV 256 mm, 64 × 64 in-plane matrix, 4 × 4 × 4 mm voxel size, 28 oblique axial slices with a 0.4 mm gap and interleaved acquisition order. Structural data were acquired using a T1-weighted 3D FSPGR sequence with 1 mm oblique axial slices, FOV 24.0 × 24.0 cm with a 256 × 256 matrix.
Hongyi Zheng et al., Acute Effects of Alcohol on the Human Brain: A Resting-State fMRI Study
All anatomical and BOLD-sensitive MRI data were acquired using gradient-echo echo-planar imaging (EPI) sequences in a 1.5T MRI scanner (GE) with an eight-channel-phased array head coil. Foam pads were used to reduce head movements and scanner noise. To measure the individual fMRI data, the imaging parameters were set as follows: slice thickness = 5 mm, slice gap = 1 mm, TR = 2,000 ms, TE = 30 ms, FOV = 24 cm × 24 cm, flip angle = 90°, and matrix = 64 × 64. 180 volumes (20 slices per volume) were acquired during 360 s of an fMRI run. During data acquisition, subjects were required to relax with eyes closed, not to fall asleep, and to move as little as possible. For anatomic data sets, we used a 3D-BRAVO sequence (thickness: 1.4 mm (no gap), TR = 8.2 ms, TE = 1.0 ms, FOV = 24 cm × 24 cm, flip angle = 25°, and matrix = 256 × 256).
Lee et al., Resting-State fMRI: A Review of Methods and Clinical Applications (review article)
Sequential learning, Statistical learning
Michelle Sandoval, Dianne Patterson, Huanping Dai, Christopher J. Vance, and Elena Plante, Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm, Front Psychol. 2017; 8: 1234. doi: 10.3389/fpsyg.2017.01234
Language areas in the right hemisphere
Rachel L. C. Mitchel et al., 2005. Right hemisphere language functions and schizophrenia: the forgotten hemisphere?
Lindell AK et al. 2006. In your right mind: right hemisphere contributions to language processing and production
Kirsten I et al., Language in the Right Cerebral Hemisphere: Contributions from Reading Studies
ND COOK How the Two Hemispheres Collaborate in the Processing of Language
Helene M van Ettinger-Veenstra et al., 2010. Right-hemispheric brain activation correlates to language performance
Wilke et al., 2006. A combined bootstrap/histogram analysis approach for computing a lateralization index from neuroimaging data
Wilke et al., 2007. LI-tool: A new toolbox to assess lateralization in functional MR-data
LI-Toolbox Univerwsity Children's Hospital Tubingen
石津 希代子, 2011. 両耳分離聴と大脳機能差研究