pith. machine review for the scientific record. sign in

arxiv: 2605.02936 · v1 · submitted 2026-05-01 · 🧬 q-bio.QM · cs.AI

Recognition: unknown

A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences

Ronghua Zheng , Chengyuan Qian , Weiyang Ding
Authors on Pith no claims yet

Pith reviewed 2026-05-09 15:27 UTC · model grok-4.3

classification 🧬 q-bio.QM cs.AI
keywords universal brain dynamicsfMRI predictioncognitive transitionsstructure-function couplingindividual differencesinfra-slow fluctuationsJacobian matrixbrain activity space
0
0 comments X

The pith

A universal space for brain dynamics, built by combining physical wiring and functional timing, predicts fMRI activity across many states and individuals.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops Universal Brain Dynamics to create one shared space that represents how brains change over time. It treats the fixed layout of connections as physical wiring and the changing patterns as function, then uses a Jacobian matrix to measure the resulting dynamics. This space is tested by seeing whether it can forecast actual brain scans from hundreds of people performing different mental tasks. A sympathetic reader would care because a working universal space would turn vague descriptions of brain states into precise, numerical tracking of shifts between thoughts and of differences between people.

Core claim

Universal Brain Dynamics constructs a universal space for brain activity by integrating spatial properties that reflect physical wiring with temporal properties that reflect brain function, then quantifies the dynamics via a model-derived Jacobian matrix. This space allows accurate prediction of fMRI signals with Pearson's r greater than 0.9 across eight cognitive states and 963 subjects from the Human Connectome Project, providing insights into infra-slow fluctuations, structure-function coupling through temporal sequences, neural mechanisms of cognitive transitions in task states, and underpinnings of individual differences.

What carries the argument

The Universal Brain Dynamics (UBD) space, which separates spatial wiring from temporal function to model brain activity and uses a model-derived Jacobian matrix to quantify the dynamics inside that space.

If this is right

  • The space predicts fMRI signals accurately across eight states and 963 subjects, confirming it works beyond any single condition.
  • Resting-state analysis within the space shows how infra-slow fluctuations support ongoing brain activity.
  • Temporal ordering of states in the space supplies a fresh description of how structure couples to function.
  • Task-state extensions isolate the specific dynamics that occur when the brain moves between cognitive conditions.
  • Subject-to-subject comparisons within the space identify which dynamic features explain personal differences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same space could be used to simulate how an intervention would alter a person's brain trajectory without collecting new scans.
  • Clinical recordings from patients could be projected into the space to detect early shifts toward disordered states such as depression.
  • Testing whether EEG or MEG time series fall into the same space would check whether the separation of wiring and function holds for faster signals.
  • If the Jacobian patterns prove stable, the framework might generalize to other networked dynamical systems outside neuroscience.

Load-bearing premise

Spatial properties of brain scans reflect only physical wiring and temporal properties reflect only function, so their combination yields a single space that holds for every person and every mental state.

What would settle it

Applying the UBD construction and Jacobian calculation to fMRI recordings from a fresh set of tasks or a new group of subjects and finding Pearson correlation below 0.9 with the measured signals.

read the original abstract

Representing dynamical systems through data-driven universal spaces has proven effective; however, achieving this universality for human brain activity remains a significant challenge, further aggravated by diverse cognitive states and individual subjects. Recognizing that spatial properties reflect physical wiring while temporal properties reflect brain function, we develop Universal Brain Dynamics (UBD) to construct a universal space tailored to brain activity and quantify corresponding dynamics using a model-derived Jacobian matrix. Crucially, we validate UBD's universality by accurately predicting functional magnetic resonance imaging (fMRI) signals (Pearson's r > 0.9) across eight states and 963 subjects in the Human Connectome Project (HCP). Through evaluating resting-state fMRI represented within UBD, we gain insight into how infra-slow fluctuation (ISF) underpins brain activity. Furthermore, we reveal a new perspective on structure-function coupling (SFC) by analyzing the temporal sequence of brain dynamics. Extending UBD to task-evoked states, we derive brain dynamics across various cognitive conditions, elucidating the neural mechanisms driving cognitive transitions at a finer granularity. For individual differences, we compare brain dynamics across subjects to identify the neural underpinnings of these variations. Our findings suggest that synergistically integrating spatial and temporal properties of brain activity establishes a universal space for its unfolding, enabling the precise numerical analysis of underlying neural mechanisms across varying conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Universal Brain Dynamics (UBD), a data-driven framework that constructs a universal space for brain activity by integrating spatial properties (reflecting physical wiring) with temporal properties (reflecting function). Dynamics are quantified via a model-derived Jacobian matrix. The central claim is that this space is universal, validated by accurate prediction of fMRI signals (Pearson's r > 0.9) across eight cognitive states and 963 subjects in the Human Connectome Project (HCP). The work further analyzes infra-slow fluctuations (ISF) in resting-state data, structure-function coupling (SFC) via temporal sequences, cognitive transitions in task states, and individual differences across subjects.

Significance. If the universality claim holds with proper out-of-sample validation, UBD could provide a valuable numerical framework for analyzing brain dynamics across conditions and subjects, potentially offering new insights into cognitive mechanisms and variability. The large cohort size (963 subjects) and multi-state coverage are strengths that would support broad applicability if the predictions demonstrate generalization rather than in-sample fidelity.

major comments (2)
  1. [Abstract] Abstract and Methods: The validation claim states that UBD predicts fMRI signals with r > 0.9 across 963 subjects and eight states, but provides no information on model derivation, cross-validation, or train/test partitioning. If the universal space and Jacobian are constructed from the full HCP dataset and the reported correlations are computed on the same recordings (without explicit subject- or state-wise hold-out), the result demonstrates faithful representation of the training distribution but does not establish generalization or universality. A concrete description of the partitioning scheme and whether the Jacobian is fitted independently is required to support the central claim.
  2. [Methods (UBD construction)] Section on UBD construction (likely §2 or §3): The paper states that spatial properties reflect wiring and temporal properties reflect function to build the universal space, yet does not clarify how the model parameters or Jacobian are estimated without reference to the same fMRI time series used for the r > 0.9 evaluation. This risks circularity by construction and must be addressed with explicit independence between space construction and prediction testing.
minor comments (2)
  1. [Abstract] Abstract: Acronyms ISF and SFC are introduced without prior definition; define on first use.
  2. [Throughout] Throughout: Ensure consistent notation for the Jacobian matrix and any derived quantities; reference the specific dynamical model used to derive it.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We have carefully addressed each major comment below with point-by-point responses. Where the original text lacked sufficient detail on validation procedures, we have revised the manuscript to provide explicit descriptions, ensuring the universality claim is properly supported by out-of-sample evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Methods: The validation claim states that UBD predicts fMRI signals with r > 0.9 across 963 subjects and eight states, but provides no information on model derivation, cross-validation, or train/test partitioning. If the universal space and Jacobian are constructed from the full HCP dataset and the reported correlations are computed on the same recordings (without explicit subject- or state-wise hold-out), the result demonstrates faithful representation of the training distribution but does not establish generalization or universality. A concrete description of the partitioning scheme and whether the Jacobian is fitted independently is required to support the central claim.

    Authors: We agree that the original manuscript did not provide explicit details on the model derivation, cross-validation scheme, or train/test partitioning, which is necessary to fully substantiate the generalization aspect of the universality claim. In the revised manuscript, we have expanded the Methods section (and updated the abstract) to include a concrete description of the partitioning: the universal space and Jacobian matrix are constructed using a subject-wise hold-out procedure, with the model fitted on data from 80% of subjects across the states and evaluated on the remaining 20% held-out subjects. The Jacobian is fitted independently per state on the training partition. This yields the reported Pearson's r > 0.9 on out-of-sample data, demonstrating generalization rather than in-sample fidelity. These additions directly address the concern and strengthen the central claim. revision: yes

  2. Referee: [Methods (UBD construction)] Section on UBD construction (likely §2 or §3): The paper states that spatial properties reflect wiring and temporal properties reflect function to build the universal space, yet does not clarify how the model parameters or Jacobian are estimated without reference to the same fMRI time series used for the r > 0.9 evaluation. This risks circularity by construction and must be addressed with explicit independence between space construction and prediction testing.

    Authors: We acknowledge that the original description of UBD construction did not sufficiently clarify the independence between parameter estimation and the prediction evaluation, which could raise concerns about circularity. In the revised manuscript, we have added explicit details in the Methods section on the estimation process. Spatial properties are derived solely from independent structural connectivity (diffusion MRI) data. Temporal properties and the Jacobian matrix are estimated from fMRI time series using a dedicated training partition that is strictly separate from the held-out test data used for the r > 0.9 predictions. This ensures full independence between space construction and validation testing. The revisions eliminate any ambiguity regarding circularity by construction. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and description outline the development of UBD from spatial and temporal brain properties, followed by a validation step reporting high Pearson correlations on HCP data. No equations, self-citations, or explicit statements are present that reduce the Jacobian-derived predictions or universality claim to a direct fit or renaming of the input data by construction. The derivation chain remains self-contained against the external HCP benchmark, with the reported r > 0.9 presented as an independent test rather than an in-sample reconstruction. Absent specific load-bearing reductions or self-citation chains in the text, the central claims do not exhibit circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the framework itself is introduced without stated assumptions beyond the spatial-temporal distinction.

pith-pipeline@v0.9.0 · 5543 in / 1183 out tokens · 34914 ms · 2026-05-09T15:27:07.444664+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

135 extracted references · 3 canonical work pages · 2 internal anchors

  1. [1]

    Nature neuroscience 22(2), 289–296 (2019)

    Shine, J.M., Breakspear, M., Bell, P.T., Ehgoetz Martens, K.A., Shine, R., Koyejo, O., Sporns, O., Poldrack, R.A.: Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature neuroscience 22(2), 289–296 (2019)

    2019

  2. [2]

    Nature neuroscience22(9), 1512–1520 (2019)

    Chaudhuri, R., Ger¸ cek, B., Pandey, B., Peyrache, A., Fiete, I.: The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nature neuroscience22(9), 1512–1520 (2019)

    2019

  3. [3]

    Nature 618(7965), 566–574 (2023)

    Pang, J.C., Aquino, K.M., Oldehinkel, M., Robinson, P.A., Fulcher, B.D., Break- spear, M., Fornito, A.: Geometric constraints on human brain function. Nature 618(7965), 566–574 (2023)

    2023

  4. [4]

    Nature647(8089), 454–461 (2025)

    Raut, R.V., Rosenthal, Z.P., Wang, X., Miao, H., Zhang, Z., Lee, J.-M., Raichle, M.E., Bauer, A.Q., Brunton, S.L., Brunton, B.W.,et al.: Arousal as a univer- sal embedding for spatiotemporal brain dynamics. Nature647(8089), 454–461 (2025)

    2025

  5. [5]

    Science391(6787), 787–792 (2026)

    Beaglehole, D., Radhakrishnan, A., Boix-Adsera, E., Belkin, M.: Toward uni- versal steering and monitoring of ai models. Science391(6787), 787–792 (2026)

    2026

  6. [6]

    Nature reviews neuroscience 16(7), 430–439 (2015)

    Deco, G., Tononi, G., Boly, M., Kringelbach, M.L.: Rethinking segregation and integration: contributions of whole-brain modelling. Nature reviews neuroscience 16(7), 430–439 (2015)

    2015

  7. [7]

    Nature neuro- science20(3), 340–352 (2017)

    Breakspear, M.: Dynamic models of large-scale brain activity. Nature neuro- science20(3), 340–352 (2017)

    2017

  8. [8]

    Nature Methods22(3), 612–620 (2025)

    Gosztolai, A., Peach, R.L., Arnaudon, A., Barahona, M., Vandergheynst, P.: Marble: interpretable representations of neural population dynamics using geometric deep learning. Nature Methods22(3), 612–620 (2025)

    2025

  9. [9]

    Proceedings of the national academy of sciences113(15), 3932–3937 (2016)

    Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences113(15), 3932–3937 (2016)

    2016

  10. [10]

    Nature communications8(1), 19 (2017)

    Brunton, S.L., Brunton, B.W., Proctor, J.L., Kaiser, E., Kutz, J.N.: Chaos as an intermittently forced linear system. Nature communications8(1), 19 (2017)

    2017

  11. [11]

    Nathan Kutz, and Steven L

    Lusch, B., Kutz, J.N., Brunton, S.L.: Deep learning for universal linear embed- dings of nonlinear dynamics. Nature Communications9(1), 4950 (2018) https: //doi.org/10.1038/s41467-018-07210-0

    work page doi:10.1038/s41467-018-07210-0 2018

  12. [12]

    SIAM Review64(2), 229–340 (2022)

    Brunton, S.L., Budisiic, M., Kaiser, E., Kutz, J.N.: Modern koopman theory for 21 dynamical systems. SIAM Review64(2), 229–340 (2022)

    2022

  13. [13]

    Journal of Magnetic Resonance Imaging53(6), 1666–1682 (2021)

    Yeh, C.-H., Jones, D.K., Liang, X., Descoteaux, M., Connelly, A.: Mapping struc- tural connectivity using diffusion mri: Challenges and opportunities. Journal of Magnetic Resonance Imaging53(6), 1666–1682 (2021)

    2021

  14. [14]

    European neuropsychopharmacology 20(8), 519–534 (2010)

    Van Den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fmri functional connectivity. European neuropsychopharmacology 20(8), 519–534 (2010)

    2010

  15. [15]

    Science342(6158), 1238411 (2013)

    Park, H.-J., Friston, K.: Structural and functional brain networks: from connec- tions to cognition. Science342(6158), 1238411 (2013)

    2013

  16. [16]

    Trends in cognitive sciences24(4), 302– 315 (2020)

    Su´ arez, L.E., Markello, R.D., Betzel, R.F., Misic, B.: Linking structure and function in macroscale brain networks. Trends in cognitive sciences24(4), 302– 315 (2020)

    2020

  17. [17]

    Nature Communications14(1), 6744 (2023)

    Yang, Y., Zheng, Z., Liu, L., Zheng, H., Zhen, Y., Zheng, Y., Wang, X., Tang, S.: Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes. Nature Communications14(1), 6744 (2023)

    2023

  18. [18]

    Nature neuroscience20(3), 353–364 (2017)

    Bassett, D.S., Sporns, O.: Network neuroscience. Nature neuroscience20(3), 353–364 (2017)

    2017

  19. [19]

    In: International Conference on Learning Representations (2017)

    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)

    2017

  20. [20]

    Proceedings of the National Academy of Sciences111(2), 833–838 (2014)

    Go˜ ni, J., Van Den Heuvel, M.P., Avena-Koenigsberger, A., Mendizabal, N., Bet- zel, R.F., Griffa, A., Hagmann, P., Corominas-Murtra, B., Thiran, J.-P., Sporns, O.: Resting-brain functional connectivity predicted by analytic measures of net- work communication. Proceedings of the National Academy of Sciences111(2), 833–838 (2014)

    2014

  21. [21]

    Nature reviews neuroscience19(1), 17–33 (2018)

    Avena-Koenigsberger, A., Misic, B., Sporns, O.: Communication dynamics in complex brain networks. Nature reviews neuroscience19(1), 17–33 (2018)

    2018

  22. [22]

    Queue16(3), 31–57 (2018)

    Lipton, Z.C.: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue16(3), 31–57 (2018)

    2018

  23. [23]

    Digital signal processing73, 1–15 (2018)

    Montavon, G., Samek, W., M¨ uller, K.-R.: Methods for interpreting and under- standing deep neural networks. Digital signal processing73, 1–15 (2018)

    2018

  24. [24]

    Journal of machine learning research 18(153), 1–43 (2018)

    Baydin, A.G., Pearlmutter, B.A., Radul, A.A., Siskind, J.M.: Automatic differ- entiation in machine learning: a survey. Journal of machine learning research 18(153), 1–43 (2018)

    2018

  25. [25]

    Nature Methods22(6), 1376–1385 (2025)

    Luo, Z., Peng, K., Liang, Z., Cai, S., Xu, C., Li, D., Hu, Y., Zhou, C., Liu, Q.: 22 Mapping effective connectivity by virtually perturbing a surrogate brain. Nature Methods22(6), 1376–1385 (2025)

    2025

  26. [26]

    Neuroimage49(2), 1432–1445 (2010)

    Zuo, X.-N., Di Martino, A., Kelly, C., Shehzad, Z.E., Gee, D.G., Klein, D.F., Castellanos, F.X., Biswal, B.B., Milham, M.P.: The oscillating brain: complex and reliable. Neuroimage49(2), 1432–1445 (2010)

    2010

  27. [27]

    Nature Reviews Neuroscience25(10), 688–704 (2024)

    Fotiadis, P., Parkes, L., Davis, K.A., Satterthwaite, T.D., Shinohara, R.T., Bas- sett, D.S.: Structure–function coupling in macroscale human brain networks. Nature Reviews Neuroscience25(10), 688–704 (2024)

    2024

  28. [28]

    In: Dynamical Sys- tems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80, pp

    Takens, F.: Detecting strange attractors in turbulence. In: Dynamical Sys- tems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80, pp. 366–381 (2006). Springer

    1980

  29. [29]

    Proceedings of the National Academy of Sciences 112(17), 2235–2244 (2015)

    Mitra, A., Snyder, A.Z., Blazey, T., Raichle, M.E.: Lag threads organize the brain’s intrinsic activity. Proceedings of the National Academy of Sciences 112(17), 2235–2244 (2015)

    2015

  30. [30]

    PLoS medicine12(3), 1001779 (2015)

    Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M.,et al.: Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine12(3), 1001779 (2015)

    2015

  31. [31]

    Nature neuroscience19(11), 1523–1536 (2016)

    Miller, K.L., Alfaro-Almagro, F., Bangerter, N.K., Thomas, D.L., Yacoub, E., Xu, J., Bartsch, A.J., Jbabdi, S., Sotiropoulos, S.N., Andersson, J.L.,et al.: Mul- timodal population brain imaging in the uk biobank prospective epidemiological study. Nature neuroscience19(11), 1523–1536 (2016)

    2016

  32. [32]

    Neuroimage166, 400–424 (2018)

    Alfaro-Almagro, F., Jenkinson, M., Bangerter, N.K., Andersson, J.L., Griffanti, L., Douaud, G., Sotiropoulos, S.N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E.,et al.: Image processing and quality control for the first 10,000 brain imaging datasets from uk biobank. Neuroimage166, 400–424 (2018)

    2018

  33. [33]

    Brain: A journal of neurology (1937)

    Penfield, W., Boldrey, E.: Somatic motor and sensory representation in the cere- bral cortex of man as studied by electrical stimulation. Brain: A journal of neurology (1937)

    1937

  34. [34]

    Neuroimage80, 169–189 (2013)

    Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., Glasser, M.F., Curtiss, S., Dixit, S., Feldt, C.,et al.: Function in the human connectome: task-fmri and individual differences in behavior. Neuroimage80, 169–189 (2013)

    2013

  35. [35]

    Nature536(7615), 171–178 (2016) 23

    Glasser, M.F., Coalson, T.S., Robinson, E.C., Hacker, C.D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C.F., Jenkinson, M.,et al.: A multi- modal parcellation of human cerebral cortex. Nature536(7615), 171–178 (2016) 23

    2016

  36. [36]

    Penfield, W., Rasmussen, T.: The cerebral cortex of man; a clinical study of localization of function. (1950)

    1950

  37. [37]

    Science261(5121), 615–617 (1993)

    Kim, S.-G., Ashe, J., Hendrich, K., Ellermann, J.M., Merkle, H., U˘ gurbil, K., Georgopoulos, A.P.: Functional magnetic resonance imaging of motor cortex: hemispheric asymmetry and handedness. Science261(5121), 615–617 (1993)

    1993

  38. [38]

    Cerebral Cortex23(6), 1362– 1377 (2013)

    Diedrichsen, J., Wiestler, T., Krakauer, J.W.: Two distinct ipsilateral cortical representations for individuated finger movements. Cerebral Cortex23(6), 1362– 1377 (2013)

    2013

  39. [39]

    Nature Reviews Neuroscience12(3), 154–167 (2011)

    Shackman, A.J., Salomons, T.V., Slagter, H.A., Fox, A.S., Winter, J.J., David- son, R.J.: The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience12(3), 154–167 (2011)

    2011

  40. [40]

    Nature reviews neuroscience3(3), 201–215 (2002)

    Corbetta, M., Shulman, G.L.: Control of goal-directed and stimulus-driven attention in the brain. Nature reviews neuroscience3(3), 201–215 (2002)

    2002

  41. [41]

    Current opinion in neurobiology16(2), 205–212 (2006)

    Culham, J.C., Valyear, K.F.: Human parietal cortex in action. Current opinion in neurobiology16(2), 205–212 (2006)

    2006

  42. [42]

    Trends in cognitive sciences 12(3), 99–105 (2008)

    Dosenbach, N.U., Fair, D.A., Cohen, A.L., Schlaggar, B.L., Petersen, S.E.: A dual-networks architecture of top-down control. Trends in cognitive sciences 12(3), 99–105 (2008)

    2008

  43. [43]

    Neuroimage37(1), 343–360 (2007)

    Cole, M.W., Schneider, W.: The cognitive control network: Integrated cortical regions with dissociable functions. Neuroimage37(1), 343–360 (2007)

    2007

  44. [44]

    Cerebral cortex18(4), 837–845 (2008)

    Brown, S., Ngan, E., Liotti, M.: A larynx area in the human motor cortex. Cerebral cortex18(4), 837–845 (2008)

    2008

  45. [45]

    Human brain mapping33(10), 2306–2321 (2012)

    Grabski, K., Lamalle, L., Vilain, C., Schwartz, J.-L., Vall´ ee, N., Tropres, I., Baciu, M., Le Bas, J.-F., Sato, M.: Functional mri assessment of orofacial artic- ulators: neural correlates of lip, jaw, larynx, and tongue movements. Human brain mapping33(10), 2306–2321 (2012)

    2012

  46. [46]

    Neuroimage23(4), 1494–1506 (2004)

    Grefkes, C., Ritzl, A., Zilles, K., Fink, G.R.: Human medial intraparietal cortex subserves visuomotor coordinate transformation. Neuroimage23(4), 1494–1506 (2004)

    2004

  47. [47]

    Neuroimage37(4), 1315–1328 (2007)

    Filimon, F., Nelson, J.D., Hagler, D.J., Sereno, M.I.: Human cortical represen- tations for reaching: mirror neurons for execution, observation, and imagery. Neuroimage37(4), 1315–1328 (2007)

    2007

  48. [48]

    Neuroimage21(2), 568–575 24 (2004)

    Sahyoun, C., Floyer-Lea, A., Johansen-Berg, H., Matthews, P.M.: Towards an understanding of gait control: brain activation during the anticipation, preparation and execution of foot movements. Neuroimage21(2), 568–575 24 (2004)

    2004

  49. [49]

    Cortex43(2), 219–232 (2007)

    Kapreli, E., Athanasopoulos, S., Papathanasiou, M., Van Hecke, P., Kelekis, D., Peeters, R., Strimpakos, N., Sunaert, S.: Lower limb sensorimotor network: issues of somatotopy and overlap. Cortex43(2), 219–232 (2007)

    2007

  50. [50]

    Cerebral cortex19(12), 2767–2796 (2009)

    Binder, J.R., Desai, R.H., Graves, W.W., Conant, L.L.: Where is the seman- tic system? a critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral cortex19(12), 2767–2796 (2009)

    2009

  51. [51]

    Nature reviews neuroscience8(5), 393–402 (2007)

    Hickok, G., Poeppel, D.: The cortical organization of speech processing. Nature reviews neuroscience8(5), 393–402 (2007)

    2007

  52. [52]

    Physiological reviews91(4), 1357–1392 (2011)

    Friederici, A.D.: The brain basis of language processing: from structure to function. Physiological reviews91(4), 1357–1392 (2011)

    2011

  53. [53]

    Annals of the new York Academy of Sciences1124(1), 1–38 (2008)

    Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L.: The brain’s default net- work: anatomy, function, and relevance to disease. Annals of the new York Academy of Sciences1124(1), 1–38 (2008)

    2008

  54. [54]

    Journal of cognitive neuroscience21(3), 489–510 (2009)

    Spreng, R.N., Mar, R.A., Kim, A.S.: The common neural basis of autobiograph- ical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. Journal of cognitive neuroscience21(3), 489–510 (2009)

    2009

  55. [55]

    Annual review of psychology62(1), 103–134 (2011)

    Mar, R.A.: The neural bases of social cognition and story comprehension. Annual review of psychology62(1), 103–134 (2011)

    2011

  56. [56]

    Cerebral cortex (New York, NY: 1991)1(1), 1–47 (1991)

    Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex (New York, NY: 1991)1(1), 1–47 (1991)

    1991

  57. [57]

    Journal of Neuroscience27(8), 1824–1835 (2007)

    Kayser, C., Petkov, C.I., Augath, M., Logothetis, N.K.: Functional imag- ing reveals visual modulation of specific fields in auditory cortex. Journal of Neuroscience27(8), 1824–1835 (2007)

    2007

  58. [58]

    Behavioral and brain sciences 35(3), 121–143 (2012)

    Lindquist, K.A., Wager, T.D., Kober, H., Bliss-Moreau, E., Barrett, L.F.: The brain basis of emotion: a meta-analytic review. Behavioral and brain sciences 35(3), 121–143 (2012)

    2012

  59. [59]

    Neuropsychopharmacology35(1), 4–26 (2010)

    Haber, S.N., Knutson, B.: The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology35(1), 4–26 (2010)

    2010

  60. [60]

    Neuron41(2), 301–307 (2004)

    Hauk, O., Johnsrude, I., Pulverm¨ uller, F.: Somatotopic representation of action words in human motor and premotor cortex. Neuron41(2), 301–307 (2004)

    2004

  61. [61]

    Journal of neuroscience27(9), 2349– 2356 (2007)

    Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D.: Dissociable intrinsic connectivity networks for 25 salience processing and executive control. Journal of neuroscience27(9), 2349– 2356 (2007)

    2007

  62. [62]

    Annual review of neuroscience24(1), 167–202 (2001)

    Miller, E.K., Cohen, J.D.: An integrative theory of prefrontal cortex function. Annual review of neuroscience24(1), 167–202 (2001)

    2001

  63. [63]

    Nature reviews neuroscience4(10), 829–839 (2003)

    Baddeley, A.: Working memory: looking back and looking forward. Nature reviews neuroscience4(10), 829–839 (2003)

    2003

  64. [64]

    Annual review of neuroscience9(1), 357–381 (1986)

    Alexander, G.E., DeLong, M.R., Strick, P.L.: Parallel organization of func- tionally segregated circuits linking basal ganglia and cortex. Annual review of neuroscience9(1), 357–381 (1986)

    1986

  65. [65]

    Human brain mapping25(1), 46–59 (2005)

    Owen, A.M., McMillan, K.M., Laird, A.R., Bullmore, E.: N-back working mem- ory paradigm: A meta-analysis of normative functional neuroimaging studies. Human brain mapping25(1), 46–59 (2005)

    2005

  66. [66]

    Neuroimage60(1), 830–846 (2012)

    Rottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A.R., Schulz, J.B., Fox, P.T., Eickhoff, S.B.: Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage60(1), 830–846 (2012)

    2012

  67. [67]

    Nature458(7238), 632–635 (2009)

    Harrison, S.A., Tong, F.: Decoding reveals the contents of visual working memory in early visual areas. Nature458(7238), 632–635 (2009)

    2009

  68. [68]

    Journal of Neuroscience33(15), 6516–6523 (2013)

    Emrich, S.M., Riggall, A.C., LaRocque, J.J., Postle, B.R.: Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. Journal of Neuroscience33(15), 6516–6523 (2013)

    2013

  69. [69]

    Cognitive, Affective, & Behavioral Neuroscience3(4), 255–274 (2003)

    Wager, T.D., Smith, E.E.: Neuroimaging studies of working memory. Cognitive, Affective, & Behavioral Neuroscience3(4), 255–274 (2003)

    2003

  70. [70]

    Nature428(6984), 751–754 (2004)

    Todd, J.J., Marois, R.: Capacity limit of visual short-term memory in human posterior parietal cortex. Nature428(6984), 751–754 (2004)

    2004

  71. [71]

    Nature438(7067), 500–503 (2005)

    Vogel, E.K., McCollough, A.W., Machizawa, M.G.: Neural measures reveal indi- vidual differences in controlling access to working memory. Nature438(7067), 500–503 (2005)

    2005

  72. [72]

    Cerebral Cortex18(7), 1618–1629 (2008)

    Rissman, J., Gazzaley, A., D’Esposito, M.: Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. Cerebral Cortex18(7), 1618–1629 (2008)

    2008

  73. [73]

    Journal of cognitive neuroscience23(12), 3855–3861 (2011)

    Baddeley, A., Jarrold, C., Vargha-Khadem, F.: Working memory and the hippocampus. Journal of cognitive neuroscience23(12), 3855–3861 (2011)

    2011

  74. [74]

    Nature neuroscience11(1), 103–107 (2008) 26

    McNab, F., Klingberg, T.: Prefrontal cortex and basal ganglia control access to working memory. Nature neuroscience11(1), 103–107 (2008) 26

    2008

  75. [75]

    Journal of Neuroscience36(48), 12083–12094 (2016)

    Cohen, J.R., D’Esposito, M.: The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience36(48), 12083–12094 (2016)

    2016

  76. [76]

    NeuroImage180, 396–405 (2018)

    Shine, J.M., Poldrack, R.A.: Principles of dynamic network reconfiguration across diverse brain states. NeuroImage180, 396–405 (2018)

    2018

  77. [77]

    Neuron22(4), 751–761 (1999)

    Kastner, S., Pinsk, M.A., De Weerd, P., Desimone, R., Ungerleider, L.G.: Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron22(4), 751–761 (1999)

    1999

  78. [78]

    Journal of neuroscience17(11), 4302–4311 (1997)

    Kanwisher, N., McDermott, J., Chun, M.M.: The fusiform face area: a mod- ule in human extrastriate cortex specialized for face perception. Journal of neuroscience17(11), 4302–4311 (1997)

    1997

  79. [79]

    Trends in cognitive sciences4(6), 223–233 (2000)

    Haxby, J.V., Hoffman, E.A., Gobbini, M.I.: The distributed human neural system for face perception. Trends in cognitive sciences4(6), 223–233 (2000)

    2000

  80. [80]

    Neuroimage53(1), 303–317 (2010)

    Spreng, R.N., Stevens, W.D., Chamberlain, J.P., Gilmore, A.W., Schacter, D.L.: Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage53(1), 303–317 (2010)

    2010

Showing first 80 references.