Recent work supports the idea that the neural processing is organized over multiple timescales (Cavanagh et al., 2020; Hasson et al., 2008; Honey et al., 2012; Kiebel et al., 2008; Murray et al., 2014; Raut et al., 2020; Soltani et al., 2021). Hierarchical temporal dynamics are a fundamental computational property of the brain—fast-changing sensory input is encoded by the fast dynamics of sensory areas, which is in turn integrated into temporally extended representations encoded by the slower dynamics of higher-order areas (Cavanagh et al., 2020; Raut et al., 2020; Soltani et al., 2021). Temporal integration and information processing over multiple timescales are crucial for understanding how the brain deals with the complexity of the real world (Zimmermann et al., 2018); hence, understanding whole-brain temporal dynamics is highly important.

As one moves from sensory to associative areas, neural processing reflects a hierarchy of progressively longer ‘temporal receptive windows’ (TRWs)—defined as the length of time during which an area accumulates information during task performance (Hasson et al., 2008). In other words, the range of the TRW of each area reflects its functional role, with sensory areas having short TRWs, to enable fast sensory processing, and associative areas having longer TRWs, allowing them to process information unfolding over longer periods of time. Not only does neural processing display these timescales during task performance, but we now know that temporal specialization arises from a region’s intrinsic dynamics, that is, temporal dynamics that are present even in the absence of direct sensory stimulation. This was first described by Murray et al., 2014 analyzing action potentials of single neurons in the macaque. They showed that timescales of baseline neuronal activity vary across cortical areas, revealing a hierarchical progression from fast timescales in sensory areas, to slower timescales in association areas. These intrinsic fluctuations in the neural signal, also described as intrinsic neural timescales (INTs), are thought to set the duration over which brain regions integrate inputs (Murray et al., 2014). They are hypothesized to be the result of multiple neuronal mechanisms, including local dynamics and recurrent connectivity (Murray et al., 2014; Soltani et al., 2021; Watanabe et al., 2019; Demirtaş et al., 2019).

Additional studies have replicated and extended the INT hierarchy, reinforcing the idea that brain regions exhibit unique timescales in their intrinsic activity, which reflect functional specialization in terms of temporal integration (Cavanagh et al., 2016; Cirillo et al., 2018; Fascianelli et al., 2019; Murray et al., 2014; Ogawa and Komatsu, 2010; Rossi-Pool et al., 2021; Maisson et al., 2021). The concept of INT hierarchy has even been extended to subcortical regions—specifically the basal ganglia (Nougaret et al., 2021). Moreover, a large-scale network model, based on anatomical connectivity (AC) and the strength of local recurrent connectivity, has been used to successfully reproduce the temporal hierarchy (Chaudhuri et al., 2015). This suggests that AC is closely related to INTs.

Alterations in hierarchical INT patterns can result in cognitive and behavioral abnormalities (Watanabe et al., 2019; Wengler et al., 2020; Zilio et al., 2021). For example, abnormal INT patterns have been associated with core symptoms of high-functioning individuals with Autism Spectrum Disorder (Watanabe et al., 2019). Likewise, delusions and hallucinations are associated with INT alterations at different hierarchical levels, leading to symptom-specific changes in hierarchical INT gradients (Wengler et al., 2020). Hence, investigations into INTs have the potential to clarify hierarchical dysfunction in whole-brain dynamics in neuropsychiatric disorders.

Traditionally, INTs have been investigated using invasive electrophysiology, which is not suited to uncover whole-brain dynamics. It is also challenging to collect in humans, particularly for large data sets (as would be needed to assess differences across populations). One way to overcome this problem is using functional magnetic resonance imaging (fMRI), which offers whole-brain coverage and can be performed across species and in larger sample sizes. However, it is not yet clear whether hemodynamic INTs derived from fMRI reflect similar neural phenomena as those derived from electrophysiology. To clarify this issue, electrophysiological and hemodynamic INTs must be compared directly within the same species. To that end, recent advancements in nonhuman primate (NHP) neuroimaging, based on the use of very high magnetic fields (10.5 T) for significantly boosting signal-to-noise ratios (SNRs) (Lagore et al., 2021; Yacoub et al., 2020), make it possible to study hemodynamic INTs in the macaque brain. Moreover, taking advantage of the whole-brain coverage of fMRI, we are investigating INT topography. This is important since more recent work has shown that there is significant heterogeneity within regions (Cavanagh et al., 2020; Cirillo et al., 2018; Nougaret et al., 2021), even though initially INTs were assigned to cortical regions as a whole.

In addition to INTs, gradients of functional connectivity (FC) can also describe the functional architecture of neural processing (Haak et al., 2018; Vos de Wael et al., 2020). Gradient approaches map macroscale brain features to low-dimensional manifold representations (Vos de Wael et al., 2020). Gradient mapping has the potential to uncover overlapping functional topographies by treating the high-dimensional connectivity structure as a spatial variance decomposition problem. Notably, this approach finds patterns in the high-dimensional FC similarity structure that describe the spatial direction of maximum FC variance. For example, areas in the early visual cortex are known to exhibit at least two gradual modes of functional change (i.e., eccentricity and polar angle maps), which can be successfully retrieved from resting-state FC via gradient mapping (Haak et al., 2018). The existence of multiple overlapping principles of topographic organization is likely a general property of the whole brain (Haak et al., 2018; Harvey et al., 2020; Jbabdi et al., 2013; Marquand et al., 2017; Przezdzik et al., 2019). Here, we carry out an investigation into the multidimensional functional architecture of the macaque brain by probing its spatial and temporal intrinsic dynamics. This allows us to achieve several significant goals. First, we are able to relate fMRI hemodynamic timescales to the gold standards of electrophysiology within the same species. Second, with these results in hand, we are able to show that one facet of the high-dimensional FC topography of any region in the brain is closely related to INT topography. We show that the hierarchical intrinsic dynamics of neural processing manifest into gradients of INTs and FC which follow the same gradual topographical arrangement. In sum, we reliably describe one facet of the macroscale functional organization of the brain, with respect to spatial and temporal dynamics.

Temporal integration is a fundamental property of neural processing (Cavanagh et al., 2016; Hasson et al., 2008; Honey et al., 2012; Soltani et al., 2021), both during task performance and in the absence of external stimulation (Cirillo et al., 2018; Hasson et al., 2008; Murray et al., 2014; Raut et al., 2020; Ito et al., 2020). However, until this point, there have been major gaps in investigating temporal integration across the brain, across species, and across populations. This is because, traditionally, temporal integration has been studied primarily using single-unit electrophysiology in NHPs (see Gao et al., 2020). Although there is some evidence that BOLD signals from fMRI may allow for whole-brain interrogation of timescales in humans (Watanabe et al., 2019; Wengler et al., 2020), such an approach had not yet been validated. Here, we focused on investigating whole-brain INT and FC topographies at different levels of description, to first validate, within-species, the study of INTs using BOLD fMRI, and then investigate the relationship between temporal and spatial dynamics of the resting brain with ultrahigh resolution fMRI across the whole macaque brain.

We have demonstrated the feasibility of estimating INTs from fMRI in the NHP brain. We did so by showing that hemodynamic INT hierarchies closely match those derived from NHP electrophysiology. While some recent human neuroimaging studies have investigated whole-brain hemodynamic INTs (Raut et al., 2020; Watanabe et al., 2019; Wengler et al., 2020), temporal hierarchies derived from electrophysiology have not been replicated via fMRI, nor within species. Furthermore, in a previous paper, INTs derived from fMRI data in human subjects were elegantly validated with EEG (Watanabe et al., 2019). The present findings are in agreement with these results, finding slower and faster INTs in frontoparietal areas and sensory-related areas, respectively. This points toward cross-species homologies in cortical INTs, hence, laying the groundwork for future translational work. Nevertheless, the step presented here is essential, because we were able to demonstrate the equivalence of the fMRI estimates of INT hierarchies with single-neuron spiking results within the same species. This finding provides validation for the complementary use of hemodynamic and electrophysiological INTs. Using fMRI in this context has several advantages. First, it can be performed on the whole brain. Second, it can easily be employed across different human populations in the future, dramatically expanding the possible uses of INT investigations.

We did observe minor deviations from electrophysiological INTs in our data. These may be in large part attributable to a high degree of INT heterogeneity within individual anatomical regions. This observation is in line with previous reports of heterogeneity within neural populations in single-unit recordings (Cirillo et al., 2018; Nougaret et al., 2021). This suggests that averaging INTs within anatomical regions might not provide a full description of macroscale organization.

It is worth noting that the current study could not resolve the exact correspondence between the time constants estimated from spiking activity and BOLD. The relationship between neural activity and the BOLD signal has been revealed via simultaneous electrophysiological-fMRI recording—that is, a spatially localized increase in the fMRI signal directly and monotonically reflects an increase in neural activity, with the mean onset time of the BOLD signal relative to the neural activation being 2 s (Logothetis et al., 2001). As a result of sensory stimulation, BOLD signal is delayed by ~2–3 s, followed by a ramp of 6–12 s to a plateau or peak value. Local field potentials (LFPs), and to a lesser extent multiunit activity, can predict the BOLD signal when convolved with the neuro-vascular impulse response function. In sum, the hemodynamic response is roughly a low-pass-filtered expression of the total neural activity; with LFPs, however, having a greater contribution to the signal. This has several implications for the interpretation of hemodynamic timescales: (1) they are more likely to reflect timescales related to LFPs rather than spiking activity; (2) the time constants estimated from spiking activity might not be linearly related to those estimated from fMRI. A further complication for the interpretability of hemodynamic timescales is that INTs have only been estimated from spiking activity, but not LFPs. The exact relationship between timescales derived from spiking activity, LFPs and the BOLD signal remains an empirical question. We hypothesize that hemodynamic timescales generally increase monotonically with those estimated from electrophysiology. In that respect, Watanabe et al., 2019 bring preliminary evidence—that is, timescales derived from electroencephalography (EEG) and fMRI have been shown to differ by 2 orders of magnitude. This is reasonable considering the BOLD time course—when convolved with the hemodynamic response function, the INTs estimated from EEG were similar in magnitude to those estimated from fMRI. Nevertheless, we show that temporal hierarchies are a general principle of organization, irrespective of the method used to estimate them.

To further demonstrate the potential of INTs estimated from fMRI, we extend the current literature by providing a finer description of the hierarchy of visual INTs compared to those previously described with electrophysiology (Murray et al., 2014). Remarkably, areas along the dorsal visual pathway follow the INT hierarchical ordering predicted by functional specialization and connectivity (Kravitz et al., 2011), with primary visual areas showing the fastest timescales and prefrontal areas showing the longest. Our findings bring further evidence that the dorsal pathway is not a unitary system, but rather subdivided into multiple parallel pathways (Binkofski and Buxbaum, 2013; Kravitz et al., 2011). This demonstrates the importance of fine-grained, large-scale descriptions of the brain for uncovering multiple hierarchies.

We investigated timescales and connectivity topographies at different levels of description to probe whether one aspect of the multidimensional functional architecture of the brain is related to temporal structure. We show that INT topographies are closely related to the overlapping FC gradient dimensions. Previous studies have demonstrated that FC gradient dimensions in sensory cortex corresponded with known organizational principles such as eccentricity and polar angle (Haak et al., 2018). Moreover, FC gradients followed M1’s somatotopic map. Our findings put forward a significantly more unified framework that generalizes over the entire brain. INTs form a topographic organizational principle ranging from sensory to associative cortex. While other organizational principles can and likely always will be represented across topographic networks of the brain, intrinsic timescales appear to be special in their general extent.

It is noteworthy that there are crucial methodological differences between the gradient and INT estimation approaches: (1) the gradient estimation pipeline consists of multiple steps to reduce the dimensionality of the data, and consequently, the amount of noise (e.g., the FC matrix was made sparse; applying the cosine similarity kernel to build an affinity matrix; and applying a nonlinear dimensionality reduction technique to estimate the gradients); (2) the gradients were estimated bilaterally. In comparison to gradients, where the spatial correlation between the voxels’ time series is just the initial step in the pipeline, the INT measure consists of the temporal autocorrelation of individual voxels. No extra steps have been taken for noise reduction. Hence, while gradients are the result of a complex processing pipeline, INTs are a relatively simple calculation. As a result, there is a higher amount of variability in the INT maps which could potentially lead to the underestimation of the FC-INT relationship. With respect to the second point, the symmetry of the gradients could be a byproduct of estimating the gradients bilaterally. Other studies e.g., Haak et al., 2018; Przezdzik et al., 2019 have derived gradients for each hemisphere individually and no major asymmetries have been reported. We estimated the gradients bilaterally because of an issue that is inherent with experimental ultrahigh fields. Specifically, RF transmit field (or B1) inhomogeneities lead to asymmetric signal intensities. We were worried that these effects might introduce asymmetries between the two hemispheres. We have reason to believe that this issue is partially being taken care of by estimating the gradients bilaterally, but the INTs are still susceptible to these effects. This could also result in the underestimation of the FC-INT relationship since there are visible asymmetries in the INT maps.

One potential limitation of the current study is using anesthetized subjects. Although obtaining high-resolution images of awake NHPs at ultrahigh fields would be ideal, it is still an enormous technical challenge that the field is working to overcome. Using our low anesthesia strategy, we were able to obtain a relatively large sample size of NHPs (because extensive training was not required). It is important to note that it was previously shown that our anesthesia protocol demonstrates no observable biases to whole-brain connectivity analysis (Yacoub et al., 2020). Moreover, it has been shown that this protocol has a relatively low impact on reproducibility (Autio et al., 2021). To our knowledge, the largest effect of vasodilative anesthetics (such as isoflurane) is an overall blunting of responses which is counteracted by ultrahigh field MRI. Finally, besides the possible effects on the hemodynamic response, anesthesia introduces a crucial difference between the current study and previous research—while in previous studies, it was not controlled for the subjects engaging in various cognitive processes, in anesthetized subjects, this confound is potentially removed. Overall, future research should replicate current results to exclude a possible effect of anesthesia.

We show that resting-state fMRI in NHPs is crucial for bridging the gap between human and animal models, but also across the various techniques of investigating the brain. Nevertheless, macaques have a disadvantage as a model organism for fMRI because of their small head size (~6% of the human brain) which makes the use of standard field strength magnets problematic for acquiring high-resolution data. As a result, collecting high-resolution, high SNR whole-brain data has been extremely difficult (Yacoub et al., 2020). We show here that one way of overcoming this problem is to use ultrahigh field strengths in combination with high channel count receiver arrays (Lagore et al., 2021; Uğurbil et al., 2019; Yacoub et al., 2020). Ultrahigh magnetic fields not only enhance SNR but also increase the BOLD contrast, especially for the microvascular BOLD contributions—which leads to mapping signals with higher precision to sites of neuronal activity (Ugurbil, 2016; Uğurbil, 2018). In conclusion, our unique approach made it possible to obtain whole-brain high-resolution data to investigate FC and INTs across both cortical and subcortical structures.

INTs, like AC and FC, are one way of characterizing the organization of the brain. Critically, combined maps of all three will require a nonhuman animal model such as the macaque, as AC cannot be directly measured in humans (Grier et al., 2020). It is clear that AC, FC, and INTs are related, although the precise nature of the relationships is somewhat speculative. For instance, vertex FC strength is related to human INTs estimated from fMRI (Raut et al., 2020). Our own results show that cortico-striatal INTs and FC gradients broadly track AC. AC establishes the pathways which the signals underpinning FC use. However, the fact that the pathways exist and are used does not tell us what types of information transformations occur along the way. Through cascaded processing, longer and longer TRWs can be built, measured as longer INTs. Thus, we expect that the space of potential INTs is constrained by both AC and FC. Future exploration of these organizational principles, including using pharmacological interventions and electrical stimulation, will likely clarify the causal relationships at play.

We show that INTs are topographically organized along FC gradients throughout the NHP brain. Although we focused our analysis on the frontal and parietal lobes, and the striatum, we also showed that a whole-brain analysis recapitulates the same conclusion: resting-state temporal and spatial dynamics are closely related. Future investigations should examine this relationship in other cortical and subcortical brain areas. A promising avenue would be to examine the FC-INT link throughout the brain across different levels of parcellation (and different types of parcellations). While we show that intrinsic timescales are related to FC, other studies have related them to other anatomical and functional markers (e.g., see Ito et al., 2020; Watanabe et al., 2019). Hence, further investigations and careful experimentation are needed to understand (1) how temporal dynamics relate to other functional and anatomical characteristics across the brain; (2) their functional relevance and translational potential.

The functional connectivity gradient maps and the timescale maps have been uploaded to figshare. Functional connectivity gradients: https://doi.org/10.6084/m9.figshare.19189331 Intrinsic neural timescales: https://doi.org/10.6084/m9.figshare.19197026.

1. 1. Manea AMG
   2. Zilverstand A
   3. Uǧurbil K
   4. Heilbronner SR
   5. Zimmermann J

   (2021) figshare

   Resting-state functional connectivity gradients and intrinsic neural timescales.

   https://doi.org/10.6084/m9.figshare.19189331

1. 1. Alexander GE
   2. DeLong MR
   3. Strick PL

   (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex

   Annual Review of Neuroscience 9:357–381.

   https://doi.org/10.1146/annurev.ne.09.030186.002041

   • PubMed
   • Google Scholar
2. 1. Autio JA
   2. Zhu Q
   3. Li X
   4. Glasser MF
   5. Schwiedrzik CM
   6. Fair DA
   7. Zimmermann J
   8. Yacoub E
   9. Menon RS
   10. Van Essen DC
   11. Hayashi T
   12. Russ B
   13. Vanduffel W

   (2021) Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection

   NeuroImage 236:118082.

   https://doi.org/10.1016/j.neuroimage.2021.118082

   • PubMed
   • Google Scholar
3. 1. Avants BB
   2. Tustison NJ
   3. Wu J
   4. Cook PA
   5. Gee JC

   (2011) An open source multivariate framework for n-tissue segmentation with evaluation on public data

   Neuroinformatics 9:381–400.

   https://doi.org/10.1007/s12021-011-9109-y

   • PubMed
   • Google Scholar
4. 1. Avants BB
   2. Tustison NJ
   3. Stauffer M
   4. Song G
   5. Wu B
   6. Gee JC

   (2014) The Insight ToolKit image registration framework

   Frontiers in Neuroinformatics 8:44.

   https://doi.org/10.3389/fninf.2014.00044

   • PubMed
   • Google Scholar
5. 1. Badre D

   (2008) Cognitive control, hierarchy, and the rostro-caudal organization of the frontal lobes

   Trends in Cognitive Sciences 12:193–200.

   https://doi.org/10.1016/j.tics.2008.02.004

   • PubMed
   • Google Scholar
6. 1. Binkofski F
   2. Buxbaum LJ

   (2013) Two action systems in the human brain

   Brain and Language 127:222–229.

   https://doi.org/10.1016/j.bandl.2012.07.007

   • PubMed
   • Google Scholar
7. 1. Carmichael ST
   2. Price JL

   (1994) Architectonic subdivision of the orbital and medial prefrontal cortex in the macaque monkey

   The Journal of Comparative Neurology 346:366–402.

   https://doi.org/10.1002/cne.903460305

   • PubMed
   • Google Scholar
8. 1. Carmichael ST
   2. Price JL

   (1996) Connectional networks within the orbital and medial prefrontal cortex of macaque monkeys

   The Journal of Comparative Neurology 371:179–207.

   https://doi.org/10.1002/(SICI)1096-9861(19960722)371:2<179::AID-CNE1>3.0.CO;2-#

   • PubMed
   • Google Scholar
9. 1. Cavanagh SE
   2. Wallis JD
   3. Kennerley SW
   4. Hunt LT

   (2016) Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

   eLife 5:e18937.

   https://doi.org/10.7554/eLife.18937

   • PubMed
   • Google Scholar
10. 1. Cavanagh SE
    2. Hunt LT
    3. Kennerley SW

    (2020) A diversity of intrinsic timescales underlie neural computations

    Frontiers in Neural Circuits 14:615626.

    https://doi.org/10.3389/fncir.2020.615626

    • PubMed
    • Google Scholar
11. 1. Chaudhuri R
    2. Knoblauch K
    3. Gariel MA
    4. Kennedy H
    5. Wang XJ

    (2015) A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Neuron 88:419–431.

    https://doi.org/10.1016/j.neuron.2015.09.008

    • PubMed
    • Google Scholar
12. 1. Cirillo R
    2. Fascianelli V
    3. Ferrucci L
    4. Genovesio A

    (2018) Neural Intrinsic Timescales in the Macaque Dorsal Premotor Cortex Predict the Strength of Spatial Response Coding

    IScience 10:203–210.

    https://doi.org/10.1016/j.isci.2018.11.033

    • PubMed
    • Google Scholar
13. 1. Cox RW

    (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

    Computers and Biomedical Research, an International Journal 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • PubMed
    • Google Scholar
14. 1. Demirtaş M
    2. Burt JB
    3. Helmer M
    4. Ji JL
    5. Adkinson BD
    6. Glasser MF
    7. Van Essen DC
    8. Sotiropoulos SN
    9. Anticevic A
    10. Murray JD

    (2019) Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics

    Neuron 101:1181–1194.

    https://doi.org/10.1016/j.neuron.2019.01.017

    • PubMed
    • Google Scholar
15. 1. Fascianelli V
    2. Tsujimoto S
    3. Marcos E
    4. Genovesio A

    (2019) Autocorrelation Structure in the Macaque Dorsolateral, But not Orbital or Polar, Prefrontal Cortex Predicts Response-Coding Strength in a Visually Cued Strategy Task

    Cerebral Cortex (New York, N.Y 29:230–241.

    https://doi.org/10.1093/cercor/bhx321

    • PubMed
    • Google Scholar
16. 1. Friedman DP
    2. Aggleton JP
    3. Saunders RC

    (2002) Comparison of hippocampal, amygdala, and perirhinal projections to the nucleus accumbens: combined anterograde and retrograde tracing study in the Macaque brain

    The Journal of Comparative Neurology 450:345–365.

    https://doi.org/10.1002/cne.10336

    • PubMed
    • Google Scholar
17. 1. Gao R
    2. van den Brink RL
    3. Pfeffer T
    4. Voytek B

    (2020) Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

    eLife 9:e61277.

    https://doi.org/10.7554/eLife.61277

    • PubMed
    • Google Scholar
18. 1. Goodale MA
    2. Milner AD

    (1992) Separate visual pathways for perception and action

    Trends in Neurosciences 15:20–25.

    https://doi.org/10.1016/0166-2236(92)90344-8

    • PubMed
    • Google Scholar
19. 1. Goodale MA
    2. Meenan JP
    3. Bülthoff HH
    4. Nicolle DA
    5. Murphy KJ
    6. Racicot CI

    (1994) Separate neural pathways for the visual analysis of object shape in perception and prehension

    Current Biology 4:604–610.

    https://doi.org/10.1016/s0960-9822(00)00132-9

    • PubMed
    • Google Scholar
20. 1. Grier MD
    2. Zimmermann J
    3. Heilbronner SR

    (2020) Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril

    Biological Psychiatry. Cognitive Neuroscience and Neuroimaging 5:846–854.

    https://doi.org/10.1016/j.bpsc.2020.04.009

    • PubMed
    • Google Scholar
21. 1. Haak KV
    2. Marquand AF
    3. Beckmann CF

    (2018) Connectopic mapping with resting-state fMRI

    NeuroImage 170:83–94.

    https://doi.org/10.1016/j.neuroimage.2017.06.075

    • PubMed
    • Google Scholar
22. 1. Haak KV
    2. Beckmann CF

    (2020) Understanding brain organisation in the face of functional heterogeneity and functional multiplicity

    NeuroImage 220:117061.

    https://doi.org/10.1016/j.neuroimage.2020.117061

    • PubMed
    • Google Scholar
23. 1. Haber SN
    2. Knutson B

    (2010) The reward circuit: linking primate anatomy and human imaging

    Neuropsychopharmacology 35:4–26.

    https://doi.org/10.1038/npp.2009.129

    • PubMed
    • Google Scholar
24. 1. Haber SN

    (2016) Corticostriatal circuitry

    Dialogues in Clinical Neuroscience 18:7–21.

    https://doi.org/10.31887/DCNS.2016.18.1/shaber

    • PubMed
    • Google Scholar
25. 1. Hallquist MN
    2. Hwang K
    3. Luna B

    (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity

    NeuroImage 82:208–225.

    https://doi.org/10.1016/j.neuroimage.2013.05.116

    • PubMed
    • Google Scholar
26. 1. Hartig R
    2. Glen D
    3. Jung B
    4. Logothetis NK
    5. Paxinos G
    6. Garza-Villarreal EA
    7. Messinger A
    8. Evrard HC

    (2021) The Subcortical Atlas of the Rhesus Macaque (SARM) for neuroimaging

    NeuroImage 235:117996.

    https://doi.org/10.1016/j.neuroimage.2021.117996

    • PubMed
    • Google Scholar
27. 1. Harvey BM
    2. Dumoulin SO
    3. Fracasso A
    4. Paul JM

    (2020) A Network of Topographic Maps in Human Association Cortex Hierarchically Transforms Visual Timing-Selective Responses

    Current Biology 30:1424–1434.

    https://doi.org/10.1016/j.cub.2020.01.090

    • PubMed
    • Google Scholar
28. 1. Hasson U
    2. Yang E
    3. Vallines I
    4. Heeger DJ
    5. Rubin N

    (2008) A hierarchy of temporal receptive windows in human cortex

    The Journal of Neuroscience 28:2539–2550.

    https://doi.org/10.1523/JNEUROSCI.5487-07.2008

    • PubMed
    • Google Scholar
29. 1. Honey CJ
    2. Thesen T
    3. Donner TH
    4. Silbert LJ
    5. Carlson CE
    6. Devinsky O
    7. Doyle WK
    8. Rubin N
    9. Heeger DJ
    10. Hasson U

    (2012) Slow cortical dynamics and the accumulation of information over long timescales

    Neuron 76:423–434.

    https://doi.org/10.1016/j.neuron.2012.08.011

    • PubMed
    • Google Scholar
30. 1. Ito T
    2. Hearne LJ
    3. Cole MW

    (2020) A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

    NeuroImage 221:117141.

    https://doi.org/10.1016/j.neuroimage.2020.117141

    • PubMed
    • Google Scholar
31. 1. Jbabdi S
    2. Sotiropoulos SN
    3. Behrens TE

    (2013) The topographic connectome

    Current Opinion in Neurobiology 23:207–215.

    https://doi.org/10.1016/j.conb.2012.12.004

    • PubMed
    • Google Scholar
32. 1. Jenkinson M
    2. Beckmann CF
    3. Behrens TEJ
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
33. 1. Jung B
    2. Taylor PA
    3. Seidlitz J
    4. Sponheim C
    5. Perkins P
    6. Ungerleider LG
    7. Glen D
    8. Messinger A

    (2021) A comprehensive macaque fMRI pipeline and hierarchical atlas

    NeuroImage 235:117997.

    https://doi.org/10.1016/j.neuroimage.2021.117997

    • PubMed
    • Google Scholar
34. 1. Kiebel SJ
    2. Daunizeau J
    3. Friston KJ

    (2008) A hierarchy of time-scales and the brain

    PLOS Computational Biology 4:e1000209.

    https://doi.org/10.1371/journal.pcbi.1000209

    • PubMed
    • Google Scholar
35. 1. Kravitz DJ
    2. Saleem KS
    3. Baker CI
    4. Mishkin M

    (2011) A new neural framework for visuospatial processing

    Nature Reviews. Neuroscience 12:217–230.

    https://doi.org/10.1038/nrn3008

    • PubMed
    • Google Scholar
36. 1. Lagore RL
    2. Moeller S
    3. Zimmermann J
    4. DelaBarre L
    5. Radder J
    6. Grant A
    7. Ugurbil K
    8. Yacoub E
    9. Harel N
    10. Adriany G

    (2021) An 8-dipole transceive and 24-loop receive array for non-human primate head imaging at 10.5 T

    NMR in Biomedicine 34:e4472.

    https://doi.org/10.1002/nbm.4472

    • PubMed
    • Google Scholar
37. 1. Logothetis NK
    2. Pauls J
    3. Augath M
    4. Trinath T
    5. Oeltermann A

    (2001) Neurophysiological investigation of the basis of the fMRI signal

    Nature 412:150–157.

    https://doi.org/10.1038/35084005

    • PubMed
    • Google Scholar
38. 1. Maisson DJN
    2. Cash-Padgett TV
    3. Wang MZ
    4. Hayden BY
    5. Heilbronner SR
    6. Zimmermann J

    (2021) Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex

    Nature Communications 12:4830.

    https://doi.org/10.1038/s41467-021-25219-w

    • PubMed
    • Google Scholar
39. 1. Marquand AF
    2. Haak KV
    3. Beckmann CF

    (2017) Functional corticostriatal connection topographies predict goal directed behaviour in humans

    Nature Human Behaviour 1:0146.

    https://doi.org/10.1038/s41562-017-0146

    • PubMed
    • Google Scholar
40. 1. Moeller S
    2. Yacoub E
    3. Olman CA
    4. Auerbach E
    5. Strupp J
    6. Harel N
    7. Uğurbil K

    (2010) Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI

    Magnetic Resonance in Medicine 63:1144–1153.

    https://doi.org/10.1002/mrm.22361

    • PubMed
    • Google Scholar
41. 1. Murray JD
    2. Bernacchia A
    3. Freedman DJ
    4. Romo R
    5. Wallis JD
    6. Cai X
    7. Padoa-Schioppa C
    8. Pasternak T
    9. Seo H
    10. Lee D
    11. Wang X-J

    (2014) A hierarchy of intrinsic timescales across primate cortex

    Nature Neuroscience 17:1661–1663.

    https://doi.org/10.1038/nn.3862

    • PubMed
    • Google Scholar
42. 1. Nougaret S
    2. Fascianelli V
    3. Ravel S
    4. Genovesio A

    (2021) Intrinsic timescales across the basal ganglia

    Scientific Reports 11:21395.

    https://doi.org/10.1038/s41598-021-00512-2

    • PubMed
    • Google Scholar
43. 1. Ogawa T
    2. Komatsu H

    (2010) Differential temporal storage capacity in the baseline activity of neurons in macaque frontal eye field and area V4

    Journal of Neurophysiology 103:2433–2445.

    https://doi.org/10.1152/jn.01066.2009

    • PubMed
    • Google Scholar
44. 1. Ongür D
    2. Price JL

    (2000) The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans

    Cerebral Cortex (New York, N.Y 10:206–219.

    https://doi.org/10.1093/cercor/10.3.206

    • PubMed
    • Google Scholar
45. 1. Price JL

    (1999) Networks within the orbital and medial prefrontal cortex

    Neurocase 5:231–241.

    https://doi.org/10.1080/13554799908402728

    • Google Scholar
46. 1. Przezdzik I
    2. Faber M
    3. Fernandez G
    4. Beckmann CF
    5. Haak KV

    (2019) The functional organisation of the hippocampus along its long axis is gradual and predicts recollection

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 119:324–335.

    https://doi.org/10.1016/j.cortex.2019.04.015

    • PubMed
    • Google Scholar
47. 1. Raut RV
    2. Snyder AZ
    3. Raichle ME

    (2020) Hierarchical dynamics as a macroscopic organizing principle of the human brain

    PNAS 117:20890–20897.

    https://doi.org/10.1073/pnas.2003383117

    • PubMed
    • Google Scholar
48. 1. Riley MR
    2. Qi XL
    3. Zhou X
    4. Constantinidis C

    (2018) Anterior-posterior gradient of plasticity in primate prefrontal cortex

    Nature Communications 9:3790.

    https://doi.org/10.1038/s41467-018-06226-w

    • PubMed
    • Google Scholar
49. 1. Rossi-Pool R
    2. Zainos A
    3. Alvarez M
    4. Parra S
    5. Zizumbo J
    6. Romo R

    (2021) Invariant timescale hierarchy across the cortical somatosensory network

    PNAS 118:e2021843118.

    https://doi.org/10.1073/pnas.2021843118

    • PubMed
    • Google Scholar
50. Conference

    1. Saad ZS
    2. Reynolds RC
    3. Argall B
    4. Japee S
    5. Cox RW

    (2004)

    SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI

    2004 2nd IEEE International Symposium on Biomedical Imaging.

    • Google Scholar
51. 1. Saad ZS
    2. Reynolds RC

    (2012) SUMA

    NeuroImage 62:768–773.

    https://doi.org/10.1016/j.neuroimage.2011.09.016

    • PubMed
    • Google Scholar
52. 1. Seidlitz J
    2. Sponheim C
    3. Glen D
    4. Ye FQ
    5. Saleem KS
    6. Leopold DA
    7. Ungerleider L
    8. Messinger A

    (2018) A population MRI brain template and analysis tools for the macaque

    NeuroImage 170:121–131.

    https://doi.org/10.1016/j.neuroimage.2017.04.063

    • PubMed
    • Google Scholar
53. 1. Setsompop K
    2. Gagoski BA
    3. Polimeni JR
    4. Witzel T
    5. Wedeen VJ
    6. Wald LL

    (2012) Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty

    Magnetic Resonance in Medicine 67:1210–1224.

    https://doi.org/10.1002/mrm.23097

    • PubMed
    • Google Scholar
54. Preprint

    1. Shinn M
    2. Hu A
    3. Turner L
    4. Noble S
    5. Achard S
    6. Anticevic A
    7. Scheinost D
    8. Constable RT
    9. Lee D
    10. Bullmore ET
    11. Murray JD

    (2021) Spatial and Temporal Autocorrelation Weave Human Brain Networks

    bioRxiv.

    https://doi.org/10.1101/2021.06.01.446561

    • Google Scholar
55. 1. Soltani A
    2. Murray JD
    3. Seo H
    4. Lee D

    (2021) Timescales of Cognition in the Brain

    Current Opinion in Behavioral Sciences 41:30–37.

    https://doi.org/10.1016/j.cobeha.2021.03.003

    • PubMed
    • Google Scholar
56. Preprint

    1. Tan PK
    2. Tang C
    3. Herikstad R
    4. Pillay A
    5. Libedinsky C

    (2020) Distinct Lateral Prefrontal Regions Are Organized in an Anterior-Posterior Functional Gradient

    bioRxiv.

    https://doi.org/10.1101/2020.12.16.423034

    • Google Scholar
57. 1. Uğurbil K
    2. Xu J
    3. Auerbach EJ
    4. Moeller S
    5. Vu AT
    6. Duarte-Carvajalino JM
    7. Lenglet C
    8. Wu X
    9. Schmitter S
    10. Van de Moortele PF
    11. Strupp J
    12. Sapiro G
    13. De Martino F
    14. Wang D
    15. Harel N
    16. Garwood M
    17. Chen L
    18. Feinberg DA
    19. Smith SM
    20. Miller KL
    21. Sotiropoulos SN
    22. Jbabdi S
    23. Andersson JLR
    24. Behrens TEJ
    25. Glasser MF
    26. Van Essen DC
    27. Yacoub E
    28. WU-Minn HCP Consortium

    (2013) Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

    NeuroImage 80:80–104.

    https://doi.org/10.1016/j.neuroimage.2013.05.012

    • PubMed
    • Google Scholar
58. 1. Ugurbil K

    (2016) What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371:20150361.

    https://doi.org/10.1098/rstb.2015.0361

    • PubMed
    • Google Scholar
59. 1. Uğurbil K

    (2018) Imaging at ultrahigh magnetic fields: History, challenges, and solutions

    NeuroImage 168:7–32.

    https://doi.org/10.1016/j.neuroimage.2017.07.007

    • PubMed
    • Google Scholar
60. 1. Uğurbil K
    2. Auerbach E
    3. Moeller S
    4. Grant A
    5. Wu X
    6. Van de Moortele PF
    7. Olman C
    8. DelaBarre L
    9. Schillak S
    10. Radder J
    11. Lagore R
    12. Adriany G

    (2019) Brain imaging with improved acceleration and SNR at 7 Tesla obtained with 64-channel receive array

    Magnetic Resonance in Medicine 82:495–509.

    https://doi.org/10.1002/mrm.27695

    • PubMed
    • Google Scholar
61. 1. Vos de Wael R
    2. Benkarim O
    3. Paquola C
    4. Lariviere S
    5. Royer J
    6. Tavakol S
    7. Xu T
    8. Hong SJ
    9. Langs G
    10. Valk S
    11. Misic B
    12. Milham M
    13. Margulies D
    14. Smallwood J
    15. Bernhardt BC

    (2020) BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

    Communications Biology 3:103.

    https://doi.org/10.1038/s42003-020-0794-7

    • PubMed
    • Google Scholar
62. 1. Watanabe T
    2. Rees G
    3. Masuda N

    (2019) Atypical intrinsic neural timescale in autism

    eLife 8:e42256.

    https://doi.org/10.7554/eLife.42256

    • PubMed
    • Google Scholar
63. 1. Wengler K
    2. Goldberg AT
    3. Chahine G
    4. Horga G

    (2020) Distinct hierarchical alterations of intrinsic neural timescales account for different manifestations of psychosis

    eLife 9:e56151.

    https://doi.org/10.7554/eLife.56151

    • PubMed
    • Google Scholar
64. 1. Whitfield-Gabrieli S
    2. Nieto-Castanon A

    (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks

    Brain Connectivity 2:125–141.

    https://doi.org/10.1089/brain.2012.0073

    • PubMed
    • Google Scholar
65. 1. Wong AL
    2. Haith AM
    3. Krakauer JW

    (2015) Motor Planning

    The Neuroscientist 21:385–398.

    https://doi.org/10.1177/1073858414541484

    • PubMed
    • Google Scholar
66. 1. Yacoub E
    2. Grier MD
    3. Auerbach EJ
    4. Lagore RL
    5. Harel N
    6. Adriany G
    7. Zilverstand A
    8. Hayden BY
    9. Heilbronner SR
    10. Uğurbil K
    11. Zimmermann J

    (2020) Ultra-high field (10.5 T) resting state fMRI in the macaque

    NeuroImage 223:117349.

    https://doi.org/10.1016/j.neuroimage.2020.117349

    • PubMed
    • Google Scholar
67. 1. Zilio F
    2. Gomez-Pilar J
    3. Cao S
    4. Zhang J
    5. Zang D
    6. Qi Z
    7. Tan J
    8. Hiromi T
    9. Wu X
    10. Fogel S
    11. Huang Z
    12. Hohmann MR
    13. Fomina T
    14. Synofzik M
    15. Grosse-Wentrup M
    16. Owen AM
    17. Northoff G

    (2021) Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

    NeuroImage 226:117579.

    https://doi.org/10.1016/j.neuroimage.2020.117579

    • PubMed
    • Google Scholar
68. 1. Zimmermann J
    2. Glimcher PW
    3. Louie K

    (2018) Multiple timescales of normalized value coding underlie adaptive choice behavior

    Nature Communications 9:3206.

    https://doi.org/10.1038/s41467-018-05507-8

    • PubMed
    • Google Scholar

Author details

1. Ana MG Manea

   1. Department of Neuroscience, University of Minnesota, Minneapolis, United States
   2. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   manea006@umn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4786-9657

2. Anna Zilverstand

   1. Department of Psychiatry & Behavioral Sciences , University of Minnesota, Minneapolis, United States
   2. Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4889-9700

3. Kamil Ugurbil

   1. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, United States
   2. Center for Neuroengineering, University of Minnesota, Minneapolis, United States
   3. Department of Radiology, University of Minnesota, Minneapolis, United States

   Contribution

   Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

4. Sarah R Heilbronner

   1. Department of Neuroscience, University of Minnesota, Minneapolis, United States
   2. Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, United States
   3. Center for Neuroengineering, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

5. Jan Zimmermann

   1. Department of Neuroscience, University of Minnesota, Minneapolis, United States
   2. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, United States
   3. Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, United States
   4. Center for Neuroengineering, University of Minnesota, Minneapolis, United States
   5. Department of Biomedical Engineering, University of Minnesota, Minneapolis, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

• 3,574

  views

• 363

  downloads

• 27

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.