Attention is the neuro-cognitive function that selects currently relevant pieces of information at the expense of irrelevant ones (Carrasco, 2011; Chelazzi et al., 2011). Attentional selection results from the interplay between exogenous factors, like intrinsic object saliency, and endogenous processes, like the relevance of an object for the task at hand. Endogenous attention is thought to be controlled by a fronto-parietal network (Corbetta et al., 2008; Kastner and Ungerleider, 2000). In the macaque monkey brain, the lateral intraparietal area (LIP; Bisley and Goldberg, 2010; Gottlieb et al., 1998) and the frontal eye field (FEF; Thompson and Bichot, 2005) exhibit key properties of attentional control and are thought to form the core of the endogenous attention network (Stemmann and Freiwald, 2016).

The fronto-parietal theory of attentional control is supported by recent whole-brain functional magnetic resonance imaging (fMRI) studies in the macaque monkey using a wide range of attention tasks (Caspari et al., 2015; Patel et al., 2015; Stemmann and Freiwald, 2016). One of these studies (Stemmann and Freiwald, 2016), found that an area in the lower bank of the Superior Temporal Sulcus (STS), the dorsal posterior infero-temporal area, PITd (Felleman and Van Essen, 1991; Saleem and Logothetis, 2007) was even more strongly attention-modulated than LIP, was engaged by multiple attention tasks, and was not modulated by the task-relevant feature dimension. Thus PITd was shown to exhibit key properties of an attentional control area (Figure 1A–B; Stemmann and Freiwald, 2016). These results raised the possibility that the network for endogenous control of attention may not be confined to fronto-parietal (dorsal) structures, but also includes a ventral region in the temporal lobe.

Figure 1 with 1 supplement see all

Download asset Open asset

A ventral attention network has been previously proposed in humans (Corbetta et al., 2008; Geng and Vossel, 2013). However, its functional properties are profoundly different from those of macaque area PITd. First, human dorsal and ventral attention networks have a different functional profile, with the first primarily supporting endogenous attention, and the latter primarily supporting exogenous attention (Corbetta et al., 2008), along with context updating (Geng and Vossel, 2013) and social cognition (Carter and Huettel, 2013; Mars et al., 2013; Schwiedrzik et al., 2015). On the contrary ventral area PITd shared similar functional properties with dorsal areas LIP and FEF (Stemmann and Freiwald, 2016). Second, the most prominent part of the human ventral attention network, the temporo-parietal-junction (TPJ), is suppressed during endogenous attention, and activated during target detection and shifts of attention, particularly when stimuli are salient or unexpected (Corbetta et al., 2008; Corbetta and Shulman, 2002), whereas PITd was strongly activated during prolonged sustained attention (Stemmann and Freiwald, 2016). Third, macaque PITd possesses a putative human homolog, phPITd, an area located far ventrally to TPJ (Glasser et al., 2016; Kolster et al., 2010). Thus the discovery of attention area PITd located ventrally to the ventral attention network raises important questions about attentional control mechanisms and their support circuitry.

In the brain imaging literature, the term network is often used loosely to describe a set of co-activated brain regions without explicit reference to their physical connections (Petersen and Sporns, 2015). The fact that multiple brain areas exhibit similar functional profiles across visual and cognitive conditions, like PITd, LIP and FEF did in (Stemmann and Freiwald, 2016), could be for a number of fundamentally different reasons. Co-activation might be initiated by a common driver outside the brain, like the visual stimulus, it could result from a common source inside the brain, with functional nodes only indirectly connected, or from connections that directly link the areas of interest. Only the characterization of the connectivity pattern between functionally identified areas establishes true network status. Understanding the structure of connectivity between putative attention control areas would thus be critical to establish whether they form an attention control network and help to answer the question of how they might coordinate a common focus of attention.

Long-range inter-areal connectivity is supported by the white matter. Uncovering the white matter tracts supporting endpoint-to-endpoint connections is important to know for at least two reasons. First, fascicles respond to behavior by adapting density, shape, and molecular composition in a manner that corresponds to individual abilities in health and disease (Fields, 2008; Jbabdi et al., 2015; Pestilli, 2018; Rokem et al., 2017; Thomason and Thompson, 2011; Wandell, 2016), and thus take an active role in shaping network function. To fully understand brain functionality, we thus need to combine knowledge about brain function and structure. Second, connectivity is crucial to elucidating how neurons and neural networks process information. The main organizing principle of the visual system is its separation into two major posterior-anterior information-processing streams: a dorsal path for spatial processing, and a ventral path for feature and object processing (Kravitz et al., 2013; Milner and Goodale, 1995). Each pathway may contain multiple, largely parallel routes (Kravitz et al., 2013), a conclusion that seems to be confirmed by the existence of dorsal and ventral attention areas. In this view, the study of connectivity between parietal and temporal attention nodes can serve as a model system for a general understanding of information flow between the dorsal and ventral visual pathways and between the parietal and temporal lobes at large.

Here we sought to, first, determine connections between attention areas PITd, LIP, and FEF, and, second, to delineate the long association fascicles of this postulated endogenous attention control network. Previous anatomical studies have demonstrated strong and direct connections between parietal area LIP and frontal area FEF (Blatt et al., 1990; Lewis and Van Essen, 2000; Petrides and Pandya, 2006) mainly via the Superior Longitudinal Fasciculus (SLF). However, function and anatomy of PITd have, so far, received little attention. More specifically, to date only a single classical anatomical study has targeted PITd (Distler et al., 1993), but due to the study’s paucity of data and lack of functional characterization, the relationship to attention networks remains unclear. We thus asked whether PITd connects to LIP, whether it connects to the FEF, and which fiber pathways these cortical areas use to exchange attentional information (Figure 1D). The general anatomy of macaque white matter organization suggests different candidate pathways: PITd may connect to LIP through the Vertical Occipital Fasciculus (VOF), a fascicle that has been shown to have some ventral terminations in the temporal cortex (Takemura et al., 2017). Alternatively, PITd and LIP might connect through a more anterior fascicle, the vertical branch of the Inferior Longitudinal Fasciculus (vILF). Similarly, it would be possible for PITd and the FEF to connect via one of two major pathways: fibers might run via the Arcuate Fasciculus (AF), as areas in the caudal superior temporal regions do (Petrides and Pandya, 2006). Alternatively, fibers connecting PITd and FEF could course via the Extreme Capsule (EmC), as the middle superior temporal region and some anterior patches of the inferior temporal sulcus do (Petrides and Pandya, 1988; Petrides and Pandya, 2006).

To test these hypotheses, we developed a new approach that combines in-vivo fMRI with high-resolution ex-vivo diffusion imaging (dMRI) and ensemble probabilistic tractography. This experimental approach was specifically designed to facilitate the comparison of dMRI with classical tracer studies, and overcome some of the limitations of dMRI and tractography. For example, this approach reduces artifacts and distortions, improves robustness to crossing fibers, and advances anatomical accuracy. This technique allows for the study of the connectivity of functionally specific brain areas, the exploration of fine-grain structural connectivity, and the description of the pathways connecting the functional areas. These three aspects are key to the understanding of brain functional networks, and the lack of a method combining them all has prevented, so far, the full characterization of the macaque attention network. High-resolution dMRI, combined with functionally-defined tractography, also facilitates the comparison between human and non-human cognitive networks, helps define which circuitry in the macaque attention system can serve as an accurate model for humans, and bridges the gap between traditional tracer studies and dMRI in humans.

Endogenous attentional control in both humans and macaque monkeys is thought to be exerted by a fronto-parietal network (Corbetta et al., 2008; Gottlieb et al., 1998; Kastner and Ungerleider, 2000; Thompson and Bichot, 2005). Here we show that, in the macaque brain, classical attentional control areas LIP, in posterior parietal cortex, and FEF, in prefrontal cortex, are connected to PITd, an area in temporal cortex only recently implied in endogenous attention (Stemmann and Freiwald, 2016). We thus provide new evidence that PITd might be an endogenous attention control area, that attentional control may not be limited to dorsal fronto-parietal structures in the macaque brain, and that these areas form an attention network through direct connections (Figure 5 and Figure 5—video 1). The fine anatomical characterization of the bundles connecting the three areas constitutes the first connectivity model of the macaque endogenous attention and should inform future theories of attentional processing.

Figure 5 with 1 supplement see all

Download asset Open asset

PITd is a visual area located in the lower bank of the STS, it contains neurons that are shape- and color-selective (Conway and Tsao, 2009; Hikosaka, 1998) and have circumscribed receptive fields slightly larger than those of V4 (Hikosaka, 1998). Its anatomical connectivity and its role in cognition have been little explored. While LIP-FEF connectivity has been established through tracer studies (Blatt et al., 1990; Lewis and Van Essen, 2000; Petrides and Pandya, 2006), connectivity of either area with the attention-modulated part of PITd has hitherto remained unknown. Even general connectivity with PITd, anatomically defined, was not fully established. One previous anatomical study investigated cortical connections of inferior temporal areas (Distler et al., 1993) and, through a single tracer injection in PITd, provided some evidence for connectivity between PITd and LIP. Due to the study’s paucity of data and lack of relating connectivity to functional characterization, the relationship to attentional control networks remained unclear. Similarly, tracer studies targeting LIP and FEF showed variable results in terms of existence and strength of connections with PITd (Barbas and Mesulam, 1981; Barbas, 1988; Blatt et al., 1990; Bullier et al., 1996; Distler et al., 1993; Schall et al., 1995). These inconsistencies are likely the result of differences in tracing methods, injection sites, anatomical definitions, and possibly even nomenclature (Felleman and Van Essen, 1991; Kötter, 2004; Stephan et al., 2001). We resolved these difficulties by combining functional mapping with dMRI. The part of PITd that is strongly modulated by attention in ways similar to those of LIP and FEF, is directly connected to these two classical attentional control regions. The comparative tractography we conducted between PITd and nearby functional areas (Figure 4—figure supplement 4) confirms the existence of tract-specific connections from PITd and demonstrates that, structurally, this connectivity is part of a larger-scale pattern of connectivity that PITd shares with its immediate neighborhood. Yet, functionally, PITd differs fundamentally from nearby STS areas in not being motion selective, while being very strongly attention modulated during attentive motion discrimination. Thus, functional specificity of PITd does not arise as a simple consequence of its connectivity with LIP and FEF. It is noteworthy that this connectivity scheme is different from the nearby face-processing network (Grimaldi et al., 2016; Moeller et al., 2008), which encompasses two face areas right next to attention-modulated area PITd (Stemmann and Freiwald, 2016). In the face-processing network, tight connectivity between face areas helps to ensure domain specificity (for faces) (Grimaldi et al., 2016; Moeller et al., 2008). Yet, for attention control networks shared connectivity fulfills the need to exert control over multiple areas and gather visual information from across multiple visual domains (Fecteau and Munoz, 2006). In particular, PITd’s position within the ventral visual pathway is likely crucial to collect attention-relevant information about properties of objects (Conway and Tsao, 2009; Hikosaka, 1998), including faces (Tsao et al., 2006) and gaze (Marciniak et al., 2014), possibly providing information on object value (Ghazizadeh et al., 2018). Through the described connections, PITd might thus make important contributions to core functionality of endogenous attention and to a wide range of attention-related functions.

In humans, attention has been proposed to be controlled by two networks with different functions, located in the dorsal and ventral part of the brain (Corbetta et al., 2008). The most prominent part of the ventral attention network is the temporo-parietal-junction (TPJ). TPJ is suppressed during endogenous attention, and activated during target detection and shifts of attention, particularly when stimuli are salient or unexpected (Corbetta et al., 2008; Corbetta and Shulman, 2002). It is thus thought to be functionally, but also anatomically, distinct from the dorsal fronto-parietal network (Corbetta et al., 2008). An area homologous to TPJ in the macaque has been hypothesized, but not yet found (Patel et al., 2015). Macaque PITd is activated during sustained attention (Stemmann and Freiwald, 2016), does not activate during attentional shift (Caspari et al., 2015), and, as we show here, is structurally connected to the dorsal fronto-parietal network. PITd thus bears neither functional nor structural similarity to human TPJ. One of the macaque candidate regions previously suggested for the putative correspondence to the human TPJ is the dorsal bank of STS and the adjacent superior temporal gyrus. Lesions of this region cause neglect-like (or spatial extinction-like) syndromes (Luh et al., 1986; Scalaidhe et al., 1995; Watson et al., 1994), which led to a suggestion of a possible homology to the (right hemisphere) superior temporal gyrus (STG) in humans, one of the major lesion foci in neglect patients (Karnath, 2001). Alternatively, macaque TPO/STP and neighboring parietal area seven were hypothesized to correspond to human TPJ. These dorsal STS areas show, similarly to areas LIP and FEF, memory delay activity during saccade preparation (Kagan et al., 2010), and exhibit task-specific changes after reversible LIP inactivation (Wilke et al., 2012). Furthermore, recent work on the social brain also suggested that a potential precursor to human TPJ may reside in macaque posterior dorsal STS (Schwiedrzik et al., 2015) or in area 7a (Sliwa and Freiwald, 2017). Furthermore, macaque PITd possesses a putative human homolog, phPITd, an area located far ventrally to TPJ (Glasser et al., 2016; Kolster et al., 2010). Human phPITd occupies a similar positions in retinotopic cortex as macaque PITd, and, like PITd, is sensitive to two-dimensional shapes and is insensitive to motion (Kolster et al., 2010). Our results predict, under a homology argument of attention network organization, this area as the ventral node of the human endogenous attention network.

PITd connects to LIP and FEF through specific white matter tracts. Knowing these tracts is important for a full characterization of endogenous attentional control circuits, their vulnerabilities, and developmental potential. For example, the properties of specific fiber-bundles have been found to correlate with attentive behaviors (Chechlacz et al., 2015; Chica et al., 2018; Parlatini et al., 2017), and their disconnection results in attentional deficits like spatial neglect (Carter et al., 2017; Lunven and Bartolomeo, 2017), it is therefore of great importance to characterize their structure and path. Traditional tracer studies typically focus on the analysis of the origins and terminations of white matter fibers, without characterizing the white matter pathways between the endpoints. Our experimental approach, by capitalizing on the macroscopic bird-eye view of dMRI (Jbabdi et al., 2015; Rokem et al., 2017) combined with behaviorally guided fMRI (Stemmann and Freiwald, 2016), allowed us also to describe the cortico-cortical pathways of the endogenous control attention. We established that the vertical branch of the ILF, but not the VOF, connects the two posterior attention areas PITd with LIP, and that the ILF and the EmC, but not the AF, connects PITd to the dorsal prefrontal area FEF. Our results thus imply a role in attentional control for the vILF and the ILF/EmC. (Figure 5 and Figure 5—video 1).

We have used quality data, combined multimodal data, and used advanced quantification and statistical evaluation methods (see Material and methods), yet we fully acknowledge that tractography has limitations and is challenged by the trade-off between specificity and sensitivity (Thomas et al., 2014). Recent studies have demonstrated limitations of dMRI-based tractography in three important way: (1) in the ability to solve crossing fibers (Maier-Hein et al., 2017; Roebroeck et al., 2008), (2) in the dependency on tractography algorithms or parameter settings (Bastiani et al., 2012; Chamberland et al., 2014; Domin et al., 2014; Kunimatsu et al., 2004; Parizel et al., 2007; Takemura et al., 2016a; Taoka et al., 2009; Thomas et al., 2014), and (3) in the accuracy for estimating fiber projections into cortical gray matter (Reveley et al., 2015). Our results set the foundation for future investigations using other modalities to infer the connectivity of attentional control circuitry in macaque monkeys and other species, like human subjects.

In humans, two distinct vertical fiber bundles that connect the dorsal and ventral visual streams have been described, the VOF and the posterior Arcuate Fasciculus (pAF). The VOF supports a variety of perceptual functions, like skilled reading and spatial orienting (Takemura et al., 2016b; Yeatman et al., 2014; Yeatman et al., 2013), the pAF, located anterior to the VOF (Catani and Thiebaut de Schotten, 2008; Thiebaut de Schotten et al., 2014; Weiner et al., 2017), is thought to support language (Catani and Thiebaut de Schotten, 2008; Thiebaut de Schotten et al., 2014) and action-to-object coordination (Budisavljevic et al., 2018). The existence of two separated vertical tracts in the posterior part of the human brain resembles the connectivity profile of the monkey described here and suggest a possible resolution of current cross-species inconsistencies in the literature. Because of its anatomical location, its cortical endpoints, and its separation from the VOF, human pAF may be a candidate tract for the exchange of attentional information between dorsal and ventral attention areas in the posterior part of the human brain, thus being functionally and anatomically similar to the vILF. The fronto-temporal connection between macaque PITd and FEF via the EmC we found in this study, may also have a human homolog. Human EmC is important for language (Catani and Bambini, 2014; Friederici and Gierhan, 2013) and may also carry attentional information (Umarova et al., 2010). Umarova and colleagues found that human EmC connects ventral frontal cortex and insula to the TPJ, namely the core nodes of the human exogenous attention network (Corbetta et al., 2008; Corbetta and Shulman, 2002). A direct connection via EmC between phPITd and FEF would suggest a division of function between phPITd and TPJ, but similar frontal lobe connectivity. Alternatively, the functional difference between PITd and TPJ may be paralleled by a difference in structural connectivity: the AF may be a good candidate to exchange endogenous attention information between human frontal and temporal lobes. Future work examining both structure and function in the human brain should be able clarify the structural similarity between macaque and human attention networks and thus reveal putative constraints by white-matter fascicles on the evolution of functional networks. Determining the mesoscopic correspondence between structure and function across species is a fundamental challenge for neuroscience (Zhang et al., 2013), a challenge that cannot be met by pure functional and pure anatomical measurements alone (Pollen and Hofmann, 2008). The first fine-scale functional characterization of the white matter tracts supporting endogenous attention described here sets up a new framework to understand the evolution of function in white matter bundles, and to determine the relationship between brain structure and function during evolution.

The main organizing principle of the visual system is its separation into two major posterior-anterior information-processing streams: a dorsal path for spatial processing, and a ventral path for feature and object processing (Kravitz et al., 2013; Milner and Goodale, 1995). Separation of information into different streams, however, appears to be at odds with the requirements of visual attention which generally needs to integrate spatial and featural information (Baylis et al., 2001; Milner, 2017; Treisman and Gelade, 1980). One possible solution to this binding problem, is the development of shape-selectivity in the dorsal stream (Theys et al., 2015). Alternatively, vertical connections between the dorsal and ventral attention areas as described here, provide a structural substrate for attentional integration that capitalizes on the full functional repertoire of both streams. This vertical connectivity may thus be a general theme, a second organizing principle of the visual system in humans (Kay and Yeatman, 2017; Pestilli et al., 2014) and macaques (Nelissen et al., 2011), extending from attention to other functions requiring vertical integration like language (Cloutman, 2013) or skilled grasping (van Polanen and Davare, 2015).

Taken together, our work advances the understanding of the architecture of attentional control by suggesting a three-node cortical network, with nodes in three different cortical lobes as the structural substrate of this cognitive function. The description of direct structural connections between these nodes we provide here, now allows investigations of the ‘effective connectivity’ between attention areas and the causal impact of these interactions. The findings reported here have important consequences for the understanding of the mechanisms and functional capabilities of endogenous attention, for cognitive theories of endogenous attention (Itti et al., 2005), for the evolution of attention systems, and for the functional organization of the primate brain.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data for Figures 2, 3, 4, 5, have been made available via the Open Science Framework (https://osf.io/8ks5t/).

1. 1. Sani I
   2. McPherson B

   (2018) Open Science Framework

   Functionally defined white matter of the macaque monkey brain reveals a dorso-ventral attention network - Data.

   https://doi.org/10.17605/osf.io/8ks5t

1. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
2. 1. Barbas H
   2. Mesulam MM

   (1981) Organization of afferent input to subdivisions of area 8 in the rhesus monkey

   The Journal of Comparative Neurology 200:407–431.

   https://doi.org/10.1002/cne.902000309

   • PubMed
   • Google Scholar
3. 1. Barbas H

   (1988) Anatomic organization of basoventral and mediodorsal visual recipient prefrontal regions in the rhesus monkey

   The Journal of Comparative Neurology 276:313–342.

   https://doi.org/10.1002/cne.902760302

   • PubMed
   • Google Scholar
4. 1. Basser PJ
   2. Pajevic S

   (2000) Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise

   Magnetic Resonance in Medicine 44:41–50.

   https://doi.org/10.1002/1522-2594(200007)44:1<41::AID-MRM8>3.0.CO;2-O

   • PubMed
   • Google Scholar
5. 1. Bastiani M
   2. Shah NJ
   3. Goebel R
   4. Roebroeck A

   (2012) Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm

   NeuroImage 62:1732–1749.

   https://doi.org/10.1016/j.neuroimage.2012.06.002

   • PubMed
   • Google Scholar
6. 1. Baylis GC
   2. Gore CL
   3. Dennis Rodriguez P
   4. Shisler RJ

   (2001) Visual extinction and awareness: The importance of binding dorsal and ventral pathways

   Visual Cognition 8:359–379.

   https://doi.org/10.1080/13506280143000052

   • Google Scholar
7. 1. Bisley JW
   2. Goldberg ME

   (2010) Attention, intention, and priority in the parietal lobe

   Annual Review of Neuroscience 33:1–21.

   https://doi.org/10.1146/annurev-neuro-060909-152823

   • PubMed
   • Google Scholar
8. 1. Blatt GJ
   2. Andersen RA
   3. Stoner GR

   (1990) Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque

   The Journal of Comparative Neurology 299:421–445.

   https://doi.org/10.1002/cne.902990404

   • PubMed
   • Google Scholar
9. 1. Budisavljevic S
   2. Dell'Acqua F
   3. Castiello U

   (2018) Cross-talk connections underlying dorsal and ventral stream integration during hand actions

   Cortex 103:224–239.

   https://doi.org/10.1016/j.cortex.2018.02.016

   • Google Scholar
10. 1. Bullier J
    2. Schall JD
    3. Morel A

    (1996) Functional streams in occipito-frontal connections in the monkey

    Behavioural Brain Research 76:89–97.

    https://doi.org/10.1016/0166-4328(95)00182-4

    • Google Scholar
11. 1. Caiafa CF
    2. Pestilli F

    (2017) Multidimensional encoding of brain connectomes

    Scientific Reports 7:11491.

    https://doi.org/10.1038/s41598-017-09250-w

    • PubMed
    • Google Scholar
12. 1. Carrasco M

    (2011) Visual attention: the past 25 years

    Vision Research 51:1484–1525.

    https://doi.org/10.1016/j.visres.2011.04.012

    • PubMed
    • Google Scholar
13. 1. Carter AR
    2. McAvoy MP
    3. Siegel JS
    4. Hong X
    5. Astafiev SV
    6. Rengachary J
    7. Zinn K
    8. Metcalf NV
    9. Shulman GL
    10. Corbetta M

    (2017) Differential white matter involvement associated with distinct visuospatial deficits after right hemisphere stroke

    Cortex 88:81–97.

    https://doi.org/10.1016/j.cortex.2016.12.009

    • PubMed
    • Google Scholar
14. 1. Carter RM
    2. Huettel SA

    (2013) A nexus model of the temporal-parietal junction

    Trends in Cognitive Sciences 17:328–336.

    https://doi.org/10.1016/j.tics.2013.05.007

    • PubMed
    • Google Scholar
15. 1. Caspari N
    2. Janssens T
    3. Mantini D
    4. Vandenberghe R
    5. Vanduffel W

    (2015) Covert shifts of spatial attention in the macaque monkey

    Journal of Neuroscience 35:7695–7714.

    https://doi.org/10.1523/JNEUROSCI.4383-14.2015

    • PubMed
    • Google Scholar
16. 1. Catani M
    2. Bambini V

    (2014) A model for social communication and language evolution and development (SCALED)

    Current Opinion in Neurobiology 28:165–171.

    https://doi.org/10.1016/j.conb.2014.07.018

    • PubMed
    • Google Scholar
17. 1. Catani M
    2. Thiebaut de Schotten M

    (2008) A diffusion tensor imaging tractography atlas for virtual in vivo dissections

    Cortex 44:1105–1132.

    https://doi.org/10.1016/j.cortex.2008.05.004

    • PubMed
    • Google Scholar
18. 1. Chamberland M
    2. Whittingstall K
    3. Fortin D
    4. Mathieu D
    5. Descoteaux M

    (2014) Real-time multi-peak tractography for instantaneous connectivity display

    Frontiers in Neuroinformatics 8:59.

    https://doi.org/10.3389/fninf.2014.00059

    • Google Scholar
19. 1. Chechlacz M
    2. Gillebert CR
    3. Vangkilde SA
    4. Petersen A
    5. Humphreys GW

    (2015) Structural variability within frontoparietal networks and individual differences in attentional functions: an approach using the theory of visual attention

    Journal of Neuroscience 35:10647–10658.

    https://doi.org/10.1523/JNEUROSCI.0210-15.2015

    • PubMed
    • Google Scholar
20. 1. Chelazzi L
    2. Della Libera C
    3. Sani I
    4. Santandrea E

    (2011) Neural basis of visual selective attention

    Wiley Interdisciplinary Reviews: Cognitive Science 2:392–407.

    https://doi.org/10.1002/wcs.117

    • PubMed
    • Google Scholar
21. 1. Chica AB
    2. Thiebaut de Schotten M
    3. Bartolomeo P
    4. Paz-Alonso PM

    (2018) White matter microstructure of attentional networks predicts attention and consciousness functional interactions

    Brain Structure and Function 223:653–668.

    https://doi.org/10.1007/s00429-017-1511-2

    • PubMed
    • Google Scholar
22. 1. Cloutman LL

    (2013) Interaction between dorsal and ventral processing streams: where, when and how?

    Brain and Language 127:251–263.

    https://doi.org/10.1016/j.bandl.2012.08.003

    • PubMed
    • Google Scholar
23. 1. Conway BR
    2. Tsao DY

    (2009) Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior temporal cortex

    PNAS 106:18034–18039.

    https://doi.org/10.1073/pnas.0810943106

    • PubMed
    • Google Scholar
24. 1. Corbetta M
    2. Patel G
    3. Shulman GL

    (2008) The reorienting system of the human brain: from environment to theory of mind

    Neuron 58:306–324.

    https://doi.org/10.1016/j.neuron.2008.04.017

    • PubMed
    • Google Scholar
25. 1. Corbetta M
    2. Shulman GL

    (2002) Control of goal-directed and stimulus-driven attention in the brain

    Nature Reviews Neuroscience 3:201–215.

    https://doi.org/10.1038/nrn755

    • PubMed
    • Google Scholar
26. 1. Croxson PL
    2. Johansen-Berg H
    3. Behrens TE
    4. Robson MD
    5. Pinsk MA
    6. Gross CG
    7. Richter W
    8. Richter MC
    9. Kastner S
    10. Rushworth MF

    (2005) Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography

    Journal of Neuroscience 25:8854–8866.

    https://doi.org/10.1523/JNEUROSCI.1311-05.2005

    • PubMed
    • Google Scholar
27. 1. D'Arceuil HE
    2. Westmoreland S
    3. de Crespigny AJ

    (2007) An approach to high resolution diffusion tensor imaging in fixed primate brain

    NeuroImage 35:553–565.

    https://doi.org/10.1016/j.neuroimage.2006.12.028

    • PubMed
    • Google Scholar
28. 1. Distler C
    2. Boussaoud D
    3. Desimone R
    4. Ungerleider LG

    (1993) Cortical connections of inferior temporal area TEO in macaque monkeys

    The Journal of Comparative Neurology 334:125–150.

    https://doi.org/10.1002/cne.903340111

    • Google Scholar
29. 1. Domin M
    2. Langner S
    3. Hosten N
    4. Lotze M

    (2014) Comparison of parameter threshold combinations for diffusion tensor tractography in chronic stroke patients and healthy subjects

    PLoS ONE 9:e98211.

    https://doi.org/10.1371/journal.pone.0098211

    • PubMed
    • Google Scholar
30. 1. Dyrby TB
    2. Søgaard LV
    3. Parker GJ
    4. Alexander DC
    5. Lind NM
    6. Baaré WF
    7. Hay-Schmidt A
    8. Eriksen N
    9. Pakkenberg B
    10. Paulson OB
    11. Jelsing J

    (2007) Validation of in vitro probabilistic tractography

    NeuroImage 37:1267–1277.

    https://doi.org/10.1016/j.neuroimage.2007.06.022

    • PubMed
    • Google Scholar
31. 1. Eichert N
    2. Verhagen L
    3. Folloni D
    4. Jbabdi S
    5. Khrapitchev AA
    6. Sibson NR
    7. Mantini D
    8. Sallet J
    9. Mars RB

    (2018) What is special about the human arcuate fasciculus? Lateralization, projections, and expansion

    Cortex.

    https://doi.org/10.1016/j.cortex.2018.05.005

    • PubMed
    • Google Scholar
32. 1. Fecteau JH
    2. Munoz DP

    (2006) Salience, relevance, and firing: a priority map for target selection

    Trends in Cognitive Sciences 10:382–390.

    https://doi.org/10.1016/j.tics.2006.06.011

    • PubMed
    • Google Scholar
33. 1. Felleman DJ
    2. Van Essen DC

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1

    • PubMed
    • Google Scholar
34. 1. Fields RD

    (2008) White matter in learning, cognition and psychiatric disorders

    Trends in Neurosciences 31:361–370.

    https://doi.org/10.1016/j.tins.2008.04.001

    • PubMed
    • Google Scholar
35. 1. Friederici AD
    2. Gierhan SME

    (2013) The language network

    Current Opinion in Neurobiology 23:250–254.

    https://doi.org/10.1016/j.conb.2012.10.002

    • Google Scholar
36. 1. Geng JJ
    2. Vossel S

    (2013) Re-evaluating the role of TPJ in attentional control: contextual updating?

    Neuroscience & Biobehavioral Reviews 37:2608–2620.

    https://doi.org/10.1016/j.neubiorev.2013.08.010

    • PubMed
    • Google Scholar
37. 1. Ghazizadeh A
    2. Griggs W
    3. Leopold DA
    4. Hikosaka O

    (2018) Temporal-prefrontal cortical network for discrimination of valuable objects in long-term memory

    PNAS 115:E2135–E2144.

    https://doi.org/10.1073/pnas.1707695115

    • PubMed
    • Google Scholar
38. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
39. 1. Gottlieb JP
    2. Kusunoki M
    3. Goldberg ME

    (1998) The representation of visual salience in monkey parietal cortex

    Nature 391:481–484.

    https://doi.org/10.1038/35135

    • PubMed
    • Google Scholar
40. 1. Grimaldi P
    2. Saleem KS
    3. Tsao D

    (2016) Anatomical connections of the functionally defined "face patches" in the macaque monkey

    Neuron 90:1325–1342.

    https://doi.org/10.1016/j.neuron.2016.05.009

    • PubMed
    • Google Scholar
41. 1. Hikosaka K

    (1998) Representation of foveal visual fields in the ventral bank of the superior temporal sulcus in the posterior inferotemporal cortex of the macaque monkey

    Behavioural Brain Research 96:101–113.

    https://doi.org/10.1016/S0166-4328(97)00202-7

    • PubMed
    • Google Scholar
42. 1. Honey CJ
    2. Sporns O

    (2008) Dynamical consequences of lesions in cortical networks

    Human Brain Mapping 29:802–809.

    https://doi.org/10.1002/hbm.20579

    • PubMed
    • Google Scholar
43. Book

    1. Itti L
    2. Rees G
    3. Tsotsos JK

    (2005)

    Neurobiology of Attention

    Amsterdam: Elsevier Academic Press.

    • Google Scholar
44. 1. Jbabdi S
    2. Sotiropoulos SN
    3. Haber SN
    4. Van Essen DC
    5. Behrens TE

    (2015) Measuring macroscopic brain connections in vivo

    Nature Neuroscience 18:1546–1555.

    https://doi.org/10.1038/nn.4134

    • PubMed
    • Google Scholar
45. 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

    • Google Scholar
46. 1. Jones DK

    (2010) Precision and accuracy in diffusion tensor magnetic resonance imaging

    Topics in Magnetic Resonance Imaging 21:87–99.

    https://doi.org/10.1097/RMR.0b013e31821e56ac

    • PubMed
    • Google Scholar
47. 1. Kagan I
    2. Iyer A
    3. Lindner A
    4. Andersen RA

    (2010) Space representation for eye movements is more contralateral in monkeys than in humans

    PNAS 107:7933–7938.

    https://doi.org/10.1073/pnas.1002825107

    • PubMed
    • Google Scholar
48. 1. Karnath HO

    (2001) New insights into the functions of the superior temporal cortex

    Nature Reviews Neuroscience 2:568–576.

    https://doi.org/10.1038/35086057

    • PubMed
    • Google Scholar
49. 1. Kastner S
    2. Ungerleider LG

    (2000) Mechanisms of visual attention in the human cortex

    Annual Review of Neuroscience 23:315–341.

    https://doi.org/10.1146/annurev.neuro.23.1.315

    • PubMed
    • Google Scholar
50. 1. Kay KN
    2. Yeatman JD

    (2017) Bottom-up and top-down computations in word- and face-selective cortex

    eLife 6:e22341.

    https://doi.org/10.7554/eLife.22341

    • PubMed
    • Google Scholar
51. 1. Kolster H
    2. Peeters R
    3. Orban GA

    (2010) The retinotopic organization of the human middle temporal area MT/V5 and its cortical neighbors

    Journal of Neuroscience 30:9801–9820.

    https://doi.org/10.1523/JNEUROSCI.2069-10.2010

    • PubMed
    • Google Scholar
52. 1. Kötter R

    (2004) Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database

    Neuroinformatics 2:127–144.

    https://doi.org/10.1385/NI:2:2:127

    • PubMed
    • Google Scholar
53. 1. Kravitz DJ
    2. Saleem KS
    3. Baker CI
    4. Ungerleider LG
    5. Mishkin M

    (2013) The ventral visual pathway: an expanded neural framework for the processing of object quality

    Trends in Cognitive Sciences 17:26–49.

    https://doi.org/10.1016/j.tics.2012.10.011

    • PubMed
    • Google Scholar
54. 1. Kunimatsu A
    2. Aoki S
    3. Masutani Y
    4. Abe O
    5. Hayashi N
    6. Mori H
    7. Masumoto T
    8. Ohtomo K

    (2004) The optimal trackability threshold of fractional anisotropy for diffusion tensor tractography of the corticospinal tract

    Magnetic Resonance in Medical Sciences 3:11–17.

    https://doi.org/10.2463/mrms.3.11

    • PubMed
    • Google Scholar
55. 1. Lewis JW
    2. Van Essen DC

    (2000) Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey

    The Journal of Comparative Neurology 428:112–137.

    https://doi.org/10.1002/1096-9861(20001204)428:1<112::AID-CNE8>3.0.CO;2-9

    • PubMed
    • Google Scholar
56. 1. Luh KE
    2. Butter CM
    3. Buchtel HA

    (1986) Impairments in orienting to visual stimuli in monkeys following unilateral lesions of the superior sulcal polysensory cortex

    Neuropsychologia 24:461–470.

    https://doi.org/10.1016/0028-3932(86)90091-6

    • PubMed
    • Google Scholar
57. 1. Lunven M
    2. Bartolomeo P

    (2017) Attention and spatial cognition: Neural and anatomical substrates of visual neglect

    Annals of Physical and Rehabilitation Medicine 60:124–129.

    https://doi.org/10.1016/j.rehab.2016.01.004

    • PubMed
    • Google Scholar
58. 1. Maier-Hein KH
    2. Neher PF
    3. Houde JC
    4. Côté MA
    5. Garyfallidis E
    6. Zhong J
    7. Chamberland M
    8. Yeh FC
    9. Lin YC
    10. Ji Q
    11. Reddick WE
    12. Glass JO
    13. Chen DQ
    14. Feng Y
    15. Gao C
    16. Wu Y
    17. Ma J
    18. Renjie H
    19. Li Q
    20. Westin CF
    21. Deslauriers-Gauthier S
    22. González JOO
    23. Paquette M
    24. St-Jean S
    25. Girard G
    26. Rheault F
    27. Sidhu J
    28. Tax CMW
    29. Guo F
    30. Mesri HY
    31. Dávid S
    32. Froeling M
    33. Heemskerk AM
    34. Leemans A
    35. Boré A
    36. Pinsard B
    37. Bedetti C
    38. Desrosiers M
    39. Brambati S
    40. Doyon J
    41. Sarica A
    42. Vasta R
    43. Cerasa A
    44. Quattrone A
    45. Yeatman J
    46. Khan AR
    47. Hodges W
    48. Alexander S
    49. Romascano D
    50. Barakovic M
    51. Auría A
    52. Esteban O
    53. Lemkaddem A
    54. Thiran JP
    55. Cetingul HE
    56. Odry BL
    57. Mailhe B
    58. Nadar MS
    59. Pizzagalli F
    60. Prasad G
    61. Villalon-Reina JE
    62. Galvis J
    63. Thompson PM
    64. Requejo FS
    65. Laguna PL
    66. Lacerda LM
    67. Barrett R
    68. Dell'Acqua F
    69. Catani M
    70. Petit L
    71. Caruyer E
    72. Daducci A
    73. Dyrby TB
    74. Holland-Letz T
    75. Hilgetag CC
    76. Stieltjes B
    77. Descoteaux M

    (2017) The challenge of mapping the human connectome based on diffusion tractography

    Nature Communications 8:1349.

    https://doi.org/10.1038/s41467-017-01285-x

    • PubMed
    • Google Scholar
59. 1. Marciniak K
    2. Atabaki A
    3. Dicke PW
    4. Thier P

    (2014) Disparate substrates for head gaze following and face perception in the monkey superior temporal sulcus

    eLife 3:e03222.

    https://doi.org/10.7554/eLife.03222

    • Google Scholar
60. 1. Mars RB
    2. Sallet J
    3. Neubert FX
    4. Rushworth MF

    (2013) Connectivity profiles reveal the relationship between brain areas for social cognition in human and monkey temporoparietal cortex

    PNAS 110:10806–10811.

    https://doi.org/10.1073/pnas.1302956110

    • PubMed
    • Google Scholar
61. 1. Mars RB
    2. Foxley S
    3. Verhagen L
    4. Jbabdi S
    5. Sallet J
    6. Noonan MP
    7. Neubert FX
    8. Andersson JL
    9. Croxson PL
    10. Dunbar RI
    11. Khrapitchev AA
    12. Sibson NR
    13. Miller KL
    14. Rushworth MF

    (2016) The extreme capsule fiber complex in humans and macaque monkeys: a comparative diffusion MRI tractography study

    Brain Structure and Function 221:4059–4071.

    https://doi.org/10.1007/s00429-015-1146-0

    • PubMed
    • Google Scholar
62. Book

    1. Milner AD
    2. Goodale MA

    (1995)

    The Visual Brain in Action

    UKMilner A. D, Perrett D. I, Johnston R. S, Benson R. S, Jordan P. J, editors. Oxford: Oxford University Press.

    • Google Scholar
63. 1. Milner AD

    (2017) How do the two visual streams interact with each other?

    Experimental Brain Research 235:1297–1308.

    https://doi.org/10.1007/s00221-017-4917-4

    • PubMed
    • Google Scholar
64. 1. Moeller S
    2. Freiwald WA
    3. Tsao DY

    (2008) Patches with links: a unified system for processing faces in the macaque temporal lobe

    Science 320:1355–1359.

    https://doi.org/10.1126/science.1157436

    • PubMed
    • Google Scholar
65. 1. Nelissen K
    2. Borra E
    3. Gerbella M
    4. Rozzi S
    5. Luppino G
    6. Vanduffel W
    7. Rizzolatti G
    8. Orban GA

    (2011) Action observation circuits in the macaque monkey cortex

    Journal of Neuroscience 31:3743–3756.

    https://doi.org/10.1523/JNEUROSCI.4803-10.2011

    • PubMed
    • Google Scholar
66. 1. Pajevic S
    2. Pierpaoli C

    (1999) Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain

    Magnetic Resonance in Medicine 42:526–540.

    https://doi.org/10.1002/(SICI)1522-2594(199909)42:3<526::AID-MRM15>3.0.CO;2-J

    • PubMed
    • Google Scholar
67. 1. Parizel PM
    2. Van Rompaey V
    3. Van Loock R
    4. Van Hecke W
    5. Van Goethem JW
    6. Leemans A
    7. Sijbers J

    (2007) Influence of user-defined parameters on diffusion tensor tractography of the corticospinal tract

    The Neuroradiology Journal 20:139–147.

    https://doi.org/10.1177/197140090702000202

    • PubMed
    • Google Scholar
68. 1. Parlatini V
    2. Radua J
    3. Dell'Acqua F
    4. Leslie A
    5. Simmons A
    6. Murphy DG
    7. Catani M
    8. Thiebaut de Schotten M

    (2017) Functional segregation and integration within fronto-parietal networks

    NeuroImage 146:367–375.

    https://doi.org/10.1016/j.neuroimage.2016.08.031

    • PubMed
    • Google Scholar
69. 1. Patel GH
    2. Yang D
    3. Jamerson EC
    4. Snyder LH
    5. Corbetta M
    6. Ferrera VP

    (2015) Functional evolution of new and expanded attention networks in humans

    PNAS 112:9454–9459.

    https://doi.org/10.1073/pnas.1420395112

    • PubMed
    • Google Scholar
70. 1. Pestilli F
    2. Yeatman JD
    3. Rokem A
    4. Kay KN
    5. Wandell BA

    (2014) Evaluation and statistical inference for human connectomes

    Nature Methods 11:1058–1063.

    https://doi.org/10.1038/nmeth.3098

    • PubMed
    • Google Scholar
71. 1. Pestilli F

    (2018) Human white matter and knowledge representation

    PLOS Biology 16:e2005758.

    https://doi.org/10.1371/journal.pbio.2005758

    • Google Scholar
72. 1. Petersen SE
    2. Sporns O

    (2015) Brain Networks and Cognitive Architectures

    Neuron 88:207–219.

    https://doi.org/10.1016/j.neuron.2015.09.027

    • Google Scholar
73. 1. Petrides M
    2. Pandya DN

    (1988) Association fiber pathways to the frontal cortex from the superior temporal region in the rhesus monkey

    The Journal of Comparative Neurology 273:52–66.

    https://doi.org/10.1002/cne.902730106

    • PubMed
    • Google Scholar
74. 1. Petrides M
    2. Pandya DN

    (2006) Efferent association pathways originating in the caudal prefrontal cortex in the macaque monkey

    The Journal of Comparative Neurology 498:227–251.

    https://doi.org/10.1002/cne.21048

    • PubMed
    • Google Scholar
75. Conference

    1. Pierpaoli C
    2. Walker L
    3. Walker MO
    4. Barnett A
    5. Basser P
    6. Chang LC
    7. Wu M

    (2010)

    TORTOISE: an integrated software package for processing of diffusion MRI data

    ISMRM 18th Annual Meeting.

    • Google Scholar
76. 1. Pollen AA
    2. Hofmann HA

    (2008) Beyond neuroanatomy: novel approaches to studying brain evolution

    Brain, Behavior and Evolution 72:145–158.

    https://doi.org/10.1159/000151474

    • PubMed
    • Google Scholar
77. 1. Reveley C
    2. Seth AK
    3. Pierpaoli C
    4. Silva AC
    5. Yu D
    6. Saunders RC
    7. Leopold DA
    8. Ye FQ

    (2015) Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography

    PNAS 112:E2820–E2828.

    https://doi.org/10.1073/pnas.1418198112

    • PubMed
    • Google Scholar
78. 1. Roebroeck A
    2. Galuske R
    3. Formisano E
    4. Chiry O
    5. Bratzke H
    6. Ronen I
    7. Kim DS
    8. Goebel R

    (2008) High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T

    NeuroImage 39:157–168.

    https://doi.org/10.1016/j.neuroimage.2007.08.015

    • PubMed
    • Google Scholar
79. 1. Rokem A
    2. Takemura H
    3. Bock AS
    4. Scherf KS
    5. Behrmann M
    6. Wandell BA
    7. Fine I
    8. Bridge H
    9. Pestilli F

    (2017) The visual white matter: The application of diffusion MRI and fiber tractography to vision science

    Journal of Vision 17:4.

    https://doi.org/10.1167/17.2.4

    • PubMed
    • Google Scholar
80. 1. Rubner Y
    2. Tomasi C
    3. Guibas LJ

    (2000)

    The earth mover’s distance as a metric for image retrieval

    International Journal of Computer Vision 40:99–121.

    • Google Scholar
81. Book

    1. Saleem KS
    2. Logothetis NK

    (2007)

    A Combined MRI and Histology Atlas of the Rhesus Monkey Brain Instereotaxic Coordinates

    London: Academic.

    • Google Scholar
82. 1. Scalaidhe SP
    2. Albright TD
    3. Rodman HR
    4. Gross CG

    (1995) Effects of superior temporal polysensory area lesions on eye movements in the macaque monkey

    Journal of Neurophysiology 73:1–19.

    https://doi.org/10.1152/jn.1995.73.1.1

    • PubMed
    • Google Scholar
83. 1. Schall JD
    2. Hanes DP

    (1993) Neural basis of saccade target selection in frontal eye field during visual search

    Nature 366:467–469.

    https://doi.org/10.1038/366467a0

    • PubMed
    • Google Scholar
84. 1. Schall JD
    2. Morel A
    3. King DJ
    4. Bullier J

    (1995) Topography of visual cortex connections with frontal eye field in macaque: convergence and segregation of processing streams

    The Journal of Neuroscience 15:4464–4487.

    https://doi.org/10.1523/JNEUROSCI.15-06-04464.1995

    • Google Scholar
85. 1. Schmahmann JD
    2. Pandya DN
    3. Wang R
    4. Dai G
    5. D'Arceuil HE
    6. de Crespigny AJ
    7. Wedeen VJ

    (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography

    Brain 130:630–653.

    https://doi.org/10.1093/brain/awl359

    • PubMed
    • Google Scholar
86. Book

    1. Schmahmann JD
    2. Pandya DN

    (2006)

    Fiber Pathways of the Brain

    Oxford University Press.

    • Google Scholar
87. 1. Schwiedrzik CM
    2. Zarco W
    3. Everling S
    4. Freiwald WA

    (2015) Face patch resting state networks link face processing to social cognition

    PLOS Biology 13:e1002245.

    https://doi.org/10.1371/journal.pbio.1002245

    • Google Scholar
88. 1. Sliwa J
    2. Freiwald WA

    (2017) A dedicated network for social interaction processing in the primate brain

    Science 356:745–749.

    https://doi.org/10.1126/science.aam6383

    • PubMed
    • Google Scholar
89. 1. Stemmann H
    2. Freiwald WA

    (2016) Attentive motion discrimination recruits an area in inferotemporal cortex

    The Journal of Neuroscience 36:11918–11928.

    https://doi.org/10.1523/JNEUROSCI.1888-16.2016

    • PubMed
    • Google Scholar
90. 1. Stephan KE
    2. Kamper L
    3. Bozkurt A
    4. Burns GA
    5. Young MP
    6. Kötter R

    (2001) Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac)

    Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356:1159–1186.

    https://doi.org/10.1098/rstb.2001.0908

    • PubMed
    • Google Scholar
91. 1. Takemura H
    2. Caiafa CF
    3. Wandell BA
    4. Pestilli F

    (2016a) Ensemble tractography

    PLOS Computational Biology 12:e1004692.

    https://doi.org/10.1371/journal.pcbi.1004692

    • PubMed
    • Google Scholar
92. 1. Takemura H
    2. Rokem A
    3. Winawer J
    4. Yeatman JD
    5. Wandell BA
    6. Pestilli F

    (2016b) A major human white matter pathway between dorsal and ventral visual cortex

    Cerebral Cortex 26:2205–2214.

    https://doi.org/10.1093/cercor/bhv064

    • PubMed
    • Google Scholar
93. 1. Takemura H
    2. Pestilli F
    3. Weiner KS
    4. Keliris GA
    5. Landi SM
    6. Sliwa J
    7. Ye FQ
    8. Barnett MA
    9. Leopold DA
    10. Freiwald WA
    11. Logothetis NK
    12. Wandell BA

    (2017) Occipital white matter tracts in human and macaque

    Cerebral Cortex 27:3346–3359.

    https://doi.org/10.1093/cercor/bhx070

    • PubMed
    • Google Scholar
94. 1. Taoka T
    2. Morikawa M
    3. Akashi T
    4. Miyasaka T
    5. Nakagawa H
    6. Kiuchi K
    7. Kishimoto T
    8. Kichikawa K

    (2009) Fractional anisotropy--threshold dependence in tract-based diffusion tensor analysis: evaluation of the uncinate fasciculus in Alzheimer disease

    American Journal of Neuroradiology 30:1700–1703.

    https://doi.org/10.3174/ajnr.A1698

    • PubMed
    • Google Scholar
95. 1. Theys T
    2. Romero MC
    3. van Loon J
    4. Janssen P

    (2015) Shape representations in the primate dorsal visual stream

    Frontiers in Computational Neuroscience 9:43.

    https://doi.org/10.3389/fncom.2015.00043

    • PubMed
    • Google Scholar
96. 1. Thiebaut de Schotten M
    2. Dell'Acqua F
    3. Forkel SJ
    4. Simmons A
    5. Vergani F
    6. Murphy DG
    7. Catani M

    (2011) A lateralized brain network for visuospatial attention

    Nature Neuroscience 14:1245–1246.

    https://doi.org/10.1038/nn.2905

    • PubMed
    • Google Scholar
97. 1. Thiebaut de Schotten M
    2. Dell'Acqua F
    3. Valabregue R
    4. Catani M

    (2012) Monkey to human comparative anatomy of the frontal lobe association tracts

    Cortex 48:82–96.

    https://doi.org/10.1016/j.cortex.2011.10.001

    • PubMed
    • Google Scholar
98. 1. Thiebaut de Schotten M
    2. Cohen L
    3. Amemiya E
    4. Braga LW
    5. Dehaene S

    (2014) Learning to read improves the structure of the arcuate fasciculus

    Cerebral Cortex 24:989–995.

    https://doi.org/10.1093/cercor/bhs383

    • PubMed
    • Google Scholar
99. 1. Thomas C
    2. Ye FQ
    3. Irfanoglu MO
    4. Modi P
    5. Saleem KS
    6. Leopold DA
    7. Pierpaoli C

    (2014) Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

    PNAS 111:16574–16579.

    https://doi.org/10.1073/pnas.1405672111

    • PubMed
    • Google Scholar
100. 1. Thomason ME
     2. Thompson PM

     (2011) Diffusion imaging, white matter, and psychopathology

     Annual Review of Clinical Psychology 7:63–85.

     https://doi.org/10.1146/annurev-clinpsy-032210-104507

     • PubMed
     • Google Scholar
101. 1. Thompson KG
     2. Bichot NP

     (2005) A visual salience map in the primate frontal eye field

     Progress in Brain Research 147:251–262.

     https://doi.org/10.1016/S0079-6123(04)47019-8

     • PubMed
     • Google Scholar
102. 1. Tournier J-D
     2. Calamante F
     3. Connelly A

     (2007) Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

     NeuroImage 35:1459–1472.

     https://doi.org/10.1016/j.neuroimage.2007.02.016

     • Google Scholar
103. 1. Tournier J-D
     2. Calamante F
     3. Connelly A

     (2012) MRtrix: Diffusion tractography in crossing fiber regions

     International Journal of Imaging Systems and Technology 22:53–66.

     https://doi.org/10.1002/ima.22005

     • Google Scholar
104. 1. Treisman AM
     2. Gelade G

     (1980) A feature-integration theory of attention

     Cognitive Psychology 12:97–136.

     https://doi.org/10.1016/0010-0285(80)90005-5

     • PubMed
     • Google Scholar
105. 1. Tsao DY
     2. Freiwald WA
     3. Tootell RB
     4. Livingstone MS

     (2006) A cortical region consisting entirely of face-selective cells

     Science 311:670–674.

     https://doi.org/10.1126/science.1119983

     • PubMed
     • Google Scholar
106. 1. Umarova RM
     2. Saur D
     3. Schnell S
     4. Kaller CP
     5. Vry MS
     6. Glauche V
     7. Rijntjes M
     8. Hennig J
     9. Kiselev V
     10. Weiller C

     (2010) Structural connectivity for visuospatial attention: significance of ventral pathways

     Cerebral Cortex 20:121–129.

     https://doi.org/10.1093/cercor/bhp086

     • PubMed
     • Google Scholar
107. 1. van Polanen V
     2. Davare M

     (2015) Interactions between dorsal and ventral streams for controlling skilled grasp

     Neuropsychologia 79:186–191.

     https://doi.org/10.1016/j.neuropsychologia.2015.07.010

     • PubMed
     • Google Scholar
108. 1. Wandell BA

     (2016) Clarifying human white matter

     Annual Review of Neuroscience 39:103–128.

     https://doi.org/10.1146/annurev-neuro-070815-013815

     • PubMed
     • Google Scholar
109. 1. Watson RT
     2. Valenstein E
     3. Day A
     4. Heilman KM

     (1994)

     Posterior neocortical systems subserving awareness and neglect. Neglect associated with superior temporal sulcus but not area 7 lesions

     Archives of Neurology 51:1014–1021.

     • PubMed
     • Google Scholar
110. 1. Wedeen VJ
     2. Wang RP
     3. Schmahmann JD
     4. Benner T
     5. Tseng WY
     6. Dai G
     7. Pandya DN
     8. Hagmann P
     9. D'Arceuil H
     10. de Crespigny AJ

     (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers

     NeuroImage 41:1267–1277.

     https://doi.org/10.1016/j.neuroimage.2008.03.036

     • PubMed
     • Google Scholar
111. 1. Weiner KS
     2. Yeatman JD
     3. Wandell BA

     (2017) The posterior arcuate fasciculus and the vertical occipital fasciculus

     Cortex 97:274–276.

     https://doi.org/10.1016/j.cortex.2016.03.012

     • PubMed
     • Google Scholar
112. 1. Wilke M
     2. Kagan I
     3. Andersen RA

     (2012) Functional imaging reveals rapid reorganization of cortical activity after parietal inactivation in monkeys

     PNAS 109:8274–8279.

     https://doi.org/10.1073/pnas.1204789109

     • PubMed
     • Google Scholar
113. 1. Yeatman JD
     2. Dougherty RF
     3. Myall NJ
     4. Wandell BA
     5. Feldman HM

     (2012) Tract profiles of white matter properties: automating fiber-tract quantification

     PLoS ONE 7:e49790.

     https://doi.org/10.1371/journal.pone.0049790

     • PubMed
     • Google Scholar
114. 1. Yeatman JD
     2. Rauschecker AM
     3. Wandell BA

     (2013) Anatomy of the visual word form area: adjacent cortical circuits and long-range white matter connections

     Brain and Language 125:146–155.

     https://doi.org/10.1016/j.bandl.2012.04.010

     • PubMed
     • Google Scholar
115. 1. Yeatman JD
     2. Weiner KS
     3. Pestilli F
     4. Rokem A
     5. Mezer A
     6. Wandell BA

     (2014) The vertical occipital fasciculus: a century of controversy resolved by in vivo measurements

     PNAS 111:E5214–E5223.

     https://doi.org/10.1073/pnas.1418503111

     • PubMed
     • Google Scholar
116. 1. Yushkevich PA
     2. Piven J
     3. Hazlett HC
     4. Smith RG
     5. Ho S
     6. Gee JC
     7. Gerig G

     (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability

     NeuroImage 31:1116–1128.

     https://doi.org/10.1016/j.neuroimage.2006.01.015

     • PubMed
     • Google Scholar
117. 1. Zhang D
     2. Guo L
     3. Zhu D
     4. Li K
     5. Li L
     6. Chen H
     7. Zhao Q
     8. Hu X
     9. Liu T

     (2013) Diffusion tensor imaging reveals evolution of primate brain architectures

     Brain Structure and Function 218:1429–1450.

     https://doi.org/10.1007/s00429-012-0468-4

     • PubMed
     • Google Scholar

Author details

1. Ilaria Sani

   Laboratory of Neural Systems, The Rockefeller University, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   isani01@rockefeller.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4389-7263

2. Brent C McPherson

   Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

3. Heiko Stemmann

   Institute for Brain Research and Center for Advanced Imaging, University of Bremen, Bremen, Germany

   Contribution

   Conceptualization, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7492-1115

4. Franco Pestilli

   Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States

   Contribution

   Conceptualization, Resources, Software, Supervision, Funding acquisition, Writing—review and editing

   Contributed equally with

   Winrich A Freiwald

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2469-0494

5. Winrich A Freiwald

   Laboratory of Neural Systems, The Rockefeller University, New York, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing—review and editing

   Contributed equally with

   Franco Pestilli

   For correspondence

   wfreiwald@rockefeller.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8456-5030

• 3,087

  views

• 476

  downloads

• 30

  citations

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