The Evolution of Complex Connectivity in Speech Networks

Summary: A new paper considers how the complex speech networks in the brain may have evolved.

Source: Nottingham Trent University.

Defining the underlying anatomical structure of complex brain functions, such as speech, is vital in improving our knowledge of how the human brain works. Gaining a clear picture of how cortical and sub-cortical networks differ between species is of particular importance in determining how complex human brain networks might have evolved.

Kumar et al (2016) provide a valuable insight into the differences in anatomical structure of human and primate laryngeal motor cortical (LMC) speech networks in their recently published paper ‘Structural Organization of the Laryngeal Motor Cortical Network and Its Implication for Evolution of Speech Production’. Speech is of great interest in the realm of brain network evolution and in gaining an understanding of the development of complex functions. The findings presented by Kumar et al (2016) show clear homologies between human and macaque LMC speech networks, in terms of connections being visible in the same cortical and subcortical regions. These observations are synonymous with previously described connections (Jurgens, 2002).

Although LMC connectivity has already been somewhat defined by tracer and diffusion tractography studies (Simonyan & Jurgens, 2002, 2003, 2005a, 2005b), Kumar et al (2016) are the first to directly compare LMC speech networks between humans and non-primates in the same study, with the intention of revealing evolutionary structural changes. A factor often overlooked in comparative studies of human and non-human brains is the application of comparable methodologies. For instance, it is often the case in neuroanatomy that we as researchers use our knowledge to project animal findings onto the human brain, or to compare in vivo findings from animals to post-mortem findings from humans. Although this approach is useful in making reasonable conclusions, a direct comparison offers a more reliable investigation.

In terms of the evolution of the LMC speech network, a detailed network analysis and application of graph measures could provide fine scale information regarding the structure of speech networks and pathways, enabling a clearer understanding of how such complex networks may have evolved. Image is for illustrative purposes only.
In terms of the evolution of the LMC speech network, a detailed network analysis and application of graph measures could provide fine scale information regarding the structure of speech networks and pathways, enabling a clearer understanding of how such complex networks may have evolved. Image is for illustrative purposes only.

Kumar et al (2016) have clearly and concisely described the differences and homologies in structural connectivity of LMC in humans and macaques, providing an excellent starting point in deciphering the LMC network differences. The findings of Kumar et al (2016) clearly demonstrate that the majority (48.9%) of LMC tracts in human brains connected to the inferior parietal lobule (IPL), whereas in macaque brains the majority (43.1%) of LMC tracts connected to Brodmann area 44 (IPL connections = 5.6%). This shows a clear difference in the connectivity of speech networks in the two species, giving a strong indication for how such human networks might have evolved, or what they evolved from. The increased parietal connectivity found here implies an increased importance of planning, spatial attention and reasoning within human speech networks compared to non-human primates.

The connectional observations described by Kumar et al (2016) show how the distribution of connections within fairly similar networks can differ greatly between species. It is in the fine detail of these networks that the key differences, and therefore possible evolutionary changes can be identified. Kumar et al (2016) suggested that the complex human speech network evolved from the existing non-human primate network, with the additional development of LMC – parietal connections which allowed for the higher-order sensorimotor coordination necessary for complex speech production. It would be interesting to extend this study and observe how LMC tracts differ in other primate species, perhaps those more closely related to humans than macaques.

Kumar et al (2016) have provided a vital starting point to unravelling the evolutionary path of complex human speech. It would be interesting to see what can be revealed from a more in depth analysis of the data. The results presented here show promising and significant differences in the target projection regions from LMC between primate and human brains. It is clear from the tractography results here that there are significantly more connections from LMC to parietal regions observed in humans compared to other primates.

There is growing evidence within the literature of a connectivity gradient in terms of abstraction. This is especially apparent in the prefrontal cortex (Ramnani & Owen, 2004; Taren et al, 2011; Christoff et al, 2009) and is supported by anatomical data (Olson & Musil, 1992; Petrides and Pandya, 1999; Bedwell et al, 2014b). Prefrontal connections to both temporal and sensory-motor regions show evidence of changes in the arrangement of connections, particularly in terms of reciprocity, as cortical areas are thought to become more abstract and more complex in their functionality. This organisational gradient may also apply to the evolution of networks. Taking the assumption that speech networks become increasingly complex through evolution, from non-human primate to human, it is reasonable to suggest that the arrangement of speech connections may follow an evolutionary gradient in terms of complexity, perhaps also decreasing in reciprocity.

Further analysis of the data presented by Kumar et al could provide some interesting insight into how increases in complex function through evolution relates to the organisational gradient seen in the increase in complex function in the same brain (Christoff et al, 2009).

Additional numerical analysis of the three dimensional locations of targets and their ordering (such as that reported in Bedwell et al, 2014a & 2015) could reveal structural properties and differences in the arrangement of connections between species which are otherwise not observed. The application of graph theoretical analysis of data derived from diffusion tensor imaging has revealed otherwise unobserved structural properties of networks across the human cerebral cortex (Gong et al, 2009) and has assisted in the description of structural network changes as a result of substance abuse (Kim et al, 2011). In terms of the evolution of the LMC speech network, a detailed network analysis and application of graph measures could provide fine scale information regarding the structure of speech networks and pathways, enabling a clearer understanding of how such complex networks may have evolved.

As described by Pearcy et al (2015), organisms can often be classified in terms of graph motifs, and follow a gradient in terms of their complexity. This could indicate an evolutionary gradient. It would be of interest to apply graph measures similar those employed by Pearcy et al (2015) and Gong et al (2009) to the speech networks of primates and humans. This could enable us to reveal how LMC structural networks might have evolved in their complex structure.

Kumar et al (2016) acknowledge the breadth of knowledge developed in the field of brain connections that has been gained from neuroanatomical tracing. It is worth mentioning that neuroanatomical tracing remains to be an important tool in the identification of cortical networks. An extension to the present study, systematically employing fluorescent tracers and a detailed numerical analysis, or perhaps using previously collected data (Simonyan et al 2002, 2003, 2005a, 2005b), could provide valuable additional data regarding the finer scale connectivity within these speech networks. Such fine scale organisation, down to a single cell level, can currently only be obtained with the use of anatomical tracers. Although tractography data is very useful in identifying and exploring networks at a macroscopic level, to gain a fine scale understanding of network evolution an additional microscopic analysis would be beneficial. This could be carried out both in vivo and post-mortem with various tracers, therefore allowing the continued use of human brain tissue. The possibility of combining data from tractography and tracer studies increases the possibilities in terms of analysis.

The discussion presented by Kumar et al (2016) raises many questions in terms of further investigation. Kumar et al (2016)’s findings will hopefully inspire increased analysis of this pathway in order to establish whether human connections in the LMC speech pathway are organised in the same manner as macaque in terms of fine scale properties, or are they arranged differently, perhaps in a more complex arrangement? Providing an answer to this could help to identify how speech networks evolved, and how this evolution compares to other high order functions.

About this neuroscience article

References:

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Note: NeuroscienceNews.com would like to thank Dr. Stacey A. Bedwell, Division of Psychology at Nottingham Trent University, for submitting this original article directly to us for inclusion on the website. Dr. Stacey A. Bedwell has no conflicts of interest to disclose.

Source: Stacey A. BedwellNottingham Trent University
Image Source: This NeuroscienceNews.com image is in the public domain.

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MLA:

Nottingham Trent University. “The Evolution of Complex Connectivity in Speech Networks.” NeuroscienceNews. NeuroscienceNews, 26 May 2016.
<https://neurosciencenews.com/speech-network-connectivity-4320/>.

APA:

Nottingham Trent University. (2016, May 26). The Evolution of Complex Connectivity in Speech Networks. NeuroscienceNews. Retrieved May 26, 2016 from https://neurosciencenews.com/speech-network-connectivity-4320/

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Nottingham Trent University. “The Evolution of Complex Connectivity in Speech Networks.” https://neurosciencenews.com/speech-network-connectivity-4320/ (accessed May 26, 2016).

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