The evolution of prefrontal inputs to the cortico-pontine system: Diffusion imaging evidence from macaque monkeys and humans

Cognitive Neuroscience Laboratory, Department of Psychology, Royal Holloway University of London, UK.
Cerebral Cortex (Impact Factor: 8.67). 07/2006; 16(6):811-8. DOI: 10.1093/cercor/bhj024
Source: PubMed


The cortico-ponto-cerebellar system is one of the largest projection systems in the primate brain, but in the human brain the nature of the information processing in this system remains elusive. Determining the areas of the cerebral cortex which contribute projections to this system will allow us to better understand information processing within it. Information from the cerebral cortex is conveyed to the cerebellum by topographically arranged fibres in the cerebral peduncle - an important fibre system in which all cortical outputs spatially converge on their way to the cerebellum via the pontine nuclei. Little is known of their anatomical organization in the human brain. New in vivo diffusion imaging and probabilistic tractography methods now offer a way in which input tracts in the cerebral peduncle can be characterized in detail. Here we use these methods to contrast their organization in humans and macaque monkeys. We confirm the dominant contribution of the cortical motor areas to the macaque monkey cerebral peduncle. However, we also present novel anatomical evidence for a relatively large prefrontal contribution to the human cortico-ponto-cerebellar system in the cerebral peduncle. These findings suggest the selective evolution of prefrontal inputs to the human cortico-ponto-cerebellar system.

1 Follower
31 Reads
  • Source
    • "), a distinction also supported by their connectivity to the basal ganglia and cerebellum (Akkal et al., 2007; Lehericy et al., 2004a; Ramnani et al., 2006). Although less a focus of earlier investigations, the cortex located anterior to pre-SMA (BA 8/9) was distinguished from the pre-SMA in some studies (Lehericy et al., 2004b; Petrides and Pandya, 2007; Zhang et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Previous studies have examined the neural correlates of proactive control using a variety of behavioral paradigms; however, the neural network relating the control process to its behavioral consequence remains unclear. Here, we applied a dynamic Bayesian model to a large fMRI data set of the stop signal task to address this issue. By estimating the probability of the stop signal - p(Stop) - trial by trial, we showed that higher p(Stop) is associated with prolonged go trial reaction time (RT), indicating proactive control of motor response. In modeling fMRI signals at trial and target onsets, we distinguished activities of proactive control, prediction error, and RT slowing. We showed that the anterior pre-supplementary motor area (pre-SMA) responds specifically to increased stop signal likelihood, and its activity is correlated with activations of the posterior pre-SMA and bilateral anterior insula during prolonged response times. This directional link is also supported by Granger causality analysis. Furthermore, proactive control, prediction error, and time-on-task are each mapped to distinct areas in the medial prefrontal cortex. Together, these findings dissect regional functions of the medial prefrontal cortex in cognitive control and provide systems level evidence associating conflict anticipation to its motor consequence. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 06/2015; 119. DOI:10.1016/j.neuroimage.2015.06.032 · 6.36 Impact Factor
    • "Current cognitive models posit that the basal ganglia (and specifically the caudate) and the cerebrocerebellar circuitry systems ''train'' the prefrontal cortex during the course of development (Antzoulatos & Miller, 2014; Koziol, Budding, & Chidekel, 2012; Koziol & Lutz, 2013). Ramnani has proposed that the cerebellum provides the cerebral cortex with the input necessary for the automation of skilled cognitive operations (Koziol, Budding, Andreasen et al., 2013; Ramnani et al., 2005). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This article describes an interactive paradigm for understanding brain functioning. This model requires both explicit and implicit learning processes. This paradigm is illustrated through the interpretation of practical examples of behavior. Applications of current neuropsychological tests are presented within this interactive paradigm. The development of new neuropsychological tests is presented, as derived from experimental test paradigms that evaluate implicit learning processes.
    10/2014; 3(4):264-273. DOI:10.1080/21622965.2014.946809
  • Source
    • "Most importantly, however, the finding of NCB/NCX ratios that do not decrease as the rest of brain grains neurons indicates that the relative expansion in numbers of neurons in the cerebral cortex over the rest of brain in primates and artiodactyls is matched by a similar expansion of the neuronal population in the cerebellum. This concerted addition of neurons to the cerebral cortex and cerebellum is consistent with the findings that, in primates, the cerebellum, neocortex, vestibular nuclei and relays between them exhibit concerted volumetric evolution, even after removing the effects of change in other structures (Whiting and Barton, 2003), and that increases in the volume of the prefrontal cerebral cortex are accompanied by increases in the volume of the prefrontal cortico-pontine system and prefrontal-projecting cerebellar lobules (Ramnani et al., 2006; Balsters et al., 2010). The concerted scaling of numbers of neurons in the cerebral cortex and cerebellum also agree with recent models of brain function that consider that these two structures work in conjunction (Leiner et al., 1989; Ramnani, 2006; Ito, 2008), instead of endorsing a functional preponderance of the cerebral cortex over the cerebellum. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Enough species have now been subject to systematic quantitative analysis of the relationship between the morphology and cellular composition of their brain that patterns begin to emerge and shed light on the evolutionary path that led to mammalian brain diversity. Based on an analysis of the shared and clade-specific characteristics of 41 modern mammalian species in 6 clades, and in light of the phylogenetic relationships among them, here we propose that ancestral mammal brains were composed and scaled in their cellular composition like modern afrotherian and glire brains: with an addition of neurons that is accompanied by a decrease in neuronal density and very little modification in glial cell density, implying a significant increase in average neuronal cell size in larger brains, and the allocation of approximately 2 neurons in the cerebral cortex and 8 neurons in the cerebellum for every neuron allocated to the rest of brain. We also propose that in some clades the scaling of different brain structures has diverged away from the common ancestral layout through clade-specific (or clade-defining) changes in how average neuronal cell mass relates to numbers of neurons in each structure, and how numbers of neurons are differentially allocated to each structure relative to the number of neurons in the rest of brain. Thus, the evolutionary expansion of mammalian brains has involved both concerted and mosaic patterns of scaling across structures. This is, to our knowledge, the first mechanistic model that explains the generation of brains large and small in mammalian evolution, and it opens up new horizons for seeking the cellular pathways and genes involved in brain evolution.
    Frontiers in Neuroanatomy 08/2014; 8:77. DOI:10.3389/fnana.2014.00077 · 3.54 Impact Factor
Show more

Similar Publications