Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

The structure and connectivity of the nervous system of the nematode Caenorhabditis elegans has been deduced from reconstructions of electron micrographs of serial sections. The hermaphrodite nervous system has a total complement of 302 neurons, which are arranged in an essentially invariant structure. Neurons with similar morphologies and connectivities have been grouped together into classes; there are 118 such classes. Neurons have simple morphologies with few, if any, branches. Processes from neurons run in defined positions within bundles of parallel processes, synaptic connections being made en passant. Process bundles are arranged longitudinally and circumferentially and are often adjacent to ridges of hypodermis. Neurons are generally highly locally connected, making synaptic connections with many of their neighbours. Muscle cells have arms that run out to process bundles containing motoneuron axons. Here they receive their synaptic input in defined regions along the surface of the bundles, where motoneuron axons reside. Most of the morphologically identifiable synaptic connections in a typical animal are described. These consist of about 5000 chemical synapses, 2000 neuromuscular junctions and 600 gap junctions.

The structure of the nervous system of the nematode Caenorhabditis elegans

The ionotropic glutamate receptors are ligand-gated ion channels that mediate the vast majority of excitatory neurotransmission in the brain. The cloning of cDNAs encoding glutamate receptor subunits, which occurred mainly between 1989 and 1992 ([Hollmann and Heinemann, 1994][1]), stimulated this

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The glutamate receptor ion channels

Manuscript in preparation

Schultz, Wolfram. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest t...

/pdf/predictive-reward-signal-of-dopamine-neurons-3f94myirza.pdf

Predictive Reward Signal of Dopamine Neurons

The cerebellum as a neuronal machine

The cerebellum and neural control

LTD has now been established as a synaptic plasticity specific to the cerebellum. Cellular and molecular mechanisms of LTD have been elucidated to some extent, but still a number of questions are left open. The most crucial question may concern its time course, as to how long the LTD lasts beyond the limit of the present maximum observation time of 3 hr, and whether and how it is eventually transformed to a permanent memory. Molecular mechanisms underlying LTD should be investigated further in respect to Ca2+ binding and storage, protein kinase C, phosphorylation of glutamate receptors, GTP proteins, etc. The ineffectiveness of mass field potentials in representing LTD makes such studies relatively difficult, and a hope for future development may be placed in reproduction of LTD in tissue cultured Purkinje cells or even in isolated glutamate receptors in a simplified form. The cerebellar neuronal network incorporating LTD as a memory element has been conceived as a simple perceptron-like (Albus 1971) or adaptive filter-like (Fujita 1982a) parallel processing computer. Such a neuronal computer incorporated in a reflex or a more complex movement system would endow the system with subtle capabilities of adaptation and learning. The scheme of the floccular control of the VOR closely resembles that of a self-tuning regulator, a type of adaptive control system. For cerebellar control of voluntary movements, however, another version of the adaptive control system, the model reference control system, seems to be more applicable (Ito 1986). This system continuously readjusts its dynamics by referring to errors derived through comparison of its performance with that of an internal model. It is important to note that a model for an unknown system can be built based on the same principle, by feeding errors derived from their comparison to adjust the model. It may thus be conceived that an internal model is built within the cerebellum in the manner of model reference adaptive control, and that an internal model so formed is utilized for adaptive control of movement. A recent simulation study successfully reproduced learning in formation of an arm trajectory based on these principles of model reference control (Kawato et al 1987). On the experimental side, however, the complex neural organization for control of locomotion, posture, and voluntary movements still eludes full elucidation. Nevertheless, evidence is accumulating to support the cerebellar learning hypothesis.(ABSTRACT TRUNCATED AT 400 WORDS)

Long-term depression

The intricate neuronal circuitry of the cerebellum is thought to encode internal models that reproduce the dynamic properties of body parts. These models are essential for controlling the movement of these body parts: they allow the brain to precisely control the movement without the need for sensory feedback. It is thought that the cerebellum might also encode internal models that reproduce the essential properties of mental representations in the cerebral cortex. This hypothesis suggests a possible mechanism by which intuition and implicit thought might function and explains some of the symptoms that are exhibited by psychiatric patients. This article examines the conceptual bases and experimental evidence for this hypothesis.

Control of mental activities by internal models in the cerebellum

Cerebellar Purkinje cells exhibit a unique type of synaptic plasticity, namely, long-term depression (LTD). When two inputs to a Purkinje cell, one from a climbing fiber and the other from a set of...

https://www.physiology.org/doi/pdf/10.1152/physrev.2001.81.3.1143

Masao Ito

Papers

The cerebellum as a neuronal machine

The cerebellum and neural control

Long-term depression

Control of mental activities by internal models in the cerebellum

Cerebellar Long-Term Depression: Characterization, Signal Transduction, and Functional Roles