Morphological and Functional Principles Governing the Plasticity Reserve in the Cerebellum: The Cortico-Deep Cerebellar Nuclei Loop Model

Abstract

Cerebellar reserve compensates for and restores functions lost through cerebellar damage. This is a fundamental property of cerebellar circuitry. Clinical studies suggest (1) the involvement of synaptic plasticity in the cerebellar cortex for functional compensation and restoration, and (2) that the integrity of the cerebellar reserve requires the survival and functioning of cerebellar nuclei. On the other hand, recent physiological studies have shown that the internal forward model, embedded within the cerebellum, controls motor accuracy in a predictive fashion, and that maintaining predictive control to achieve accurate motion ultimately promotes learning and compensatory processes. Furthermore, within the proposed framework of the Kalman filter, the current status is transformed into a predictive state in the cerebellar cortex (prediction step), whereas the predictive state and sensory feedback from the periphery are integrated into a filtered state at the cerebellar nuclei (filtering step). Based on the abovementioned clinical and physiological studies, we propose that the cerebellar reserve consists of two elementary mechanisms which are critical for cerebellar functions: the first is involved in updating predictions in the residual or affected cerebellar cortex, while the second acts by adjusting its updated forecasts with the current status in the cerebellar nuclei. Cerebellar cortical lesions would impair predictive behavior, whereas cerebellar nuclear lesions would impact on adjustments of neuronal commands. We postulate that the multiple forms of distributed plasticity at the cerebellar cortex and cerebellar nuclei are the neuronal events which allow the cerebellar reserve to operate in vivo. This cortico-deep cerebellar nuclei loop model attributes two complementary functions as the underpinnings behind cerebellar reserve.

Keywords: cerebellar ataxias; cerebellar reserve; internal forward model; long-term depression; predictive control; synaptic plasticity.

Figure 3

Computational diagrams of internal models. The internal forward model integrates sensory feedback signals and the efference copy of a control signal and predicts the current status of the body. Citation from our previous paper [39].

Figure 1

A scheme of the cerebellar neural circuit. MF, mossy fiber; CF, climbing fiber; GC, granule cell; PF, parallel fiber; PCs, Purkinje cells; GABAergic IN, stellate and basket cells; + excitation [open circles, excitatory neurons]; − inhibitory [filled circles, inhibitory neurons].

Figure 2

A scheme of microzones. Functional congruence between the 2 major input systems (mossy fibers, climbing fibers) is observed anatomically, with a contribution of mossy fibers into multizonal microcomplexes integrated in cerebellar modules subserving the operational aspects of the cerebellar machinery. PF: parallel fiber, CF: climbing fiber, GC: granule cell, Go: Golgi cell, Bc: basket cell, IO: inferior olive. Citation from our previous paper [5], with permission.

Figure 4

Summary schematic of the internal forward model in the cerebellar cortex. MF, mossy fiber (red); PC, Purkinje cell (green); DC, dentate cell (light blue). Granule cells (orange) and inhibitory interneurons (blue). The linear equations of neuron activities resemble those of an estimator known as the Kalman filter. Citation from our previous paper [39].

Figure 5

A schematic diagram of circuits within the cerebellar cortex, cited from our previous paper [13]. PC: Purkinje cell, GC: granule cell, INs: molecular layer interneurons; Golgi: Golgi cell; MF: mossy fiber, PF: parallel fiber, CF: climbing fiber. Two cellular mechanisms underlying cerebellar reserve are illustrated. (1) Multiple forms of synaptic plasticity (illustrated by stars) co-exist in the cerebellar cortex. (2) Convergence and divergence of MF inputs. For example, MF1 innervates both microzones A and B. Different MFs converge simultaneously to multiple microzones. Thus, a single microzone receives abundant central and peripheral inputs through MF, which results in redundancy of information.