When considering cognitive rehabilitation, an important distinction to make is whether the focus of rehabilitative efforts is on compensation or restitution. Compensation refers to the use of strategies and/or external aids in order to circumvent cognitive dysfunction and allow a person to successfully carry out a task. In contrast, the goal of restitution is to reorganize underlying neural circuitry and attempt to improve damaged cognitive functions (Sohlberg & Mateer, 2001). A critical concept that is related to the latter method (restitution) is neuroplasticity.
Neuroplasticity refers to the brain’s ability to change and alter its structure and function. Within the CNS, neuroplasticity is sustained by a variety of changes in grey matter (e.g., changes in neural morphology), white matter (e.g., changes in axonal branching, myelination), as well as other tissue compartments (e.g., glial cell size and number) (Zatorre, Fields, & Johansen-Berg, 2012). Though there are various mechanisms which underlie neuroplasticity after brain injury and which have significant implications for rehabilitation, the one that is most relevant to computerized cognitive training and which will be reviewed here is modification of synaptic connectivity (Sohlberg & Mateer, 2001). This process refers to the phenomenon whereby following brain injury, neurons that have lost input from a damaged neuron develop new dendrites to receive information from another neuron either in the same circuit or from another circuit further away. Such synaptic plasticity is ongoing and is in the process that underlies associative learning as well as experience-dependent learning. That is to say, if there were no
36 inputs to drive the system, such new connections would not be able to form. A critical
implication of this concept with respect to rehabilitation is that differences in an individual’s experience following injury will inevitably shape synaptic interconnections, which in turn, will influence one’s recovery (Robertson & Murre, 1999a).
Hebbian learning is an important concept that helps to understand the neural basis of behaviour. Popularly referred to as the “cells that fire together, wire together” phenomenon, Hebb (Hebb, 1949) argued that synaptic connections are strengthened when two neurons or groups of neurons that have been disconnected by a lesion become reconnected if they are activated at the same time. Moreover, it is through several repetitions of such simultaneous activation that the two disconnected neurons (or groups of neurons) become reconnected. There is plenty of evidence to support this phenomenon, including intracortical microstimulation research, whereby electrically stimulating cortical cells to fire in temporal proximity has been shown to strengthen synaptic connections between them (Dinse, Recanzone, & Merzenich, 1993).
Guided recovery. There is evidence to suggest that a triage of recovery patterns after brain injury exists: some individuals appear to recover spontaneously; some show very little or incomplete recovery even over several years; and some show recovery, but this recovery seems to depend on rehabilitative input (Robertson & Murre, 1999b). It is this third group that is referred to as the ‘guided recovery’ group and for which the focus is on restitution-oriented rehabilitation, in particular, Hebbian learning-based reconnection. Facilitating recovery among damaged neural networks appears to benefit from additional external structured input (Robertson & Murre, 1999b). For example, one study (Mayer, Brown, Dunnett, & Robbins, 1992) showed
37 that rats given striatal neural transplants only benefited from transplants when they were
provided with the opportunity for perceptuomotor learning. That is, in the absence of behavioural driving of the neural tissue, sufficient connectivity did not develop to produce behavioural
improvements. In another animal study (Nudo, Wise, SiFuentes, & Milliken, 1996), researchers found that following lesions to the hand area in monkeys, hand movement representations in originally undamaged areas adjacent to the lesion were lost. However, intensive behavioural training of skilled hand use prevented this loss of representation in adjacent tissue, and actually led to larger areas of hand representation. Taken together, these findings suggest that structured input and structured activity (i.e., rehabilitative training) can guide synaptic reorganization, resulting in behavioural improvements.
Research suggests that wherever possible, stimulation should be targeted to foster adaptive connections within a lesioned network and to minimize the possibility of fostering faulty or maladaptive connections (Robertson & Murre, 1999b). In a physiotherapy study of motor rehabilitation in humans (Bütefisch, Hummelsheim, Denzler, & Mauritz, 1995), highly repetitive hand and finger movements in the impaired arm produced significantly greater improvement in function than standard hand and finger exercises which involved a range of movements. This concept is critical to understanding the mechanisms underlying behavioural change in targeted computerized training programs.
Neuroplasticity in MS. Despite the widespread and multifaceted nature of the disease, there is evidence to show that functional reorganization accompanying recovery across brain systems in MS can limit the impact of damage on behaviour (Filippi et al., 2012; Reddy et al., 2002; Tomassini et al., 2012a). Functional studies which have investigated working memory
38 (Chiaravalloti et al., 2005) and other executive functions (Mainero et al., 2004) in MS have consistently found that these processes involve activity of wider and more bilateral networks of task-specific regions in MS patients than in healthy controls. Stronger interhemispheric
functional and structural interactions have been observed in MS patients than in controls, with such increased strength of connectivity found to be associated with damage to specific, task- relevant white matter tracts (Rocca et al., 2009). In a recent review of neuroplasticity and functional recovery in MS (Tomassini et al., 2012b), the authors describe a number of individual-specific and disease-related factors which could influence adaptive functional reorganization in MS. Age at disease onset may influence the premorbid cognitive functional reserve, which may in turn, help explain the effect of age on cortical reorganization processes that underlie recovery in MS. The type, location, extent, and severity of MS damage affects adaptive reorganization. For example, acute inflammation (e.g., such as in RRMS) alters functional brain responses, which then return to baseline activity with resolution of
inflammation, whereas chronic inflammation (e.g., primary progressive MS) may produce sustained reorganization of function across brain systems, that may either be adaptive or
maladaptive. Furthermore, more extensive and irreversible tissue loss is usually associated with reduced potential for functional reorganization. The authors argue that a substantial preservation of brain structural architecture via efforts aimed at promoting functional reorganization in MS can enable underlying neural mechanisms to act, even when MS damage or task demands are increased. Finally, the authors describe the importance of using optimal methods that can detect the effects of interventions on promoting functional reorganization. fMRI has been widely used in studies on recovery in MS, to detect changes in baseline neural activity and vascular response as a result of interventions (Hyder, Rothman & Shulman, 2002). The authors of the review
39 article highlight the importance of controlling for individual and disease-related factors that may modulate neural responses in order to optimize interpretability of the results.
The next section will describe a popular computerized WM training program (Cogmed) that has been used with a number of pediatric and adult-based populations, but has yet to be implemented in patients with MS.