Cognitive training for impaired neural systems in neuropsychiatric illness

Abstract

Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks-combined with the availability of advanced computerized technology-suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain 'learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate some of the exciting future directions of this field.

Figures

Figure 1

The brain is organized in a hierarchy of neural assemblies that consist of multiple parallel loops. Intermediate- and long-range connections link the various loops in the cerebral cortex, and link cortical assemblies to subcortical structures. Sensory information passes through the thalamus to sensory cortex, and is in turn modulated by prefrontal influences. Modification of connections is determined by input from the senses, the environment, and interactions with other brains. Adapted from Buzsaki (2006).

Figure 2

During successful learning, the brain enhances the neural representations of behaviorally relevant stimuli and actions. Monkeys trained to apply the tips of their second and third fingers to a rotating disc show substantially enlarged cortical representations of those digits' tips after training (Jenkins et al, 1990).

Figure 3

Prefrontal cortical areas interact bidirectionally with lower levels of sensory and perceptual operations, with multiple feed-back and feed-forward effects. A schematic representation is presented for the visual system. Adapted and modified from Ahissar et al (2009).

Figure 4

In order for prefrontal cortical operations to engage in efficient decision-making and adaptive behavior, the brain must be able to continuously make accurate predictions about the near future. These predictions rely on rapidly and accurately comparing high-fidelity perceptions of our current internal and external environments with past experiences.

Figure 5

If lower-level perceptual and/or attentional processes are degraded or abnormally biased, the brain will have difficulty adaptively performing other more complex multimodal operations, predictions, or decisions on the data.

Figure 6

(a) The first phase of skill learning involves rapid improvements in performance. (b) The second phase of learning is characterized by massive reorganization of task-specific representations in the brain.

Figure 7

Targeted auditory training improves functional and structural impairments in the aged brain. (Above) In the aged rat brain, the normally smooth gradient of frequency tuning in the primary auditory cortex (A1) is disorganized and neurons lose their frequency tuning selectivity. Forty sessions of intensive training on a deviant tone detection paradigm completely reverses these impairments. The polygons shown above represent the location of neurons recorded in a typical rat A1 during cortical mapping experiments. On the top row, the color represents the neurons' frequency tuning (blue for low frequency, red for high), while in the bottom row, the color represents the sharpness of tuning. (Below) Low power photomicrographs demonstrate the loss of parvalbumin (PV) immunoreactivity in the aged A1. PV is contained in a specific class of interneurons involved in salient stimulus detection and noise suppression in the cortex. The same auditory training task described above significantly increased the number of cells staining for PV in the cortex (a, d, g, j), decreased the number of cell staining only lightly for PV (b, e, h, k) and increased dendritic PV immunoreactivity (c, f, i). Error bars are SEM. Scale bar in g (apply for a and d): 200 μm; in h (apply for b and e): 100 μm; in i (apply for c and f): 50 μm. **P<0.01. Adapted from de Villers-Sidani et al (2010).

Figure 8

Pairing auditory tones with VTA stimulation affects plastic changes in A1. In A1, frequency tuning follows a tonotopic gradient where neurons tuned to low frequencies are found at one extremity of the map and neurons tuned to high frequencies are located at the other (a). Here, a sequence of two tones was presented along with stimulation of the VTA, which releases dopamine in the cortex. The first 4 kHz tone preceded the VTA stimulation by 500 ms; the second 9 kHz tone followed the VTA stimulation 500 ms later. This simple paradigm resulted in an ∼300% increase in the A1 area tuned to 4 kHz and a 50% decrease in A1 tuning to 9 kHz (b, c). These findings suggest that the timing of a reward provided in the context of perceptual training has to be carefully considered in order to maximize cortical plasticity responses *P<0.05, **P<0.0005. Adapted from Bao et al (2001).

Figure 9

Moment-to-moment top–down biasing of frequency tuning in A1 occurs after training on a sound sequence. The color of each polygon indicates the frequency tuning of neurons recorded in the A1 of control rats and rats trained to respond to the occurrence of a 7-kHz tone presented only after the occurrence of a 3-kHz tone. (Top row) Training resulted in a slight increase in the representation of each tone presented in isolation. (Middle row) When measured right after the presentation of a 3-kHz tone, the area of A1 tuned to 7 kHz is more than doubled compared with when a different tone is used first in the sequence (bottom row). Dark or light-gray polygons indicate recording sites tuned to 3 or 7 kHz±0.25 octaves, respectively. A, anterior; D, dorsal. Adapted from Zhou et al (2010).

Figure 10

Cortical reorganization can occur in maladaptive directions: monkeys trained to detect a bar contacting the second, third, and fourth finger simultaneously show a degraded and undifferentiated representation of those digits in somatosensory cortex (Wang et al, 1995); this degraded map leads to focal hand dystonia. Similar principles of maladaptive reorganization of somatosensory cortex underlie the development of phantom limb pain (Yang et al, 1994).

Figure 11

Learning across a distributed neural system can be conceptualized as occurring across hierarchical ‘levels.' For illustrative purposes only, three levels are shown schematically, ranging from lower-level perceptual representations, to explicit higher-order perceptions and operations, to global high-level ecologically meaningful cognitions and contexts. Mid-level and higher-level representations are influenced by the quality of lower-level perceptual processing, just as pre-attentive perceptual processing is biased or influenced by higher-level predictions and expectancies. (a) During an initial learning phase, higher-level neural representations are strengthened, as the prefrontal cortex orients toward the task at hand, and very few plastic changes occur at lower levels. With sufficient intensive practice, in a healthy brain, plastic changes can propagate backwards from these higher-level representations, resulting in strengthened accuracy and fidelity of processing at lower-levels of the system (eg, the wine expert who is able to taste and identify a very wide range of flavors). In an impaired brain, distortions or limitations at any level will create bottlenecks for learning-induced widespread adaptive changes. (b) If training of sufficient intensity and duration progresses on specific sets of informative lower-level and middle-level stimuli and tasks, plastic changes will feed forward to improve the representational fidelity of information at higher levels, and learning will be partially transferred to higher-level contexts that use the trained lower-level features. Adapted and modified from Ahissar et al (2009).