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WIREs Cogn Sci
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Adaptation to sensory loss

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The human brain has the remarkable ability to adapt to changes in its environment by benefiting from its ‘plastic’ properties. Following brain injury, the amputation of a limb, or the loss of a sensory input such as peripheral blindness, brain circuitry often seems to be able to reorganize itself in order to compensate for the handicap by being recruited to carry out tasks not associated with their prior ‘default’ functioning. The purpose of this review is to illustrate the brain's remarkable ability to adapt to changes in its environment, particularly when it is faced with a sensory loss. Two excellent models to study this phenomenon are provided by blind and deaf individuals. In both cases, studies have shown that they appear to compensate for the loss of sensory input with enhanced abilities in their remaining senses. These behavioral modifications are often coupled with changes in cerebral processing, generally in the form of crossmodal recruitment of deaffarented primary and secondary sensory areas. We will also discuss the possible mechanisms underlying these changes and whether the functional topography of these regions present in unimpaired individuals is preserved in blindness and deafness. The notion of a critical period for plastic changes will also be discussed and its importance will be shown to be twofold. On the one hand, the functional relevance of crossmodal processing appears to decrease as a function of the age of onset of the deficiency. On the other hand, the more cortical reorganization takes place, the less likely brain areas will be able to process input from its original sensory modality. This is especially important for deaf individuals as auditory input can now be restored thanks to cochlear implants. Copyright © 2010 John Wiley & Sons, Ltd.

Figure 1.

Sound localization performances: (A) Sighted control subjects in the binaural condition of listening; (B) one representative totally blind subject in the binaural condition of listening; (C) sighted control subjects in the monaural condition of listening; (D) one totally blind subject who correctly localized the sound with no directional bias. The dashed lines indicate the actual sound sources locations, whereas the black dots refer to the perceived target locations with their respective standard deviations. (Adapted with permission from Ref 33. Copyright 1985 Nature Publishing Group).

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Figure 2.

Angle discrimination in the blind. (A) Position of the arm of the subject during haptic angle discrimination, relative to the angles (90° angle shown here). Angles were explored with the arm outstretched using the distal phalanx of the index finger for exploration. A single continuous to and fro movement was used to explore the angles, following the sequence abcba (digit shown in the start position a here). (B) Comparison of the performance of blind (n = 14) and sighted subjects (n = 15) in the 2‐D angle discrimination task. Logistic functions fitted to the pooled data are shown here, with proportion of correct responses versus Δ angle. (C) Mean discrimination threshold ( ± SEM) in sighted (black) and blind subjects (striped). (Adapted with permission from Ref 67. Copyright 2008 Springer).

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Figure 3.

Correlation of brain activity with performance. (A) The scattergram shows the individual values extracted for performance in the monaural localization task and CBF values in dorsal extrastriate cortex (closed circles indicate blind subjects; open circles indicate sighted ones). (B) An illustration of the statistical parametric map of the correlation with one of its maximal points (two other occipital foci were found but are not shown here). X coordinate is in standardized stereotaxic space. (Adapted with permission from Ref 35. Copyright 2009 PLoS Biology).

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Figure 4.

Correlational analyses for the monaural task. In the left panel (A) are shown the scattergram plotting CBF change against age of onset of blindness (top) and the statistical parametric map of the correlation with a maximal point in the right extrastriate cortex (bottom). The negative relationship between the two variables indicates that the earlier a blind person loses his/her sight, the more the occipital is recruited by the task. Similarly, in the right panel (B) are shown the scattergram plotting CBF change against performance (top) and the statistical parametric map of the correlation with a maximal point in the right lateral occipito‐temporal cortex (bottom). The negative relationship between the variables implies the less this region is recruited, the better the performance of the blind person. (Adapted with permission from Ref 122. Copyright 2006 MIT Press).

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Figure 5.

Scalp distributions and waveforms at the maximum amplitude of the Oz P2 component in response to visual motion stimuli in controls (A), good performers (B), and poor performers (C). (Adapted with permission from Ref 188. Copyright 2006 Oxford University Press).

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Figure 6.

Audiovisual interaction in CI users. In the top panel is the illustration of the experimental procedure. Each condition began (A) and ended (C) in a static neutral position. In all audiovisual conditions (B), auditory stimuli (D) were simultaneously presented with a visual stimulus change (color, movement, or video sequence). In the bottom panel are plotted the decreases in performance (%) for each audiovisual condition for both proficient (E) and nonproficient (F) CI users. (Adapted with permission from Ref 194. Copyright 2007 Elsevier).

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