Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

Abstract

We view schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left (dominant) temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a disturbance in connectivity (associative strength) but not network size (number of associates) in patients with schizophrenia. Paralleling these findings are recent neural network simulation studies of the abnormal connectivity effect in schizophrenia through 'lesioning' network connection weights while holding constant network size. Supporting evidence at the level of biology are in vitro studies examining N-methyl-D-aspartate (NMDA) receptor antagonists on recurrent inhibition; simulations in neural populations with realistically modeled biophysical properties show NMDA antagonists produce a schizophrenia-like disturbance in pattern association. We propose a similar failure of NMDA-mediated recurrent inhibition as a candidate biological substrate for attention and semantic anomalies of schizophrenia.

Fig. 1

Free associate responses to the word, THREAD produced in descending order of frequency, NEEDLE, SEW, STRING, CLOTH, CLOTHES, and ROPE.

Fig. 2

Schematic of basic network architecture of WANN (see text for further discussion).

Fig. 3

WANN propagation in response to studying target word.

Fig. 4

WANN propagation in response to cued recall of target word.

Fig. 5

Cellular model of failure of recurrent inhibition, + is excitatory (glutamatergic) and − is inhibitory (GABA-ergic). The two horizontal lines on the projection neuron synapse represent NMDA receptor blockade. Note that inhibitory interneuron also has cholinergic, serotonergic, and dopaminergic modulatory synapses. These are also possible sites of abnormal receptors and/or abnormal inputs (see text for further discussion).