Department of Psychiatry, McLean Hospital, Belmont, Massachusetts
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
Correspondence: Dr Ralph Hoffman, YaleNew Haven Psychiatric Hospital, 20 York Street, New Haven, CT 06504, USA
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ABSTRACT |
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Aims To demonstrate that reductions in neuritic processes can produce excessive priming in patients with schizophrenia.
Method Associative memory was simulated using a computer-based neural network system consisting of two interactive neural groups, one coding for individual memories and the other for the category to which each memory belonged.
Results Variation of a single parameter determining the density of local connections within the two neuronal groups gave a close approximation to levels of memory access and semantic priming previously reported in normal subjects and in patients with schizophrenia.
Conclusions This study suggests that schizophrenia arises from excessive pruning of local connections in association cortex. Its findings shed light on the mechanisms underlying cognitive priming more generally, and how it might emerge developmentally.
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INTRODUCTION |
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Semantic priming and schizophrenia
It has often been observed that people with schizophrenia seem to get
stuck in semantic categories. Bleuler gives as an example the
patient who gave as her family members father, son... and Holy
Ghost. The experimental analogue of this is semantic priming. An early
study by Meyer & Schvaneveldt
(1971) demonstrated that when
normal subjects were shown a target word (e.g. nurse) they could
more quickly identify it (as a word v. a non-word) when it was
preceded by a semantically related priming word, such as doctor,
as opposed to an unrelated word, such as bread. This effect was
termed semantic priming. Maher
(1983) proposed that
associative intrusions expressed in the speech of patients with schizophrenia
arise, at least in part, from enhanced semantic priming. Subsequent studies
have tended to support this view. Studies by Manschreck et al
(1988), Spitzer et al
(1994), Henik et al
(1995) and Kwapil et
al (1990), among others,
all showed greater semantic priming in patients with schizophrenia relative to
a normal control group.
It should be noted that some more recent studies (e.g. Barch et al, 1996) have not demonstrated enhanced semantic priming in patients with schizophrenia. One explanation for these discrepant findings is suggested by Maher et al (1996), who showed that semantic priming was positive for patients with short length of illness, but declined to negative values as the length of illness increased. The gradient of decline was significant and was shown to be neither an artefact of age nor related to medication status. They concluded that the probability of positive semantic priming among people with schizophrenia depends significantly on chronicity of illness. Consistent with this view is a body of research indicating that later stages of schizophrenic illness are associated with cognitive slowing (e.g. Mitrushina et al, 1996), which will increase reaction time and contaminate semantic priming estimates. Significantly, studies that have called into question enhanced priming in schizophrenia have not taken into account length of illness. A second explanation is suggested by studies demonstrating that the subgroup of thought-disordered patients with schizophrenia show heightened semantic priming (Maher et al, 1987; Manschreck et al, 1988; Spitzer et al, 1994). Similarly, Moritz et al (1999) demonstrated that people who were not diagnosed with any psychiatric disorder but revealed schizophrenia-like language disturbances showed increased priming. Therefore, studies focusing on patients with more recent onset and with thought disorder seem especially likely to demonstrate enhanced semantic priming.
A third issue is the operationalisation of priming measures. Priming is generally measured in one of two ways: a word pronunciation test or a lexical decision task. Spitzer has argued that the lexical decision method is most appropriate, as the naming required for the word pronunciation task can be performed by participants without semantic processing (M. Spitzer, 1999, personal communication). This matter is significant because some of the negative studies (including that of Barch et al, 1996) employed the word pronunciation task. The above considerations, taken together, suggest that excessive semantic priming remains an important clinical phenomenon in understanding neurocognitive alterations in a significant subgroup of patients with schizophrenia.
Theoretical explanations: spreading activation
Most models of semantic priming have largely relied on notions of
spreading activation. This approach assumes that individual
neurons or small groups of neurons code for particular concepts a
theory of local representation. Researchers who have used this framework to
explain semantic priming envisage semantic information as organised in
webs of meaning, in which nodes coding for similar concepts are
closer together and more strongly connected than those coding for dissimilar
concepts. When a concept (node) is activated, this activation spreads to its
neighbouring nodes and decays over time, so that more distant, and
semantically unrelated, concepts are not activated (see
Collins & Loftus,
1975).
There is scant neurobiological evidence, however, that the brain relies on localised representation to store and retrieve memories. This view forces the conclusion that each concept is required to have its own unique set of neurons there would need to be specific neurons corresponding, for instance, to grandmothers. Rather, there is instead a large and growing body of theoretical and empirical studies indicating that the brain stores and processes information as distributed patterns of activation (Bressler, 1995). If so, neurobiological mechanisms of semantic priming require another explanation.
Neurodevelopmental models of schizophrenia
It is well known that adolescence is accompanied by dramatic reductions in
corticocortical connectivity in frontal regions and probably other regions of
human association cortex (Huttenlocher,
1979). Some workers have proposed that schizophrenia arises from
excessive loss of corticocortical connectivity (e.g.
Feinberg, 1982;
Hoffman & McGlashan,
1997). Consistent with that view are studies demonstrating
reductions in cortical neuropil (Selemon
et al, 1995), synapse-associated phosphoproteins
(Eastwood & Harrison, 1995)
and dendritic spine density (Glantz &
Lewis, 2000) in patients with schizophrenia compared with normal
controls. Given that schizophrenia generally emerges during late adolescence
or early adulthood, these data suggest that this disorder may represent a
failure to arrest the normal, physiological process of pruning of connections
during adolescence.
The study reported below uses a distributed model of neural network processes to investigate effects of reduced corticocortical connectivity on semantic priming. Our model used two network modules: one specifying semantic categories, and a second module characterising items within categories. Neurons exchanged connections within and between each of the two modules. We predicted that reductions in connectivity within the modules would enhance the salience of semantic category information transmitted between the modules. If so, excessive reductions in within-module connectivity would lead to excessive semantic priming. The relevance of this approach is suggested by a study by Woo et al (1997) demonstrating in monkeys that frontal pruning occurring around pubescence selectively reduces local rather than distant connections. Viewing schizophrenia as a pathological extension of late (i.e. adolescent) cortical pruning therefore suggests that local connections are preferentially lost in this disorder, thereby leading to excessive semantic priming.
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METHOD |
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At any given time, each neuron in the system is either active or inactive, these states being represented by neuronal activations of 1 and 0, respectively. Thus, at any instant, the state of the network can be characterised by a 120-dimensional binary vector. During one cycle, or iteration, the state of the network is updated using the following two-step process.
First, all inputs to a given neuron, both intrinsic and extrinsic, are
summed and a threshold function is applied. Input to neuron
µj is calculated by multiplying the axonal output of
each neuron supplying input into it (termed si below) by
the ij connection weight, termed Tij. That
is,
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Next, in order to capture the fluid, dynamic nature of neurocognitive
processes, an adaptation factor was included to allow our simulation to shift
readily from one attractor to another. We chose to model adaptation by
degrading neuronal output in an activity-dependent manner. That is, each time
a neuron is activated, the strength of its axonal signal is diminished; if and
when the activation of the neuron falls to 0, the adaptation level is reset to
0. Mathematically, we can represent this by the following set of difference
equations:
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Memory storage and retrieval
Each of the network's memories was a particular pattern of
activation of its 120 constituent neurons and, as such, each could be
represented by a 120-element binary vector. Memories were created in which, on
average, only 20% of the constituent neurons were active; thus, a typical
memory was composed of approximately 24 (randomly selected) active neurons.
The network was trained by presenting a given memory that is, turning
on the neurons of the network corresponding to the activated neurons of the
memory. The formula below was then applied, which increased the connection
weight between any two neurons that are activated simultaneously
(Amit et al, 1987);
this serves to embed memories and the categories to which they belong in the
network connection strengths:
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Simulation of cognitive priming
Testing cognitive priming in a neural network system requires patterns of
neuronal activation (memories) of different categories. We operationalised
this as follows. The first 40 of the memory's 120 neurons were designated as
categorical. For a given category, the activation states of
these 40 neurons were randomly generated and identical for all memories of
that category. The remaining 80 neurons designated as
case neurons were generated randomly for all memories in
all categories. Twenty-five memories, five in each of five categories
designated A to E, were generated.
To test cognitive priming, the system was presented with a particular memory, say memory 1 from category A. The network then cycled through five iterations. For each cycle, adaptation was applied to neuronal outputs, and the network's new activation state was calculated as described above. After this exposure phase, a different memory from the same category (say, memory 2 of category A) was presented to this primed network. However, this memory was presented in a highly degraded form: only 33% of the nodes constituting the memory, which were selected randomly by the program, were presented. The network then returned a pattern of activation which might or might not resemble that of memory 2.
At this point the network's performance that is, how readily it could identify the degraded stimulus was tested. To do this, the 120 units of the network's output were compared with the 120 units of a given memory vector. The sum of the instances in which corresponding elements were both 1 was calculated and divided by the number of active neurons in the memory in question. This was repeated for each of the 25 memories. We termed this quotient the similarity quotient and used it to rank the memories, the highest being most similar to the network's output. If this was the same as the cued memory (in our example, memory 2) and its similarity quotient, as defined above, was 0.4 or greater, it was counted as a correct hit. If these two criteria were not met, it was scored as an incorrect response. All 25 memories were used as both priming and test patterns, and the above procedure was carried out for all 125 intracategory primetest combinations.
We evaluated the priming effect by first calculating the number of correct hits achieved under priming (0-125) and dividing by 125; this figure is shown in column 4 of Table 1. To evaluate the base-line (unprimed) performance of the system, each of the 25 patterns was degraded to 33% of its original activation, as it had been in the test scenarios, and presented to the system; the percentage of times the network could identify the test pattern was calculated (column 3 of Table 1).
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Comparison with clinical data
Performance of our computer model was compared with an empirical study of
priming in schizophrenia reported by Kwapil et al
(1990). These researchers
created a pool of 96 semantically related word pairs (i.e. 96
primetarget combinations). First, the priming word was displayed to the
person undergoing the test, then a blank screen was shown briefly, then the
degraded target word was displayed. To establish a baseline, or non-primed,
condition, the word blank was shown as the prime. For the
unrelated condition, prime and target words were of different
semantic categories. The percentage of correctly identified words was
recorded; the overall results are summarised in
Table 2. Their
percentage correct in the neutral condition is analogous to our
measure of percentage correct without priming. Similarly, their
related test condition is analogous to our percentage correct
with priming. Participants with schizophrenia showed a clear positive priming
effect when compared with a control group, as well as in a comparison with
patients with bipolar affective disorder.
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Neural pruning
Our simulation differentiated between category regions and case nodes, the
former comprising a subset of 40 neurons and the latter 80
neurons. This assignation allowed us to distinguish three sorts
of connections: those restricted to category neurons (intra-category
connections), those restricted to case neurons (intracase connections), and
those that connected case and category neurons. To make this explicit we have
represented the two sets of neurons as anatomically segregated
in Fig. 1, but clearly this
does not imply that they are segregated in real nervous systems.
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Connectivity was reduced using a pruning parameter for each category of connection: if the absolute value of an axon's weight factor was below this threshold, it was eliminated (i.e. set to 0). We used one pruning parameter for both intracategory and intracase connections (hereafter referred to as intramodular connections) and a second pruning parameter for between-module connections.
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RESULTS |
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By varying one parameter (for intramodular connections), we were able to simulate both findings in Kwapil's schizophrenia study group, i.e. increased priming and decreased overall memory recall. By increasing this parameter from 0.0106 to 0.0148, the level of intracategory pruning increased from 36.5% to 61.8% and that of intracase pruning increased from 83.2% to 93.5% (see Table 1). This manipulation enhanced priming and reduced performance in a way that closely approximated the empirical findings for patients with schizophrenia reported by Kwapil et al (1990).
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DISCUSSION |
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Implications regarding mechanism of priming
Semantic priming is a well-characterised process that is central to
understanding the human cognitive functions of learning and memory. It allows
for more efficient recognition of categorically similar items and allows one
to stay in a particular semantic set (as opposed to switching
sets). Our study shows how this cognitive capacity can emerge in a distributed
network and sheds light, more generally, on how meanings may be encoded
neurally. Our findings call into question theories of cognition based on local
representation i.e. that a given concept is represented in an
individual neuron or node.
Our simulation builds on the work of Hermann et al (1993), who also observed semantic priming using a distributed network model. They created a neural network with adaptation incorporated at the activation function level, and employed memories of distinct semantic classes, each of which was defined by a fuzzy core of similarity at the neuronal level. Their network also exhibited priming behaviour in its normally functioning state. While their model is informative, it does not consider the possible effects of selective elimination of intrinsic corticocortical neuronal processes, a normal component of postnatal development, in producing semantic priming in health and possibly in schizophrenia.
Our model simulated not only enhanced priming but also cognitive impairment. Specifically, the model's non-primed recall performance declined as schizophrenogenic pruning increased. This can be understood as the analogue of the cognitive impairments seen in actual patients with schizophrenia. Such impairments are demonstrated in the study by Kwapil et al, where participants in the schizophrenia group performed worse (by about 10%) than the control group on baseline degraded stimuli recognition tests (the schizophrenia group had a correct response rate of 43.0% and the normal group 48.1%, as shown in Table 2), as well as a large number of experimental studies.
Correlation with neuroanatomic studies
Our results estimated reductions in connectivity associated with normal as
well as schizophrenic development. By using a methodology described by Hoffman
& McGlashan (1997), the
model can also be used to estimate corresponding reductions in synapses. These
estimates can be generated if one assumes that the number of synapses
mediating a projection from one neuron to another is linearly correlated with
the strength or weight of that projection. Using this technique, we calculated
the reduction in synapses in moving from the control to the schizophrenia case
to be 32.3%. This is roughly similar to results obtained by Glantz & Lewis
(2000) their study
indicated that dendritic spine density of layer III pyramidal neurons in the
dorsolateral prefrontal cortex (DLPFC area 46) was decreased by 23% in
schizophrenia compared with normal brains. Based on the confidence interval
that they provide, our model's predictions fall well within one standard
deviation of their results. Our study also has parallels with the work of
Benes et al (1991),
who showed that in the brains of patients with schizophrenia interneurons were
reduced in most layers of cingulate cortex.
Neurodevelopment and the disconnection syndromes
The feasibility of pruning as a central neuro-developmental event leading
to full adult cognitive functioning is supported by Woo et al
(1997). Their study comparing
prepubertal and mature monkeys showed that in the course of development
considerable synaptic pruning occurred in the prefrontal cortex and that this
process involved primarily short, intrinsic, within-region connections.
Specifically, they found much greater synaptic elimination among the intrinsic
axon projections of the supragranular pyramidal neurons than among the
relatively longer association fibres connecting different cortical regions.
This neurodevelopmental pattern was also seen by Lewis & Gonzalez-Burgos
(2000), who argued that the
local, intrinsic connections in the prefrontal cortex are an important
anatomic substrate for the development of working memory functions and that
the selective elimination of these connections at adolescence could underlie
schizophrenic symptomatology. If schizophrenia reflects an extension of normal
adolescent pruning we would expect short connections to be eliminated
selectively precisely the connections that our model predicts are
deficient.
Our findings can also be understood in the context of the disconnection syndromes described by Friston (1996) among others. This theoretical approach views schizophrenia as a failure of functional integration, which can be looked at in neuropathological terms as a deficit in anatomic, functional or effective connectivity. Moreover, these researchers describe neuroimaging methodologies that permit assessment of functional connectivity. The model described here assumes an anatomic disconnection syndrome specifically involving local level intrinsic connectivity. In particular, our findings suggest that the elucidation of both normal neuro-development and schizophrenia may benefit from future studies combining measures of semantic priming and functional corticocortical connectivity based on neuroimaging methods.
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Clinical Implications and Limitations |
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LIMITATIONS
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REFERENCES |
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Received for publication August 21, 2000. Revision received August 28, 2001. Accepted for publication October 5, 2001.
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