Cortical Neurodynamics, Schizophrenia, Depression, and Obsessive-Compulsive Disorder

A computational neuroscience approach to the symptoms, mechanisms of, and treatments for schizophrenia and depression is described. The approach is based on a stochastic neurodynamical framework in which the stability of attractor networks in the brain is analyzed. The stability is influenced by statistical fluctuations in populations of neurons caused by the neuronal spiking time randomness for a given mean firing rate. The stability of the high firing rate attractor state which implements effects such as short-term memory and attention is increased if the firing rates are sufficiently high to dominate the spiking-related noise. The stability of the low, spontaneous, firing rate state in the absence of input must also be maintained, and GABA-mediated inhibition is important for this. The approach aims towards producing a neurally based mechanistic model that can account for the phenomenology of disorders as experienced by patients, such as schizophrenia, depression, and obsessive-compulsive disorder. In schizophrenia, the approach suggests that a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, present in schizophrenia, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms of schizophrenia. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, which is likely to contribute to reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this is suggested to reduce the effects on external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition caused by a reduction of GABA neurotransmission, present in schizophrenia in for example the temporal lobes, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. For depression, the theory is that there is an attractor system in the lateral orbitofrontal cortex that is sensitive to not obtaining expected rewards, which can lead to sadness and depression, and that this system is over-responsive and over-connected in depression. The lateral orbitofrontal cortex has increased functional connectivity with the precuneus and posterior cingulate cortex in depression, which are involved in the sense of self and autobiographical memories, and this may account for the low self-esteem in depression. The lateral orbitofrontal cortex also has increased functional connectivity in depression with the angular gyrus, a brain area related to language, and this may be related to rumination in depression. The reward-related medial orbitofrontal cortex has reduced connectivity with temporal lobe memory systems in depression and reduced sensitivity to rewards, and these may contribute to fewer happy memories, and anhedonia, in depression.