Our computational model of visual attention disruptions in schizophrenia

My latest modeling paper has been published in Computational Psychiatry.

Visual Attention Deficits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex (Open Access!)

Here’s the abstract:

“Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.”

Here’s Figure 1, which shows the circuit we modeled.


“the term reward has no consistent meaning”

A very important point about dopamine (DA) and reward, from a recent review paper:

“The DA hypothesis of reward is a ubiquitous feature of the scientific literature, as well as popular media, the internet, and film. Yet, despite the almost automatic tendency of some to explain virtually any aspect of DA function as somehow being dependent on reward, there are critical theoretical and empirical problems with this, many of which have been reviewed in detail elsewhere (Salamone et al., 1997, 2007; Salamone and Correa, 2002, 2012; Floresco, 2015; Nicola, 2016). First and foremost, the term reward has no consistent scientific meaning (Salamone et al., 2005; Salamone and Correa, 2012), and, depending upon the paper, or even the paragraph, this term is used variously to refer to subjective pleasure or hedonic reactivity, appetite, preference, and even reinforcement learning. Given the slippery and imprecise nature of this term, it is wholly inadequate to attribute specific effects in experiments simply to reward without any qualification or explication.” [Emphasis added]

Salamone, J. D., Correa, M., Ferrigno, S., Yang, J. H., Rotolo, R. A., & Presby, R. E. (2018). The psychopharmacology of effort-related decision making: Dopamine, adenosine, and insights into the neurochemistry of motivation. Pharmacological Reviews70(4), 747-762.

Are mental disorders the same as brain disorders? Maybe not!

I am currently reading an excellent paper that will be published in Behavioral and Brain Sciences soon. It raises some very important issues with popular conceptions of mental illness.

Brain disorders? Not really… Why network structures block reductionism in psychopathology research

These two figures capture some of the key points:

Here is the abstract:

“In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. However, the intense search for the biological basis of mental disorders has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this paper, we show that this conceptualization can help understand why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition, because in symptom networks there is no such common cause. Second, symptom network relations depend on the content of mental states and as such feature intentionality. Third, the strength of network relations is highly likely to partially depend on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett’s (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.”

Behavioral and Brain Sciences is one of the premier journals for “big thinking” in cognitive science and neuroscience, so it’s great to see these ideas there.

What neuroscience too often neglects: Behavior

A Quora conversation led me to recent paper in Neuron that highlights a very important problem with a lot of neuroscience research: there is insufficient attention paid to the careful analysis of behavior. The paper is not quite a call to return to behaviorism, but it is an invitation to consider that the pendulum has swing too far in the opposite direction, towards ‘blind’ searches for neural correlates. The paper is a wonderful big picture critique, so I’d like to just share some excerpts.


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