Why context matters? Divisive normalization and canonical microcircuits in psychiatric disorders

Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy

Adaptive coding of reward is the process by which neurons adapt their response to the context of available compensations. Higher rewards lead to a stronger brain response, but the increase of the response depends on the range of available rewards. A steeper increase is observed in a narrow range and a more gradual slope in a wider range. In schizophrenia, adaptive coding appears to be affected in different domains, especially in the reward domain. Here, we tested adaptive coding of reward in a large group of patients with schizophrenia (n = 86) and control subjects (n = 66). We assessed: (i) the association between adaptive coding deficits and symptoms; (ii) the longitudinal stability of deficits (the same task was performed 3 months apart); and (iii) the stability of results between two experimental sites. We used functional MRI and the monetary incentive delay task to assess adaptation of participants to two different reward ranges: a narrow range and a wide range. We used a region-of-interest analysis to evaluate adaptation within striatal and visual regions. Patients and control subjects underwent a full demographic and clinical assessment. We found reduced adaptive coding in patients, with a decreased slope in the narrow reward range with respect to that of control participants, in striatal but not visual regions. This pattern was observed at both research sites. Upon retesting, patients increased their narrow-range slopes, showing improved adaptive coding, whereas control subjects slightly reduced them. At retesting, patients with overly steep slopes in the narrow range also showed higher levels of negative symptoms. Our data confirm deficits in reward adaptation in schizophrenia and reveal an effect of practice in patients, leading to improvement, with steeper slopes upon retesting. However, in some patients, an excessively steep slope may result in poor discriminability of larger rewards, owing to early saturation of the brain response. Together, the loss of precision of reward representation in new (first exposure, underadaptation) and more familiar (retest, overadaptation) situations might contribute to the multiple motivational symptoms in schizophrenia.

Computational model links normalization to chemoarchitecture in the human visual system

A goal of cognitive neuroscience is to provide computational accounts of brain function. Canonical computations-mathematical operations used by the brain in many contexts-fulfill broad information-processing needs by varying their algorithmic parameters. A key question concerns the identification of biological substrates for these computations and their algorithms. Chemoarchitecture-the spatial distribution of neurotransmitter receptor densities-shapes brain function. Here, we propose that local variations in specific receptor densities implement algorithmic modulations of canonical computations. To test this hypothesis, we combine mathematical modeling of brain responses with chemoarchitecture data. We compare parameters of divisive normalization obtained from 7-tesla functional magnetic resonance imaging with receptor density maps obtained from positron emission tomography. We find evidence that serotonin and γ-aminobutyric acid receptor densities are the biological substrate for algorithmic modulations of divisive normalization in the human visual system. Our model links computational and biological levels of vision, explaining how canonical computations allow the brain to fulfill broad information-processing needs.

The presence of irrelevant alternatives paradoxically increases confidence in perceptual decisions

Confidence in perceptual decisions is thought to reflect the probability of being correct. According to this view, confidence should be unaffected or minimally reduced by the presence of irrelevant alternatives. To test this prediction, we designed five experiments. In Experiment 1, participants had to identify the largest geometrical shape among two or three alternatives. In the three-alternative condition, one of the shapes was much smaller than the other two, being a clearly incorrect option. Counter-intuitively, confidence was higher when the irrelevant alternative was present, evidencing that confidence construction is more complex than previously thought. Four computational models were tested, only one of them accounting for the results. This model predicts that confidence increases monotonically with the number of irrelevant alternatives, a prediction we tested in Experiment 2. In Experiment 3, we evaluated whether this effect replicated in a categorical task, but we did not find supporting evidence. Experiments 4 and 5 allowed us to discard stimuli presentation time as a factor driving the effect. Our findings suggest that confidence models cannot ignore the effect of multiple, possibly irrelevant alternatives to build a thorough understanding of confidence.

Adaptive coding occurs in object categorization and may not be associated with schizotypal personality traits

Processing more likely inputs with higher sensitivity (adaptive coding) enables the brain to represent the large range of inputs coming in from the world. Healthy individuals high in schizotypy show reduced adaptive coding in the reward domain but it is an open question whether these deficits extend to non-motivational domains, such as object categorization. Here, we develop a novel variant of a classic task to test range adaptation for face/house categorization in healthy participants on the psychosis spectrum. In each trial of this task, participants decide whether a presented image is a face or a house. Images vary on a face-house continuum and appear in both wide and narrow range blocks. The wide range block includes most of the face-house continuum (2.50-97.5% face), while the narrow range blocks limit inputs to a smaller section of the continuum (27.5-72.5% face). Adaptive coding corresponds to better performance for the overlapping smaller section of the continuum in the narrow range than in the wide range block. We find that participants show efficient use of the range in this task, with more accurate responses in the overlapping section for the narrow range blocks relative to the wide range blocks. However, we find little evidence that range adaptation in our object categorization task is reduced in healthy individuals scoring high on schizotypy. Thus, reduced range adaptation may not be a domain-general feature of schizotypy.

Comparing adaptive coding of reward in bipolar I disorder and schizophrenia

Deficits in neural processing of reward have been described in both bipolar disorder (BD) and schizophrenia (SZ), but it remains unclear to what extent these deficits are caused by similar mechanisms. Efficient reward processing relies on adaptive coding which allows representing large input spans by limited neuronal encoding ranges. Deficits in adaptive coding of reward have previously been observed across the SZ spectrum and correlated with total symptom severity. In the present work, we sought to establish whether adaptive coding is similarly affected in patients with BD. Twenty-five patients with BD, 27 patients with SZ and 25 healthy controls performed a variant of the Monetary Incentive Delay task during functional magnetic resonance imaging in two reward range conditions. Adaptive coding was impaired in the posterior part of the right caudate in BD and SZ (trend level). In contrast, BD did not show impaired adaptive coding in the anterior caudate and right precentral gyrus/insula, where SZ showed deficits compared to healthy controls. BD patients show adaptive coding deficits that are similar to those observed in SZ in the right posterior caudate. Adaptive coding in BD appeared more preserved as compared to SZ participants especially in the more anterior part of the right caudate and to a lesser extent also in the right precentral gyrus. Thus, dysfunctional adaptive coding could constitute a fundamental deficit in severe mental illnesses that extends beyond the SZ spectrum.

Neurophysiological Perspective on Allostasis and Homeostasis: Dynamic Adaptation in Viable Systems

Allostasis is a physiological principle based on a dynamic regulatory system, contrary to homeostasis, in which the goal is to reach a steady state and recover from deviation from a set point in the internal environment. The concept of allostasis has continued to develop with advances in the field of neuroscience. In this short review, the author presents several new findings in neuroscience and extend the concept of allostasis as mutual regulation between cognitive, somatic, and autonomic systems. In this manner, biological systems adapt to external and internal environments by changing themselves.

Heterogeneous GAD65 Expression in Subtypes of GABAergic Neurons Across Layers of the Cerebral Cortex and Hippocampus

Gamma-aminobutyric acid (GABA), a major inhibitory transmitter in the central nervous system, is synthesized via either of two enzyme isoforms, GAD65 or GAD67. GAD65 is synthesized in the soma but functions at synaptic terminals in an activity-dependent manner, playing a distinct role in excitatory-inhibitory balance. However, the extent to which each GABAergic subtype expresses GAD65 in the resting state remains unclear. In this study, we compared GAD65 expression among six GABAergic subtypes: NPY⁺, nNOS⁺, PV⁺, SOM⁺, CR⁺, and CCK⁺. According to the results, the GABAergic subtypes were classified into two groups per region based on GAD65 expression levels: high-expression (NPY⁺ and nNOS⁺) and low-expression groups (PV⁺, SOM⁺, CR⁺, and CCK⁺) in the cerebral cortex and high-expression (NPY⁺, nNOS⁺, and CCK⁺) and low-expression groups (PV⁺, SOM⁺, and CR⁺) in the hippocampus. Moreover, these expression patterns revealed a distinct laminar distribution in the cerebral cortex and hippocampus. To investigate the extent of GAD65 transport from the soma to synaptic terminals, we examined GAD65 expression in colchicine-treated rats in which GAD65 was synthesized in the soma but not transported to terminals. We found a significant positive correlation in GAD65 expression across subtypes between colchicine-treated and control rats. In summary, each GABAergic subtype exhibits a distinct GAD65 expression pattern across layers of the cerebral cortex and hippocampus. In addition, the level of GAD65 expression in the soma can be used as a proxy for the amount of GAD65 in the cytoplasm. These findings suggest that exploration of the distinct profiles of GAD65 expression among GABAergic subtypes could clarify the roles that GABAergic subtypes play in maintaining the excitatory-inhibitory balance.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

A canonical neural computation is a mathematical operation applied by the brain in a wide variety of contexts and capable of explaining and unifying seemingly unrelated neural and perceptual phenomena. Here, we use a combination of state-of-the-art experiments (ultra-high-field functional MRI) and mathematical methods (population receptive field [pRF] modeling) to uniquely demonstrate the role of divisive normalization (DN) as the canonical neural computation underlying visuospatial responses throughout the human visual hierarchy. The DN pRF model provides a tool to investigate and interpret the computational processes underlying neural responses in human and animal recordings, but also in clinical and cognitive dimensions.

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.