Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.

136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences.

On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA.

These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we tested the negative learning bias hypothesis in a large naturalistic sample of psychiatric patients, including depression, anxiety, addiction, attention-deficit/hyperactivity disorder, and/or autism. First, we assessed whether the negative bias hypothesis of depression generalized to a heterogeneous (and hence more naturalistic) depression sample compared with controls. Second, we assessed whether negative bias extends to other psychiatric disorders. Third, we adopted a dimensional approach, by using symptom severity as a way to assess associations across the sample.

We administered a probabilistic reversal learning task to 217 patients and 81 healthy controls. According to the negative bias hypothesis, participants with depression should exhibit enhanced learning and flexibility based on punishment v. reward. We combined analyses of traditional measures with more sensitive computational modeling.

In contrast to previous findings, this sample of depressed patients with psychiatric comorbidities did not show a negative learning bias.

These results speak against the generalizability of the negative learning bias hypothesis to depressed patients with comorbidities. This study highlights the importance of investigating unselected samples of psychiatric patients, which represent the vast majority of the psychiatric population.

Electroconvulsive therapy (ECT) is a highly effective treatment for severe psychiatric disorders. Despite its high efficacy, the use of ECT would be greater if the risk of cognitive side effects were reduced. Over the last 20 years, developments in ECT technique, including improvements in the dosing methodology and modification of the stimulus waveform, have allowed for improved treatment methods with reduced adverse cognitive effects. There is increasing evidence that the electrode placement is important for orienting the electrical stimulus and therefore modifying treatment outcomes, with potential for further improvement of the placements currently used in ECT.

We used computational modelling to perform an in-depth examination into regional differences in brain excitation by the ECT stimulus for several lesser known and novel electrode placements, in order to investigate the potential for an electrode placement that may optimise clinical outcomes.

High resolution finite element human head models were generated from MRI scans of three subjects. The models were used to compare regional differences in average electric field (EF) magnitude among a total of thirteen bipolar ECT electrode placements, i.e. three conventional placements as well as ten lesser known and novel placements.

In this exploratory study on a systemic comparison of thirteen ECT electrode placements, the EF magnitude at regions of interest (ROIs) was highly dependent upon the position of both electrodes, especially the ROIs close to the cortical surface. Compared to conventional right-unilateral (RUL) ECT using a temporo-parietal placement, fronto-parietal and supraorbito-parietal RUL also robustly stimulated brain regions considered important for efficacy, while sparing regions related to cognitive functions, and may be a preferrable approach to the currently used placement for RUL ECT. The simulations also found that regional average EF magnitude varied between individual subjects, due to factors such as head size, and results also depended on the size of the defined ROI.

Extensive clinical research has shown that the efficacy and cognitive outcomes of electroconvulsive therapy (ECT) are determined, in part, by the type of electrode placement used. Bitemporal ECT (BT, stimulating electrodes placed bilaterally in the frontotemporal region) is the form of ECT with relatively potent clinical and cognitive side effects. However, the reasons for this are poorly understood.

This study used computational modelling to examine regional differences in brain excitation between BT, Bifrontal (BF) and Right Unilateral (RUL) ECT, currently the most clinically-used ECT placements. Specifically, by comparing similarities and differences in current distribution patterns between BT ECT and the other two placements, the study aimed to create an explanatory model of critical brain sites that mediate antidepressant efficacy and sites associated with cognitive, particularly memory, adverse effects.

High resolution finite element human head models were generated from MRI scans of three subjects. The models were used to compare differences in activation between the three ECT placements, using subtraction maps.

In this exploratory study on three realistic head models, Bitemporal ECT resulted in greater direct stimulation of deep midline structures and also left temporal and inferior frontal regions. Interpreted in light of existing knowledge on depressive pathophysiology and cognitive neuroanatomy, it is suggested that the former sites are related to efficacy and the latter to cognitive deficits. We hereby propose an approach using binarised subtraction models that can be used to optimise, and even individualise, ECT therapies.

People with psychoses often report fixed, delusional beliefs that are sustained even in the presence of unequivocal contrary evidence. Such delusional beliefs are the result of integrating new and old evidence inappropriately in forming a cognitive model. We propose and test a cognitive model of belief formation using experimental data from an interactive ‘Rock Paper Scissors’ (RPS) game.

Participants (33 controls and 27 people with schizophrenia) played a competitive, time-pressured interactive two-player game (RPS). Participants' behavior was modeled by a generative computational model using leaky integrator and temporal difference methods. This model describes how new and old evidence is integrated to form a playing strategy to beat the opponent and to provide a mechanism for reporting confidence in one's playing strategy to win against the opponent.

People with schizophrenia fail to appropriately model their opponent's play despite consistent (rather than random) patterns that can be exploited in the simulated opponent's play. This is manifest as a failure to weigh existing evidence appropriately against new evidence. Furthermore, participants with schizophrenia show a ‘jumping to conclusions’ (JTC) bias, reporting successful discovery of a winning strategy with insufficient evidence.

The model presented suggests two tentative mechanisms in delusional belief formation: (i) one for modeling patterns in other's behavior, where people with schizophrenia fail to use old evidence appropriately, and (ii) a metacognitive mechanism for ‘confidence’ in such beliefs, where people with schizophrenia overweight recent reward history in deciding on the value of beliefs about the opponent.