Transdiagnostic comparison of visual working memory capacity in bipolar disorder and schizophrenia

External cues improve visual working memory encoding in the presence of salient distractors in schizophrenia

BACKGROUND

People with schizophrenia (PSZ) are impaired in attentional prioritization of non-salient but relevant stimuli over salient distractors during visual working memory (VWM) encoding. Conversely, guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ encode more information in the presence of salient distractors.

METHODS

We employed a visuospatial change-detection task using four Gabor patches with differing orientations in 66 PSZ and 74 healthy controls (HCS). Two Gabor patches flickered which were designated either as targets or distractors and either a predictive or a non-predictive cue was displayed to manipulate top-down attention, resulting in four conditions.

RESULTS

We observed significant effects of group, salience and cue as well as significant interactions of salience by cue, group by salience and group by cue. Across all conditions, PSZ stored significantly less information in VWM than HCS. PSZ stored significantly less non-flickering than flickering information with a non-predictive cue. However, PSZ stored significantly more flickering and non-flickering information with a predictive cue.

CONCLUSIONS

Our findings indicate that control of attentional selection is impaired in schizophrenia. We demonstrate that additional top-down information significantly improves performance in PSZ. The observed deficit in attentional control suggests a disturbance of GABAergic inhibition in early visual areas. Moreover, our findings are indicative of a mechanism for enhancing attentional control in PSZ, which could be utilized by pro-cognitive interventions. Thus, the current paradigm is suitable to reveal both preserved and compromised cognitive component processes in schizophrenia.

Reinforcement learning and working memory in mood disorders: A computational analysis in a developmental transdiagnostic sample

BACKGROUND

Mood disorders commonly onset during adolescence and young adulthood and are conceptually and empirically related to reinforcement learning abnormalities. However, the nature of abnormalities associated with acute symptom severity versus lifetime diagnosis remains unclear, and prior research has often failed to disentangle working memory from reward processes.

METHODS

The present sample (N = 220) included adolescents and young adults with a lifetime history of unipolar disorders (n = 127), bipolar disorders (n = 28), or no history of psychopathology (n = 62), and varying severity of mood symptoms. Analyses fitted a reinforcement learning and working memory model to an instrumental learning task that varied working memory load, and tested associations between model parameters and diagnoses or current symptoms.

RESULTS

Current severity of manic or anhedonic symptoms negatively correlated with task performance. Participants reporting higher severity of current anhedonia, or with lifetime unipolar or bipolar disorders, showed lower reward learning rates. Participants reporting higher severity of current manic symptoms showed faster working memory decay and reduced use of working memory.

LIMITATIONS

Computational parameters should be interpreted in the task environment (a deterministic reward learning paradigm), and developmental population. Future work should test replication in other paradigms and populations.

CONCLUSIONS

Results indicate abnormalities in reinforcement learning processes that either scale with current symptom severity, or correspond with lifetime mood diagnoses. Findings may have implications for understanding reward processing anomalies related to state-like (current symptom) or trait-like (lifetime diagnosis) aspects of mood disorders.

Test-retest reliability of prepulse inhibition (PPI) and PPI correlation with working memory

OBJECTIVE

Sensorimotor gating is experimentally operationalized by the prepulse inhibition (PPI) of the startle response (SR). Previous studies suggest high test-retest reliability of PPI and potential correlation with working memory (WM). Here, we aimed to validate and extend the test-retest reliability of PPI in healthy humans and its correlation with WM performance.

METHODS

We applied an acoustic startle PPI paradigm with four different prepulse intensities (64, 68, 72 and 76 dB) and two different WM tasks [n-back, change detection task (CDT)] in a group of 26 healthy adults (final sample size n = 23). To assess test-retest reliability, we performed all tests on two separate days ~27 days (range: 21-32 days) apart.

RESULTS

We were able to confirm high test-retest reliability of the PPI with a mean intraclass correlation (ICC) of > 0.80 and significant positive correlation of PPI with n-back but not with CDT performance. Detailed analysis showed that PPI across all prepulse intensities significantly correlated with both the 2-back and 0-back conditions, suggesting regulation by cross-conditional processes (e.g. attention). However, when removing the 0-back component from the 2-back data, we found a specific and significant correlation with WM for the 76-dB PPI condition.

CONCLUSION

With the present study, we were able to confirm the high test-retest reliability of the PPI in humans and could validate and expand on its correlation with WM performance.

Working Memory in People with Schizophrenia

Working memory (WM) refers to the ability to maintain a small number of representations in an activated, easily accessible state for a short period of time in the service of ongoing cognitive processing and behavior. Because WM is a resource critical for multiple forms of complex cognition and executive control of behavior, it is of central interest in the study of disorders such as schizophrenia that involve a broad compromise of cognitive function and in the regulation of goal-directed behavior. There is now robust evidence that WM impairment is characteristic of people with schizophrenia. The impairment includes both elementary storage capacity as well as more complex forms of WM that involve the manipulation and updating of WM representations. These impairments appear to underlie a substantial portion of the generalized cognitive deficit in schizophrenia. Neuroimaging studies have implicated widespread abnormalities in the broad neural system that subserves WM performance, consistent with the evidence of broad cognitive impairment seen in PSZ.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.