Development of a classifier for gambling disorder based on functional connections between brain regions.

AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Cognition and interpersonal coordination of patients with schizophrenia who have sports habits.

Team sports activities are effective for improving the negative symptoms and cognitive functions in patients with schizophrenia. However, the interpersonal coordination during the sports and visual cognition of patients with schizophrenia who have team sports habits are unknown. The main objectives of this study were to test two hypotheses: first, patients with schizophrenia perform the skill requiring ball passing and receiving worse than healthy controls; and second, the patients will be impaired in these functionings in accordance with the previous studies regarding schizophrenia in general. Twelve patients with schizophrenia and 15 healthy controls, who had habits in football, participated in this study. The participants performed three conventional cognitive tests and a 3-vs-1 ball possession task to evaluate their interpersonal coordination. The results showed that in the 3-vs-1 possession task, the displacement in the pass angle for the patients was significantly smaller than that for the control. The recall in the complex figure test, the performance in the trail making test, and that in the five-choice reaction task for the patients were worse than those for the control. Moreover, we found the significant partial correlations in the patients between the extradimensional shift error and the pass angle as well as between the time in the trail making test and the displacement in the pass angle, whereas there was no significant correlation in the control group. This study clarified the impaired interpersonal coordination during team sports and the visual cognition of patients with schizophrenia who have team sports habits.

Enhanced carbonyl stress and disrupted white matter integrity in schizophrenia.

Carbonyl stress is a state caused by an increase in rich reactive carbonyl compounds (RCOs); RCOs facilitate the formation of advanced glycation end products (AGEs), which are associated with various age-related illnesses. Recently, enhanced carbonyl stress and lower levels of pyridoxal, a kind of vitamin B6 that scavenges RCOs, have been shown to be associated with schizophrenia. Meanwhile, lower levels of pyridoxal have been reported to decrease myelination through the biochemical process of carbonyl stress. Despite a number of reports on white matter disruption in schizophrenia, it is unclear whether this disruption is related to enhanced carbonyl stress. Therefore, we investigated the relationship between carbonyl stress and white matter integrity in schizophrenia using diffusion tensor imaging. A total of 53 patients with schizophrenia and 83 age- and gender-matched healthy controls were recruited. We used plasma pentosidine, an AGE, and serum pyridoxal as carbonyl stress markers. Between-group differences in these carbonyl stress markers and their relationships with white matter integrity were investigated using Tract-Based Spatial Statistics. In the schizophrenia group, plasma pentosidine level was significantly higher and serum pyridoxal level was lower than those of controls. There was a significant negative correlation between plasma pentosidine and white matter integrity in the schizophrenia group, but not in the control group. Our findings suggest that enhanced carbonyl stress is a possible underlying mechanism of white matter microstructural disruption in schizophrenia.

Framing effects on financial and health problems in gambling disorder.

Gambling disorder (GD) patients show excessively risky decision-making in the financial domain. We aimed to clarify whether GD patients show risky decision-making in domain-general or in domain-specific. Furthermore, we also investigated the effect of the well-known cognitive bias, the framing effect on GD's decision-making under risk. Sixty-two male GD patients and 74 age-matched healthy male controls (HC) conducted a risky choice task in which they should choose solutions for difficult situations between a sure and a risky option that had the same expectations. Six situations were prepared for each financial and health domain. For each domain, three situations were presented with options using positive frames, and the other three situations were presented with options using negative frames. The results showed that GD chose more risky options in the financial domain with positive frames than HC, but chose comparably in the financial domain with negative frames, whereas GD and HC chose comparably in the health domain regardless of the frames. Thus, GD showed risky decision-making in domain-specific. In addition, the results indicate the importance of considering the influence of the framing effect for assessment of risky decision-making by GD. Domains and the influence of the framing effect should be considered when decision-making patterns of neuropsychiatric disorders are studied.

Central Histamine Boosts Perirhinal Cortex Activity and Restores Forgotten Object Memories.

BACKGROUND: A method that promotes the retrieval of lost long-term memories has not been well established. Histamine in the central nervous system is implicated in learning and memory, and treatment with antihistamines impairs learning and memory. Because histamine H3 receptor inverse agonists upregulate histamine release, the inverse agonists may enhance learning and memory. However, whether the inverse agonists promote the retrieval of forgotten long-term memory has not yet been determined. METHODS: Here, we employed multidisciplinary methods, including mouse behavior, calcium imaging, and chemogenetic manipulation, to examine whether and how the histamine H3 receptor inverse agonists, thioperamide and betahistine, promote the retrieval of a forgotten long-term object memory in mice. In addition, we conducted a randomized double-blind, placebo-controlled crossover trial in healthy adult participants to investigate whether betahistine treatment promotes memory retrieval in humans. RESULTS: The treatment of H3 receptor inverse agonists induced the recall of forgotten memories even 1 week and 1 month after training in mice. The memory recovery was mediated by the disinhibition of histamine release in the perirhinal cortex, which activated the histamine H2 receptor. Histamine depolarized perirhinal cortex neurons, enhanced their spontaneous activity, and facilitated the reactivation of behaviorally activated neuronal ensembles. A human clinical trial revealed that treatment of H3 receptor inverse agonists is specifically more effective for items that are more difficult to remember and subjects with poorer performance. CONCLUSIONS: These results highlight a novel interaction between the central histamine signaling and memory engrams.

Amygdala volume is associated with risky probability cognition in gambling disorder.

Gambling disorder (GD) is characterized by continual gambling despite negative consequences. Risky decision-making is a hallmark of the disorder. We applied a tool from behavioral economics for assessing probability cognition in both gain and loss domains to GD. We aimed to examine the alteration of probability cognition and its relationship with brain structure in GD. Forty-six GD patients and 52 age-matched healthy controls (HCs) conducted a risky choice task in which subjects should choose between a sure and a risky option in both loss and gain domains. The distortion and elevation parameters of the probability weighting function were estimated. We compared the parameters between GD and HC and examined their relationships with the striatum and amygdala volumes in GD. GD showed greater elevation parameter in the gain domain and smaller regional gray matter volume in the left amygdala than HC. The elevation parameter in the gain domain showed a negative correlation with the left amygdala volume in GD. Altered probability cognition in the gain domain but not in the loss domain might be more relevant to risky decision-making in GD. Our findings indicate that alteration in the amygdala might play a significant role in risky decision-making of GD. Longitudinal studies are recommended to examine the causal relationship between brain abnormalities and risky decision-making in GD.