Neurometabolic and functional connectivity basis of prosocial behavior in early adolescence.

Human prosocial behavior (PB) emerges in childhood and matures during adolescence. Previous task-related functional magnetic resonance imaging (fMRI) studies have reported involvement of the medial prefrontal cortex including the anterior cingulate cortex (ACC) in social cognition in adolescence. However, neurometabolic and functional connectivity (FC) basis of PB in early adolescence remains unclear. Here, we measured GABA levels in the ACC and FC in a subsample (aged 10.5-13.4 years) of a large-scale population-based cohort with MR spectroscopy (MEGA-PRESS) and resting-state fMRI. PB was negatively correlated with GABA levels in the ACC (N = 221), and positively correlated with right ACC-seeded FC with the right precentral gyrus and the bilateral middle and posterior cingulate gyrus (N = 187). Furthermore, GABA concentrations and this FC were negatively correlated, and the FC mediated the association between GABA levels and PB (N = 171). Our results from a minimally biased, large-scale sample provide new insights into the neurometabolic and neurofunctional correlates of prosocial development during early adolescence.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers.

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

Computational neuroscience approach to biomarkers and treatments for mental disorders.

Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry.

Development of a neurofeedback protocol targeting the frontal pole using near-infrared spectroscopy.

AIM: Neurofeedback has been studied with the aim of controlling cerebral activity. Near-infrared spectroscopy is a non-invasive neuroimaging technique used for measuring hemoglobin concentration changes in cortical surface areas with high temporal resolution. Thus, near-infrared spectroscopy may be useful for neurofeedback, which requires real-time feedback of repeated brain activation measurements. However, no study has specifically targeted neurofeedback, using near-infrared spectroscopy, in the frontal pole cortex. METHODS: We developed an original near-infrared spectroscopy neurofeedback system targeting the frontal pole cortex. Over a single day of testing, each healthy participant (n = 24) received either correct or incorrect (Sham) feedback from near-infrared spectroscopy signals, based on a crossover design. RESULTS: Under correct feedback conditions, significant activation was observed in the frontal pole cortex (P = 0.000073). Additionally, self-evaluation of control and metacognitive beliefs were associated with near-infrared spectroscopy signals (P = 0.006). CONCLUSION: The neurofeedback system developed in this study might be useful for developing control of frontal pole cortex activation.

Auditory mismatch negativity and P3a in response to duration and frequency changes in the early stages of psychosis.

BACKGROUND: A shorter duration of untreated psychosis in patients with schizophrenia results in better symptomatic and functional outcomes. Therefore, identifying biological markers in the early stages of psychosis is an important step toward early detection and intervention. Mismatch negativity (MMN) and P3a are leading candidate biomarkers. MMN measures differ in their sensitivity to varying deviants. However, this has not been fully addressed in assessing the early stages of psychosis. In the current study, we examined MMN/P3a to duration deviant (dMMN/dP3a) and frequency deviant (fMMN/fP3a) in the early stages of psychosis. To our knowledge, this is the first study that examined both MMN/P3a to duration deviant (dMMN/dP3a) and frequency deviant (fMMN/fP3a) in the early stages of psychosis. METHODS: Participants consisted of 20 patients with first episode schizophrenia (FES), 21 ultra-high risk (UHR) individuals, and 22 healthy controls (HC). We measured dMMN/dP3a and fMMN/fP3a ERP components by means of a 64 electrodes-cap for EEG recording, and we used two-tone auditory oddball paradigms with 2000 stimuli. RESULTS: The amplitude of dMMN was significantly reduced in FES and UHR compared to HC. The amplitude of fMMN showed no significant difference among the three groups. The amplitudes of dP3a and fP3a were significantly reduced in FES and UHR compared to HC. CONCLUSION: These findings suggest that dMMN may have higher sensitivity than fMMN whereas dP3a and fP3a may have similar sensitivity in the early stages of psychosis.

Men and women show distinct brain activations during imagery of sexual and emotional infidelity.

Jealousy-related behaviors such as intimate partner violence and morbid jealousy are more common in males. Principal questionnaire studies suggest that men and women have different modules to process cues of sexual and emotional infidelity. We aimed to elucidate the neural response to sentences depicting sexual and emotional infidelity in men and women using functional magnetic resonance imaging. Although there was no sex difference in the self-rating score of jealousy for sexual and emotional infidelity, men and women showed different brain activation patterns in response to the two types of infidelity. During jealous conditions, men demonstrated greater activation than women in the brain regions involved in sexual/aggressive behaviors such as the amygdala and hypothalamus. In contrast, women demonstrated greater activation in the posterior superior temporal sulcus. Our fMRI results are in favor of the notion that men and women have different neuropsychological modules to process sexual and emotional infidelity. Our findings might contribute to a better understanding of the neural basis of the jealousy-related behaviors predominantly observed in males.