Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

Nutraceuticals have been taken as an alternative and add-on treatment for depressive disorders. Direct comparisons between different nutraceuticals and between nutraceuticals and placebo or antidepressants are limited. Thus, it is unclear which nutraceuticals are the most efficacious.

We conducted a network meta-analysis to estimate the comparative efficacy and tolerability of nutraceuticals for the treatment of depressive disorder in adults. The primary outcome was the change in depressive symptoms, as measured by the standard mean difference (SMD). Secondary outcomes included response rate, remission rate, and anxiety. Tolerability was defined as all-cause discontinuation and adverse events. Frequentist random-effect NMA was conducted.

Hundred and ninety-two trials involving 17,437 patients and 44 nutraceuticals were eligible for inclusion. Adjunctive nutraceuticals consistently showed better efficacy than antidepressants (ADT) alone in outcomes including SMD, remission, and response. Notable combinations were Eicosapentaenoic acid + Docosahexaenoic Acid plus ADT (EPA + DHA + ADT) (SMD 1.04, 95% confidence interval 0.64–1.44), S-Adenosyl Methionine (SAMe) + ADT (0.99, 0.31–1.68), curcumin + ADT (1.03, 0.55–1.51), Zinc + ADT (1.59, 0.63–2.55), tryptophan + ADT (1.24, 0.32–2.16), and folate + ADT (0.64, 0.17–1.10). Additionally, four nutraceutical monotherapies demonstrated superior efficacy compared to ADT: EPA + DHA (0.6, 0.32–0.88), SAMe (0.52, 0.18–0.87), curcumin (0.62, −0.17 to 1.40) and saffron (0.69, 0.34–1.04). It is noted that EPA + DHA, SAMe, and curcumin showed strong performance as either monotherapies or adjuncts to ADT. Most nutraceuticals showed comparable tolerability to placebo.

This extensive systematic review and NMA of nutraceuticals for treating depressive disorders indicated a number of nutraceuticals that could offer benefits, either as adjuncts or monotherapies.

In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Persistent cognitive deficits and functional impairments are associated with bipolar disorder (BD), even during the euthymic phase. The dysfunction of default mode network (DMN) is critical for self-referential and emotional mental processes and is implicated in BD. The current study aims to explore the balance of excitatory and inhibitory neurotransmitters, i.e. glutamate and γ-aminobutyric acid (GABA), in hubs of the DMN during the euthymic patients with BD (euBD).

Thirty-four euBD and 55 healthy controls (HC) were recruited to the study. Using proton magnetic resonance spectroscopy (1H-MRS), glutamate (with PRESS sequence) and GABA levels (with MEGAPRESS sequence) were measured in the medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC) and the posterior cingulate gyrus (PCC). Measured concentrations of excitatory glutamate/glutamine (Glx) and inhibitory GABA were used to calculate the excitatory/inhibitory (E/I) ratio. Executive and attentional functions were respectively assessed using the Wisconsin card-sorting test and continuous performance test.

euBD performed worse on attentional function than controls (p = 0.001). Compared to controls, euBD had higher E/I ratios in the PCC (p = 0.023), mainly driven by a higher Glx level in the PCC of euBD (p = 0.002). Only in the BD group, a marginally significant negative association between the mPFC E/I ratio (Glx/GABA) and executive function was observed (p = 0.068).

Disturbed E/I balance, particularly elevated Glx/GABA ratio in PCC is observed in euBD. The E/I balance in hubs of DMN may serve as potential biomarkers for euBD, which may also contribute to their poorer executive function.

Despite mounting evidence demonstrates circulating endothelial progenitor cells (cEPCs) quantitative changes in depression, no study has investigated cEPC functions in major depressive disorder (MDD). We investigated the role of cEPC adhesive and apoptotic functions in MDD.

We recruited 68 patients with MDD and 56 healthy controls (HCs). The depression symptoms, anxiety, psychosomatic symptoms, subjective cognitive dysfunction, quality of life, and functional disability were evaluated using the Hamilton Depression Rating Scale and Montgomery–Åsberg Depression Rating Scale, Hamilton Anxiety Rating Scale, Depression and Somatic Symptoms Scale (DSSS), Perceived Deficits Questionnaire-Depression, 12-Item Short Form Health Survey (SF-12), and Sheehan Disability Scale (SDS), respectively. Working memory and executive function were assessed using a 2-back task and Wisconsin Card Sorting Test (WCST). Inflammatory marker (soluble interleukin-6 receptor, C-reactive protein, and tumor necrosis factor-α receptor-1), cEPC adhesive, and apoptotic levels were measured using in vitro assays.

The MDD patients showed significantly lower cEPC adhesive levels than the HCs, and this difference in adhesive function remained statistically significant even after adjusting for inflammatory marker levels. The cEPC adhesion levels were in inverse correlations with commission and omission errors in 2-back task, the percent perseverative response and percent perseverative errors in WCST, and the DSSS and SDS scores, but in positive correlations with SF-12 physical and mental component scores. cEPC apoptotic levels did not differ significantly between the groups.

The findings indicate that cEPC adhesive function is diminished in MDD and impacts various aspects of cognitive and psychosocial functions associated with the disorder.

A proportion of patients with bipolar disorder (BD) manifests with only unipolar mania (UM). This study examined relevant clinical features and psychosocial characteristics in UM compared with depressive-manic (D-M) subgroups. Moreover, comorbidity patterns of physical conditions and psychiatric disorders were evaluated between the UM and D-M groups.

This clinical retrospective study (N = 1015) analyzed cases with an average of 10 years of illness duration and a nationwide population-based cohort (N = 8343) followed up for 10 years in the Taiwanese population. UM was defined as patients who did not experience depressive episodes and were not prescribed adequate antidepressant treatment during the disease course of BD. Logistic regression models adjusted for relevant covariates were used to evaluate the characteristics and lifetime comorbidities in the two groups.

The proportion of UM ranged from 12.91% to 14.87% in the two datasets. Compared with the D-M group, the UM group had more psychotic symptoms, fewer suicidal behaviors, a higher proportion of morningness chronotype, better sleep quality, higher extraversion, lower neuroticism, and less harm avoidance personality traits. Substantially different lifetime comorbidity patterns were observed between the two groups.

Patients with UM exhibited distinct clinical and psychosocial features compared with patients with the D-M subtype. In particular, a higher risk of comorbid cardiovascular diseases and anxiety disorders is apparent in patients with D-M. Further studies are warranted to investigate the underlying mechanisms for diverse presentations in subgroups of BDs.

Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.

The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18–30 years), and older (31–50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400).

We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240.

The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

High prevalence of insulin resistance (IR) has been reported in bipolar disorder (BD) patients. Importantly, impaired insulin sensitivity could modulate the course and treatment outcome in BD. Here, we hypothesized that insulin sensitivity could be potentially associated with the neurocognitive trajectory in euthymic BD. We aimed to examine differences in insulin sensitivity and executive function between BD patients and controls.

Sixty-two patients with BD receiving mood stabilizer treatment and 62 controls, matching age, sex, and body mass index, were recruited in this study. Insulin sensitivity was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR). The Wisconsin card-sorting test (WCST) was applied to test participants’ ability to shift cognitive set. Group differences were measured and multivariate regression analysis was performed to examine relationships among factors.

The results indicated that the HOMA-IR (P = .048) value in the patients with BD were significantly higher than those in controls. With regards to executive function, the BD patients performed significantly poorer than the control subjects (P < .05). Moreover, the interaction effect between BD diagnosis and HOMA-IR value on the WCST-preservation errors was significant (P = .01), and post-hoc analyses showed that the cognitive abilities were worse in the BD patients with a higher IR than in the others groups.

Insulin sensitivity is associated with the neurocognitive performance in euthymic BD patients. Although the underlying mechanisms remain unclear, interventions to improve insulin sensitivity could potentially improve the functional outcome of BD.

It is well-known that attention deficit hyperactivity disorder (ADHD) is associated with changes in the dopaminergic system. However, the relationship between central dopaminergic tone and the blood oxygen level-dependent (BOLD) signal during receipt of rewards and penalties in the corticostriatal pathway in adults with ADHD is unclear.

Single-photon emission computed tomography with [99mTC]TRODAT-1 was used to assess striatal dopamine transporter (DAT) availability. Event-related functional magnetic resonance imaging was conducted on subjects performing the Iowa Gambling Test.

DAT availability was found to be associated with the BOLD response, which was a covariate of monetary loss, in the medial prefrontal cortex (r = 0.55, P = .03), right ventral striatum (r = 0.69, P = .003), and right orbital frontal cortex (r = 0.53, P = .03) in adults with ADHD. However, a similar correlation was not found in the controls.

The results confirmed that dopaminergic tone may play a different role in the penalty-elicited response of adults with ADHD. It is plausible that a lower neuro-threshold accompanied by insensitivity to punishment could be exacerbated by the hypodopaminergic tone in ADHD.

Previous studies have indicated that there is dopamine transporter (DAT) dysregulation and P300 abnormality in adults with attention-deficit hyperactivity disorder (ADHD); however, the correlations among the three have not been fully explored.

A total of 11 adults (9 males and 2 females) with ADHD and 11 age-, sex-, and education-level-matched controls were recruited. We explored differences in DAT availability using single-photon emission computed tomography and P300 wave of event-related potentials between the two groups. The correlation between DAT availability and P300 performance was also examined.

DAT availability in the basal ganglia, caudate nucleus, and putamen was significantly lower in the ADHD group. Adults with ADHD had lower auditory P300 amplitudes at the Pz and Cz sites, as well as longer Fz latency than controls. DAT availability was negatively correlated to P300 latency at Pz and Fz.

Adults with ADHD had both abnormal DAT availability and P300 amplitude, suggesting that ADHD is linked to dysfunction of the central dopaminergic system and poor cognitive processes related to response selection and execution.