Hidehiko Takahashi

Background: Bipolar disorder (BP) affects 1% to 3% of adults worldwide, with diagnostic delays averaging 6 years and high initial misdiagnosis rates. The absence of objective diagnostic measures contributes to clinical uncertainty and suboptimal outcomes. Objective: This study aimed to (1) develop a machine learning model for BP diagnosis and mood state classification using linguistic and acoustic features, (2) identify features associated with different mood states by comparing each with healthy controls (HC), and (3) explore whether features distinguish between mood states within BP. Methods: We analyzed data prospectively collected over 7 years from 61 patients with BP and 99 HC across Japan. Speech samples were collected via semistructured interviews over 1 to 5 visits and analyzed cross-sectionally, using aggregated or single-visit observations per participant rather than within-person longitudinal modeling. We extracted 6373 acoustic features (openSMILE 3.0, ComParE 2016) and 211 linguistic features. Least absolute shrinkage and selection operator (LASSO)–regularized logistic regression with nested cross-validation and age- and sex-matched undersampling was used. Mood states were classified as depressive state (DS; Hamilton Depression Rating Scale-17 [HAMD-17] ≥8, Young Mania Rating Scale [YMRS] ≤7), state with manic/mixed symptoms (MS/MX; YMRS ≥8), or euthymic state (ES; HAMD-17 ≤7, YMRS ≤7). Seven classification tasks included BP vs HC, each mood state vs HC, and interstate comparisons. Feature importance was validated via permutation testing ( P <.05). Medication sensitivity analyses were performed. Results: The model achieved high discrimination for BP vs HC (area under the receiver operating characteristic curve [AUC]=0.916, 95% CI 0.854‐0.958; sensitivity 0.933; specificity 0.859) and strong performance for mood states vs HC (AUC=0.805‐0.874). Acoustic features comprised 96% to 98% of the features achieving statistical significance ( P <.05), including mel-frequency cepstral coefficients, spectral characteristics, and voice quality measures, showing strong correlations with diagnostic probabilities (|ρ|=0.35‐0.85) and symptom severity (HAMD-17: ρ=–0.51; YMRS: ρ=–0.37); 2 to 4 linguistic features per comparison also reached significance. With sample independence ensured for interstate comparisons, DS vs ES significantly exceeded chance (AUC=0.760, permutation P =.03), whereas MS/MX vs ES (AUC=0.601, P =.20) and DS vs MS/MX (AUC=0.569, P =.30) did not, suggesting that markers primarily reflect persistent disease characteristics with partial state-associated components in depressive states. Conclusions: The combined acoustic-linguistic model, driven primarily by acoustic features, shows potential as an objective aid for BP diagnosis (AUC>0.9), largely free of medication confounding; some features remained detectable during euthymia, although ES discrimination could not be fully separated from concurrent antipsychotic treatment. Although DS showed partial distinguishability from ES, overall limitations remain for monitoring mood state changes. Findings require validation in diverse populations and against other psychiatric conditions.

Background: Linguistic abnormalities in schizophrenia (SCZ) span morphological, syntactic, semantic, and discourse levels. Converging cross-linguistic evidence suggests that SCZ may involve semantic narrowing alongside reduced syntactic differentiation, yet how these changes co-occur across linguistic domains and whether they represent core, task-general disturbances remains unclear. We applied a multilevel NLP framework to a large Japanese dataset to identify structurally related linguistic markers of SCZ across elicitation contexts. Methods: Speech from 104 patients with SCZ and 101 healthy controls was collected through semi-structured interviews. Transcripts from free conversation, storytelling, and picture description were analyzed using GiNZA, Word2Vec, TF-IDF, and SentenceBERT to extract 76 morphosyntactic, semantic, and discourse features. Factor analysis identified representative features independent of diagnosis, which were tested using generalized estimating equations and validated with bootstrap and permutation procedures. Cross-task stability was examined to determine core linguistic markers. Results: In free conversation, reduced Case-particle (Kakujoshi) and Adverb use and increased Mean Pairwise Word Similarity were strongly associated with SCZ (AUC = 0.87, 95% CI: 0.74–0.97). Adverbial, case-particle, and semantic-network measures functioned as cross-task markers. Conclusions: SCZ involves multidimensional language disturbances characterized by a tripartite linguistic phenotype of diminished morphosyntactic explicitness, semantic narrowing, and reduced modification-based contextual modulation in spontaneous discourse. Extending cross-linguistic evidence, our results indicate that lexical-semantic contraction co-occurs with reduced overt marking of argument relations in Japanese, alongside weakened adverbial elaboration and framing – suggesting convergent, largely task-general dimensions of SCZ language pathology, most evident in free conversation.

Background and aims: Gambling disorder (GD) patients continue to gamble despite negative consequences, and this behavior can be partly attributed to their insensitivity to failures and losses. GD may worsen over time and may stem from dysfunctions in the reward system and habenula, which encodes negative reward prediction errors. We aimed to elucidate habenular volume alterations that could intensify with illness duration and demonstrate heterogeneity in GD patients. Methods: Sixty-eight male GD patients and 75 male healthy controls were included. We computed the habenular volume by deep learning–based auto-segmentation from T1-weighted MRI data and examined the between-group differences. We retrospectively calculated illness duration and evaluated the effects of illness duration, personality traits, and symptom severity on habenular volume in GD patients. Results: GD patients showed comparable habenular volumes to those of healthy participants. After controlling for age, smoking status, IQ, and MRI scanner model, partial correlation analysis revealed a negative correlation between illness duration and habenular volume in GD patients ( r = −0.26, p = 0.029). A significant correlation between habenular volume and illness duration appeared only in the severe subgroup ( r = −0.42, p = 0.011). In the severe subgroup, higher neuroticism and lower conscientiousness were associated with larger habenular volume. Discussion and Conclusions: Habenular volume was negatively correlated with illness duration in GD patients, particularly in severe cases, and was influenced by symptom severity and personality traits. Habenular structural heterogeneity is based on severity and personality and possibly contributes to persistent gambling despite aversive consequences.

Background: The increasing popularity of mental-health information on social media platforms such as TikTok is raising concerns regarding misinformation. Previous research is limited to single disorders and videos in the English language only. Our objective was to investigate the quality of mental health information on German-language TikTok for a broader spectrum of disorders. Method: Thirty German-language TikTok-videos of each of the six most viewed hashtags on mental disorders (attention-deficit and hyperactivity disorder (ADHD), depression, autism, anxiety disorder, narcissism and post-traumatic stress disorder (PTSD)) were classified regarding authorship and rated either as “correct”, “overgeneralized”, “incorrect” or “subjective experience”. The modified DISCERN (mDISCERN) and the Global Quality Scale (GQS) were used to rate reliability and quality of information for patients. Results: The 177 videos finally included in this study gathered a total of 94,348,220 views and 19.2% ( n = 34) of the videos were rated as correct, 33.3% ( n = 59) as incorrect, 18.1% ( n = 32) as overgeneralized and 29.4% ( n = 52) as personal experience. Chi-Square tests and Kruskal-Wallis tests showed significant relationships between either authorship or diagnosis and quality and reliability. Videos on PTSD and videos by expert authors showed the best and videos on narcissism and videos by laypeople the worst overall results. Conclusion: With around half of the analyzed videos supplying incorrect information, the quality of German-language TikTok mental health content is insufficient. Differences in the quality of content seem to be influenced by the topic and the authorship. Healthcare institutions and clinicians should be aware of this, educate patients accordingly, and could improve the quality of information by participating in online discourses.

Aim: Problematic smartphone use (PSU) is a behavioral addiction characterized by excessive smartphone engagement interfering with daily life. 10–30% of adolescents are suggested to be affected. This study examined time-of-day variations in smartphone use between adolescent patients with PSU and a non-clinical adolescent group using objective data to evaluate usage pattern differences.Method: 72 adolescents (31 PSU, 41 CNT) participated. Screen-on time was recorded continuously for two weeks via ULoggerApp. To compare temporal patterns between groups, each day was divided into three 8-h segments (0:00–7:59, 8:00–15:59, 16:00–23:59) and a linear mixed-effects model (LMM) was applied to account for individual baseline and diurnal usage patterns. To discriminate PSU from CNT, each day was also divided into eight 3-h segments for finer time windows. A random forest using time segments as predictors was applied and Spearman correlations between 3-h segment screen time and Smartphone Addiction Scale–Short Version (SAS-SV) scores were calculated.Result: The PSU group showed significantly longer total screen time and higher daytime use than CNT. The random forest model identified afternoon to early evening (15:00–17:59) as most informative for classifying PSU, with weaker group differences for nighttime use (0:00–7:59). SAS-SV scores correlated positively with screen time from early afternoon to evening (12:00–20:59), strongest for 12:00–14:59 (ρ = 0.366, p = 0.003).Conclusion: Elevated smartphone use during after-school hours is closely linked to PSU and higher symptom severity. Interventions targeting device use and daily structure in the late afternoon and early evening may help reduce the functional impact of PSU in adolescents.

Aim: The long‐term impact of pharmacological treatment on cognitive function in adults with attention‐deficit/hyperactivity disorder (ADHD), particularly whether benefits persist after treatment discontinuation, is not well understood. This study aimed to investigate whether prior medication history is associated with different patterns of cognitive change following methylphenidate (MPH) administration. Methods: Thirty‐six adults with ADHD (17 medication‐naive, 19 medication‐experienced) were assessed on five cognitive tasks, the multitasking test (MTT), reaction time (RTI), rapid visual processing (RVP), stop signal task (SST), and spatial working memory (SWM), from the Cambridge Neuropsychological Test Automated Battery baseline and following a period of clinically stable MPH treatment (Post). We used mixed‐design ancova s to test for Time × Medication history interactions. Results: We found a significant interaction in the SWM task (6‐box condition; q = 0.044). A sensitivity analysis excluding atomoxetine‐experienced participants showed a preserved effect size but trend‐level significance ( q = 0.061). Post hoc analyses revealed that the medication‐naive group's SWM performance significantly improved from baseline to Post, whereas the medication‐experienced group, who exhibited a significantly better baseline performance, showed no change. Exploratory analyses suggested similar trends across the other SWM difficulty levels. No significant interactions were found for other tasks. Conclusion: Two groups showed differential trajectories of change in the SWM task. Rather than reflecting a purely pharmacological effect, these patterns may involve a combination of baseline cognitive differences, learned cognitive strategies, and pre‐existing individual characteristics. These preliminary findings highlight the importance of considering treatment history in clinical practice and research.

Aim: Attention‐deficit/hyperactivity disorder (ADHD) is associated with dysregulation of dopamine and norepinephrine neurotransmission. Extended‐release methylphenidate (MPH), a first‐line treatment for ADHD, modulates these systems. However, its specific effects on the dopamine transporter (DAT) and norepinephrine transporter (NET) are inadequately characterized. This study evaluated the effects of MPH on DAT and NET binding in adults with ADHD and examined the relationship between transporter binding and cognitive improvement. Methods: This longitudinal dual‐tracer positron emission tomography (PET) study used [ ¹⁸ F]‐FEPE2I for DAT imaging and (S,S)‐[ ¹⁸ F]‐ FMeNER‐D2 for NET imaging. PET imaging and cognitive assessments were conducted on 21 adults with ADHD before MPH treatment, and 12 of these participants were reevaluated after achieving treatment stabilization. Results: MPH treatment significantly decreased DAT binding in the caudate and putamen and NET binding in the thalamus and pons. Improvements in attention and multitasking abilities were observed. Baseline NET binding in the pons was higher in individuals with ADHD than in controls, and higher baseline DAT binding in the putamen predicted improvement in sustained attention. However, changes in DAT and NET binding did not correlate with cognitive improvement. Blood concentration analysis revealed that even at MPH blood levels that resulted in high DAT occupancy in the striatum, NET occupancy in the thalamus remained moderate. Conclusions: MPH simultaneously reduced DAT and NET binding in ADHD, enhanced dopamine and norepinephrine transmission, and improved cognitive function. The differential occupancy of DAT and NET suggests distinct contributions to the therapeutic effects of MPH.

Background: Problematic smartphone use (PSU) involves excessive smartphone engagement that disrupts daily functioning and is linked to attentional control deficits and altered reward processing. The nucleus accumbens (NAcc), a key structure in the reward system, may contribute to difficulty disengaging from rewarding digital content. This study examined relationships between NAcc volume, attentional switching, and objectively measured nighttime screen time in individuals with PSU. Methods: Fifty-three participants (aged ≥ 13 years) from an outpatient internet dependency clinic completed psychological assessments, brain MRI, and smartphone logging. PSU was diagnosed by two psychiatrists. Attentional switching was measured via the Autism Spectrum Quotient subscale. Nighttime screen time (00:00–06:00) was recorded via smartphone. MRI-derived NAcc volumes were normalized to total gray matter volume. Correlations, multiple regression (controlling for ASD and ADHD), and mediation analyses were conducted. Results: Difficulty in attention switching correlated with larger right NAcc volume (r = 0.45, p = 0.012) and increased nighttime screen time (r = 0.44, p = 0.014). Right NAcc volume also correlated with nighttime screen time (r = 0.46, p = 0.012). Regression showed right NAcc volume predicted nighttime screen time (β = 0.33, p = 0.022), whereas attentional switching was not significant. Mediation was unsupported. Sensitivity analyses confirmed associations. Conclusions: Larger right NAcc volume independently predicts prolonged nighttime smartphone use and is associated with impaired attentional switching in PSU. Structural variations in reward-related regions may underlie difficulty disengaging from digital content. Integrating neurobiological, cognitive, and behavioral measures offers a framework for understanding PSU.