New infundibulopelvic angle measurement method can predict stone-free rates following retrograde intrarenal surgery.

To enhance the accuracy of predicting stone-free rates after retrograde intrarenal surgery, we devised a novel approach to assess the renal infundibulopelvic angle. We conducted a retrospective review of patient records for those who underwent retrograde intrarenal surgery for renal stones between April 2018 and August 2019. Patient demographics, stone characteristics, and perioperative data were recorded. Subsequently, we introduced a modified angle measurement called the pelvic stone angle and evaluated its predictive performance for stone-free rates by comparing it with the traditional method in scoring systems. A total of 43 individuals were included in this study. Notable differences in stone burden and Hounsfield unit measurements were found between stone-free and non-stone-free patients. The pelvic stone angle demonstrated a good model fit when used in scoring systems, performing equally well as the conventional approach. The area under the receiver operating characteristic curve for the R.I.R.S. scoring system using the pelvic stone angle and the conventional approach did not show a significant difference. In conclusion, the predictive ability of the pelvic stone angle for stone-free rates was comparable to the old measurement method. Moreover, scoring systems using the pelvic stone angle exhibited a better model fit than those using the conventional approach.

Atypical development in white matter microstructures in ADHD: A longitudinal diffusion imaging study.

BACKGROUND: In cross-sectional studies, alterations in white matter microstructure are evident in children with attention-deficit/hyperactivity disorder (ADHD) but not so prominent in adults with ADHD compared to typically-developing controls (TDC). Moreover, the developmental trajectories of white matter microstructures in ADHD are unclear, given the limited longitudinal imaging studies that characterize developmental changes in ADHD vs. TDC. METHODS: This longitudinal study acquired diffusion spectrum imaging (DSI) at two time points. The sample included 55 participants with ADHD and 61 TDC. The enrollment/first DSI age ranged from 7 to 18 years, with a five-year mean follow-up time. We examined time-by-diagnosis interaction on the generalized fractional anisotropy (GFA) of 45 white matter tracts, adjusting for confounding factors and correcting for multiple comparisons. We also tested whether the longitudinal changes of microstructures were associated with ADHD symptoms and attention performance in a computerized continuous performance test. RESULTS: Participants with ADHD showed more rapid development of GFA in the arcuate fasciculus, superior longitudinal fasciculus, frontal aslant tract, cingulum, inferior fronto-occipital fasciculus (IFOF), frontostriatal tract connecting the prefrontal cortex (FS-PFC), thalamic radiation, corticospinal tract, and corpus callosum. Within participants with ADHD, more rapid GFA increases in cingulum and FS-PFC were associated with slower decreases in inattention symptoms. In addition, in all participants, more rapid GFA increases in cingulum and IFOF were associated with greater improvement in attention performance. CONCLUSION: Our findings suggest atypical developmental trajectories of white matter tracts in ADHD, characterized by normalization and possible compensatory neuroplastic processes with age from childhood to early adulthood.

Altered gut microbiota correlates with behavioral problems but not gastrointestinal symptoms in individuals with autism.

BACKGROUND: Despite inconsistent results across studies, emerging evidence suggests that the microbial micro-environment may be associated with autism spectrum disorder (ASD). Geographical and cultural factors highly impact microbial profiles, and there is a shortage of data from East Asian populations. This study aimed to comprehensively characterize microbial profiles in an East Asian sample and explore whether gut microbiota contributes to clinical symptoms, emotional/behavioral problems, and GI symptoms in ASD. METHODS: We assessed 82 boys and young men with ASD and 31 typically developing controls (TDC), aged 6-25 years. We analyzed the stool sample of all participants with 16S V3-V4 rRNA sequencing and correlated its profile with GI symptoms, autistic symptoms, and emotional/behavioral problems. RESULTS: Autistic individuals, compared to TDC, had worse GI symptoms. There were no group differences in alpha diversity of species richness estimates (Shannon-wiener and Simpson diversity indices). Participants with ASD had an increased relative abundance of Fusobacterium, Ruminococcus torques group (at the genus level), and Bacteroides plebeius DSM 17135 (at the species level), while a decreased relative abundance of Ruminococcaceae UCG 013, Ervsipelotrichaceae UCG 003, Parasutterella, Clostridium sensu stricto 1, Turicibacter (at the genus level), and Clostridium spiroforme DSM 1552 and Intestinimonas butyriciproducens (at the species level). Altered taxonomic diversity in ASD significantly correlated with autistic symptoms, thought problems, delinquent behaviors, self dysregulation, and somatic complaints. We did not find an association between gut symptoms and gut microbial dysbiosis. CONCLUSIONS: Our findings suggest that altered microbiota are associated with behavioral phenotypes but not GI symptoms in ASD. The function of the identified microbial profiles mainly involves the immune pathway, supporting the hypothesis of a complex relationship between altered microbiome, immune dysregulation, and ASD that may advance the discovery of molecular biomarkers for ASD.

Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD.

The neurodevelopmental model of schizophrenia is supported by multi-level impairments shared among schizophrenia and neurodevelopmental disorders. Despite schizophrenia and typical neurodevelopmental disorders, i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), as disorders of brain dysconnectivity, no study has ever elucidated whether whole-brain white matter (WM) tracts integrity alterations overlap or diverge between these three disorders. Moreover, whether the linked dimensions of cognition and brain metrics per the Research Domain Criteria framework cut across diagnostic boundaries remains unknown. We aimed to map deviations from normative ranges of whole-brain major WM tracts for individual patients to investigate the similarity and differences among schizophrenia (281 patients subgrouped into the first-episode, subchronic and chronic phases), ASD (175 patients), and ADHD (279 patients). Sex-specific WM tract normative development was modeled from diffusion spectrum imaging of 626 typically developing controls (5-40 years). There were three significant findings. First, the patterns of deviation and idiosyncrasy of WM tracts were similar between schizophrenia and ADHD alongside ASD, particularly at the earlier stages of schizophrenia relative to chronic stages. Second, using the WM deviation patterns as features, schizophrenia cannot be separated from neurodevelopmental disorders in the unsupervised machine learning algorithm. Lastly, the canonical correlation analysis showed schizophrenia, ADHD, and ASD shared linked cognitive dimensions driven by WM deviations. Together, our results provide new insights into the neurodevelopmental facet of schizophrenia and its brain basis. Individual's WM deviations may contribute to diverse arrays of cognitive function along a continuum with phenotypic expressions from typical neurodevelopmental disorders to schizophrenia.

Validation of neuroimaging-based brain age gap as a mediator between modifiable risk factors and cognition.

Neuroimaging-based brain age gap (BAG) is presumably a mediator linking modifiable risk factors to cognitive changes, but this has not been verified yet. To address this hypothesis, modality-specific brain age models were constructed and applied to a population-based cohort (N = 326) to estimate their BAG. Structural equation modeling was employed to investigate the mediation effect of BAG between modifiable risk factors (assessed by 2 cardiovascular risk scores) and cognitive functioning (examined by 4 cognitive assessments). The association between higher burden of modifiable risk factors and poorer cognitive functioning can be significantly mediated by a larger BAG (multimodal: p = 0.014, 40.8% mediation proportion; white matter-based: p = 0.023, 15.7% mediation proportion), which indicated an older brain. Subgroup analysis further revealed a steeper slope (p = 0.019) of association between cognitive functioning and multimodal BAG in the group of higher modifiable risks. The results confirm that BAG can serve as a mediating indicator linking risk loadings to cognitive functioning, implicating its potential in the management of cognitive aging and dementia.

Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics.

Conceptualizing mental disorders as deviations from normative functioning provides a statistical perspective for understanding the individual heterogeneity underlying psychiatric disorders. To broaden the understanding of the idiosyncrasy of brain aging in schizophrenia, we introduced an imaging-derived brain age paradigm combined with normative modeling as novel brain age metrics. We constructed brain age models based on GM, WM, and their combination (multimodality) features of 482 normal participants. The normalized predicted age difference (nPAD) was estimated in 147 individuals with schizophrenia and their 130 demographically matched controls through normative models of brain age metrics and compared between the groups. Regression analyses were also performed to investigate the associations of nPAD with illness duration, onset age, symptom severity, and intelligence quotient. Finally, regional contributions to advanced brain aging in schizophrenia were investigated. The results showed that the individuals exhibited significantly higher nPAD (P < 0.001), indicating advanced normative brain age than the normal controls in GM, WM, and multimodality models. The nPAD measure based on WM was positively associated with the negative symptom score (P = 0.009), and negatively associated with the intelligence quotient (P = 0.039) and onset age (P = 0.006). The imaging features that contributed to nPAD mostly involved the prefrontal, temporal, and parietal lobes, especially the precuneus and uncinate fasciculus. This study demonstrates that normative brain age metrics could detect advanced brain aging and associated clinical and neuroanatomical features in schizophrenia. The proposed nPAD measures may be useful to investigate aberrant brain aging in mental disorders and their brain-phenotype relationships.

Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.

OBJECTIVE: The heterogeneity of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) preclude definitive identification of neurobiomarkers and biological risks. High clinical overlap suggests multifaceted circuit-level alterations across diagnoses, which remains elusive. This study investigated whether individuals with ADHD or ASD and their unaffected siblings constitute a spectrum of neurodevelopmental conditions in terms of white matter etiology. METHODS: Sex-specific white matter tract normative development was modeled from diffusion MRI of 626 typically developing control subjects (ages 5-40 years; 376 of them male). Individualized metrics estimating white matter tract deviation from the age norm were derived for 279 probands with ADHD, 175 probands with ASD, and their unaffected siblings (ADHD, N=121; ASD, N=72). RESULTS: ASD and ADHD shared diffuse white matter tract deviations in the commissure and association tracts (rho=0.54; p<0.001), while prefrontal corpus callosum deviated more remarkably in ASD (effect size=-0.36; p<0.001). Highly correlated deviance patterns between probands and unaffected siblings were found in both ASD (rho=0.69; p<0.001) and ADHD (rho=0.51; p<0.001), but only unaffected sisters of ASD probands showed a potential endophenotype in long-range association fibers and projection fibers connecting prefrontal regions. ADHD and ASD shared significant white matter tract idiosyncrasy (rho=0.55; p<0.001), particularly in tracts connecting prefrontal regions, not identified in either sibling group. Canonical correlation analysis identified multiple dimensions of psychopathology/cognition across categorical entities; autistic, visual memory, intelligence/planning/inhibition, nonverbal-intelligence/attention, working memory/attention, and set-shifting/response-variability were associated with distinct sets of white matter tract deviations. CONCLUSIONS: When conceptualizing neurodevelopmental disorders as white matter tract deviations from normative patterns, ASD and ADHD are more alike than different. The modest white matter tract alterations in siblings suggest potential endophenotypes in these at-risk populations. This study further delineates brain-driven dimensions of psychopathology/cognition, which may help clarify within-diagnosis heterogeneity and high between-diagnosis co-occurrence.

Generalization of diffusion magnetic resonance imaging-based brain age prediction model through transfer learning.

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p â€‹< â€‹0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test-retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient â€‹= â€‹0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p â€‹< â€‹0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and -0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.