Neural synchronization predicts marital satisfaction.

Marital attachment plays an important role in maintaining intimate personal relationships and sustaining psychological well-being. Mate-selection theories suggest that people are more likely to marry someone with a similar personality and social status, yet evidence for the association between personality-based couple similarity measures and marital satisfaction has been inconsistent. A more direct and useful approach for understanding fundamental processes underlying marital satisfaction is to probe similarity of dynamic brain responses to maritally and socially relevant communicative cues, which may better reflect how married couples process information in real time and make sense of their mates and themselves. Here, we investigate shared neural representations based on intersubject synchronization (ISS) of brain responses during free viewing of marital life-related, and nonmarital, object-related movies. Compared to randomly selected pairs of couples, married couples showed significantly higher levels of ISS during viewing of marital movies and ISS between married couples predicted higher levels of marital satisfaction. ISS in the default mode network emerged as a strong predictor of marital satisfaction and canonical correlation analysis revealed a specific relation between ISS in this network and shared communication and egalitarian components of martial satisfaction. Our findings demonstrate that brain similarities that reflect real-time mental responses to subjective perceptions, thoughts, and feelings about interpersonal and social interactions are strong predictors of marital satisfaction, reflecting shared values and beliefs. Our study advances foundational knowledge of the neurobiological basis of human pair bonding.

Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles.

Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.

Rapid AMR prediction in Pseudomonas aeruginosa combining MALDI-TOF MS with DNN model.

BACKGROUND: Pseudomonas aeruginosa is a significant clinical pathogen that poses a substantial threat due to its extensive drug resistance. The rapid and precise identification of this resistance is crucial for effective clinical treatment. Although matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been used for antibiotic susceptibility differentiation of some bacteria in recent years, the genetic diversity of P. aeruginosa complicates population analysis. Rapid identification of antimicrobial resistance (AMR) in P. aeruginosa based on a large amount of MALDI-TOF-MS data has not yet been reported. In this study, we employed publicly available datasets for P. aeruginosa, which contain data on bacterial resistance and MALDI-TOF-MS spectra. We introduced a deep neural network model, synergized with a strategic sampling approach (SMOTEENN) to construct a predictive framework for AMR of three widely used antibiotics. RESULTS: The framework achieved area under the curve values of 90%, 85%, and 77% for Tobramycin, Cefepime, and Meropenem, respectively, surpassing conventional classifiers. Notably, random forest algorithm was used to assess the significance of features and post-hoc analysis was conducted on the top 10 features using Cohen's d. This analysis revealed moderate effect sizes (d = 0.5-0.8) in Tobramycin and Cefepime models. Finally, putative AMR biomarkers were identified in this study. CONCLUSIONS: This work presented an AMR prediction tool specifically designed for P. aeruginosa, which offers a hopeful pathway for clinical decision-making.

A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain.

Much recent attention has been directed toward investigating the spatial and temporal organization of brain dynamics, but the rules which constrain the variation of spatio-temporal organization in functional connectivity under different brain states remain unclear. Here, we developed a novel computational approach based on tensor decomposition and regularization to represent dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights. In this approach, dynamic modules represent co-activating functional connectivity patterns, and time-varying weights represent the temporal expression of dynamic modules. We applied this dynamic decomposition model (DDM) on a resting-state fMRI dataset and found that whole-brain dynamic functional connectivity can be decomposed as a linear combination of eight dynamic modules which we summarize as 'high order modules' and 'primary-high order modules', according to their spatial attributes and correspondence with existing intrinsic functional brain networks. By clustering the time-varying weights, we identified five brain states including three major states and two minor states. We found that state transitions mainly occurred between the three major states, and that temporal variation of dynamic modules may contribute to brain state transitions. We then conceptualized the variability of weights as the flexibility of the corresponding dynamic modules and found that different dynamic modules exhibit different amounts of flexibility and contribute to different cognitive measures. Finally, we applied DDM to a schizophrenia resting-state fMRI dataset and found that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia. Overall, this work provides a quantitative framework that characterizes temporal variation in the topology of dynamic functional connectivity.

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder.

Resting-state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently-proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment-to-moment-activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting-state functional magnetic resonance imaging (rs-fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high-amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.

Linked Social-Communication Dimensions and Connectivity in Functional Brain Networks in Autism Spectrum Disorder.

Much recent attention has been directed toward elucidating the structure of social interaction-communication dimensions and whether and how these symptom dimensions coalesce with each other in individuals with autism spectrum disorder (ASD). However, the underlying neurobiological basis of these symptom dimensions is unknown, especially the association of social interaction and communication dimensions with brain networks. Here, we proposed a method of whole-brain network-based regression to identify the functional networks linked to these symptom dimensions in a large sample of children with ASD. Connectome-based predictive modeling (CPM) was established to explore neurobiological evidence that supports the merging of communication and social interaction deficits into one symptom dimension (social/communication deficits). Results showed that the default mode network plays a core role in communication and social interaction dimensions. A primary sensory perceptual network mainly contributed to communication deficits, and high-level cognitive networks mainly contributed to social interaction deficits. CPM revealed that the functional networks associated with these symptom dimensions can predict the merged dimension of social/communication deficits. These findings delineate a link between brain functional networks and symptom dimensions for social interaction and communication and further provide neurobiological evidence supporting the merging of communication and social interaction deficits into one symptom dimension.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain.

Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5-18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure-function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.

Altered inter- and intrahemispheric functional connectivity dynamics in autistic children.

Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra- and interhemispheric functional connectivity patterns remain unknown in ASD. Resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7-12 years) available from the Autism Brain Imaging Data Exchange database. Whole-brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding-window analysis was utilized to capture the intra- and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra- and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra- and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children. These findings demonstrate altered temporal dynamics of the intra- and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD.

Impaired interactions among white-matter functional networks in antipsychotic-naive first-episode schizophrenia.

Schizophrenia has been conceptualized as a disorder arising from structurally pathological alterations to white-matter fibers in the brain. However, few studies have focused on white-matter functional changes in schizophrenia. Considering that converging evidence suggests that white-matter resting state functional MRI (rsfMRI) signals can effectively depict neuronal activity and psychopathological status, this study examined white-matter network-level interactions in antipsychotic-naive first-episode schizophrenia (FES) to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia. We recruited 42 FES patients (FESs) and 38 healthy controls (HCs), all of whom underwent rsfMRI. We identified 11 white-matter functional networks, which could be further classified into deep, middle, and superficial layers of networks. We then examined network-level interactions among these 11 white-matter functional networks using coefficient Granger causality analysis. We employed group comparisons on the influences among 11 networks using network-based statistic. Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in FESs compared with HCs. Additionally, an extra failure of suppression within superficial networks (including the frontoparietal network, temporofrontal network, and the orbitofrontal network) was observed in FESs. We additionally recruited an independent cohort (13 FESs and 13 HCs) from another center to examine the replicability of our findings across centers. Similar replication results further verified the white-matter functional network interaction model of schizophrenia. The novel findings of impaired interactions among white-matter functional networks in schizophrenia indicate that the pathophysiology of schizophrenia may also lie in white-matter functional abnormalities.

Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain.

BACKGROUND: Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS: We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS: Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS: Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder.

BACKGROUND: Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS: We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS: Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS: Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy.

Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α4ß2) profiles, but a distinct association was seen in the muscarinic Ach receptor (M1). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

Association among internalizing problems, white matter integrity, and social difficulties in children with autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by social difficulties and often accompanied by internalizing and externalizing problems, which are frequently overlooked. Here, we examined and compared fractional anisotropy (FA) between 79 children with ASD (aged 4-7.8 years) and 70 age-, gender-, and handedness- matched typically developing controls (TDCs, aged 3-7.2 years). We aimed to explore the relationship among social difficulties, internalizing and externalizing problems, and brain structural foundation (characterized by white matter integrity). Compared with the TDCs, the children with ASD exhibited more severe internalizing and externalizing problems, which were positively correlated with social difficulties. Reduced FA values were observed in specific white matter tracts that integrate a fronto-temporal-occipital circuit. In particular, the FA values within this circuit were negatively correlated with internalizing problems and SRS-TOTAL scores. Mediation analysis revealed that internalizing problems mediated the relationship between the FA values in the left middle longitudinal fasciculus (L-MdLF) and corpus callosum forceps major (CCM) and social difficulties in children with ASD. These findings contribute to our understanding of social difficulties, internalizing and externalizing problems, and white matter integrity in children with ASD and highlight internalizing problems as a mediator between social difficulties and white matter integrity.

Case report: Curing a rare, unstable hemoglobin variant Hb Bristol-Alesha using haploidentical hematopoietic stem cell transplantation.

Unstable hemoglobinopathies are a rare, heterogeneous group of diseases that disrupt the stability of hemoglobin (Hb), leading to chronic hemolysis and anemia. Patients with severe phenotypes often require regular blood transfusions and iron chelation therapy. Although rare, studies have reported that hematopoietic stem cell transplantation (HSCT) seems to be an available curative approach in transfusion-dependent patients with unstable hemoglobinopathies. Here, we describe successful haploidentical HSCT for the treatment of an unstable Hb variant, Hb Bristol-Alesha, in a 6-year-old boy with severe anemia since early childhood. Two years after transplantation, he had a nearly normal hemoglobin level without evidence of hemolysis. DNA analysis showed complete chimerism of the donor cell origin, confirming full engraftment with normal erythropoiesis.

Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain.

OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ).

Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization.

Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles.

Predictive Biomarkers for Antipsychotic Treatment Response in Early Phase of Schizophrenia: Multi-Omic Measures Linking Subcortical Covariant Network, Transcriptomic Signatures, and Peripheral Epigenetics.

Background: Volumetric alterations of subcortical structures as predictors of antipsychotic treatment response have been previously corroborated, but less is known about whether their morphological covariance relates to treatment outcome and is driven by gene expression and epigenetic modifications. Methods: Subcortical volumetric covariance was analyzed by using baseline T1-weighted magnetic resonance imaging (MRI) in 38 healthy controls and 38 drug-naïve first-episode schizophrenia patients. Patients were treated with 8-week risperidone monotherapy and divided into responder and non-responder groups according to the Remission in Schizophrenia Working Group (RSWG). We utilized partial least squares (PLS) regression to examine the spatial associations between gene expression of subcortical structures from a publicly available transcriptomic dataset and between-group variances of structural covariance. The peripheral DNA methylation (DNAm) status of a gene of interest (GOI), overlapping between genes detected in the PLS and 108 schizophrenia candidate gene loci previously reported, was examined in parallel with MRI scanning. Results: In the psychotic symptom dimension, non-responders had a higher baseline structural covariance in the putamen-hippocampus-pallidum-accumbens pathway compared with responders. For disorganized symptoms, significant differences in baseline structural covariant connections were found in the putamen-hippocampus-pallidum-thalamus circuit between the two subgroups. The imaging variances related to psychotic symptom response were spatially related to the expression of genes enriched in neurobiological processes and dopaminergic pathways. The DNAm of GOI demonstrated significant associations with patients' improvement of psychotic symptoms. Conclusion: Baseline subcortical structural covariance and peripheral DNAm may relate to antipsychotic treatment response. Phenotypic variations in subcortical connectome related to psychotic symptom response may be transcriptomically and epigenetically underlaid. This study defines a roadmap for future studies investigating multimodal imaging epigenetic biomarkers for treatment response in schizophrenia.

Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear. METHODS: T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes. RESULTS: Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes. CONCLUSIONS: Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.