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OBJECTIVE: Utilizing national longitudinal data, this study examines how polygenic depression risk and childhood abuse interactively influence the life-course development of depressive conditions from middle to late adulthood. METHOD: Data from 7512 participants (4323 females and 3189 males) of European ancestry aged 51-90, retrieved from the U.S. Health and Retirement Study (1992-2020), were analyzed. Childhood physical abuse and polygenic depression score were the primary predictors. Depressive symptoms were assessed using the Center for Epidemiologic Studies-Depression (CESD) scale, and clinical depression risk was a binary indicator. Growth-curve linear mixed and logit mixed-effects models were conducted for analysis. RESULTS: Increasing polygenic depression scores were associated with elevated CES-D levels and potential risks of clinical depression. Males experienced more detrimental effects of childhood abuse on depression development from ages 51 to 90 years. In contract, non-maltreated females generally exhibited higher depressive symptoms and clinical depression risk than males. A significant interactive effect was found between polygenic depression risk and childhood abuse among males. Higher depression levels and clinical risk were observed with increasing polygenic depression score among maltreated males, surpassing those of females with standardized polygenic score ≥0 from age 51 to 90 years. CONCLUSIONS: The interaction between childhood abuse and genetic factors significantly shaped lifelong depression trajectories in males, while the negative impact of abusive parenting remained constant regardless of polygenic depression risk among females. Individualized prevention and intervention strategies could be crucial in mitigating lifelong depression development, especially for high-genetic-risk males with a history of childhood physical abuse.
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Depresión , Interacción Gen-Ambiente , Humanos , Masculino , Femenino , Anciano , Persona de Mediana Edad , Estudios Longitudinales , Depresión/epidemiología , Depresión/genética , Estados Unidos/epidemiología , Anciano de 80 o más Años , Herencia Multifactorial , Factores de Riesgo , Maltrato a los Niños/psicología , Maltrato a los Niños/estadística & datos numéricos , Adultos Sobrevivientes del Maltrato a los Niños/psicologíaRESUMEN
Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age±SD=39.02±11.82; 105 males) and healthy controls (HC; n=143, mean age±SD=37.07±10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.
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Imagen de Difusión Tensora , Esquizofrenia , Sustancia Blanca , Humanos , Masculino , Esquizofrenia/diagnóstico por imagen , Esquizofrenia/patología , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Femenino , Adulto , Persona de Mediana Edad , Índice de Severidad de la Enfermedad , Disfunción Cognitiva/diagnóstico por imagen , Disfunción Cognitiva/patología , Disfunción Cognitiva/psicología , Disfunción Cognitiva/fisiopatologíaRESUMEN
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.
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Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.
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Esquizofrenia , Humanos , Esquizofrenia/diagnóstico por imagen , Esquizofrenia/genética , Esquizofrenia/complicaciones , Estudios Transversales , Encéfalo , Corteza Cerebral , Lóbulo FrontalRESUMEN
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
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People with schizophrenia and their high-risk, first-degree relatives report widespread episodic memory impairments that are purportedly due, at least in part, to failures of mnemonic discrimination. Here, we examined the status of mnemonic discrimination in 36 children and adolescents (aged 11-17 years) with and without familial risk for schizophrenia by employing an object-based recognition task called the Mnemonic Similarity Task (MST). The MST assesses the ability to discriminate between studied images and unstudied images that are either perceptually similar to studied images or completely novel. We compared 16 high-risk, unaffected first-degree relatives of people with schizophrenia, bipolar disorder, and/or schizoaffective disorder to 20 low-risk, control participants. High-risk participants showed worse mnemonic discrimination than low-risk participants, with no difference in recognition memory or perceptual discrimination. Our findings demonstrate that mnemonic discrimination deficits previously observed in people with schizophrenia are also present in their young, high-risk, first-degree relatives.
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Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.
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Esquizofrenia , Humanos , Esquizofrenia/diagnóstico , ElectroencefalografíaRESUMEN
Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.
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Esquizofrenia , Humanos , Esquizofrenia/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Mapeo Encefálico/métodos , Encéfalo/diagnóstico por imagen , Cerebelo , BiomarcadoresRESUMEN
This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia.
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Esquizofrenia , Humanos , Esquizofrenia/diagnóstico por imagen , Anhedonia , Encéfalo/diagnóstico por imagen , Trastornos del Humor , MotivaciónRESUMEN
BACKGROUND: The auditory N100 is an event related potential (ERP) that is reduced in schizophrenia, but its status in individuals at clinical high risk for psychosis (CHR) and its ability to predict conversion to psychosis remains unclear. We examined whether N100 amplitudes are reduced in CHR subjects relative to healthy controls (HC), and this reduction predicts conversion to psychosis in CHR. METHODS: Subjects included CHR individuals (n = 552) and demographically similar HC subjects (n = 236) from the North American Prodrome Longitudinal Study. Follow-up assessments identified CHR individuals who converted to psychosis (CHRC; n = 73) and those who did not (CHR-NC; n = 225) over 24 months. Electroencephalography data were collected during an auditory oddball task containing Standard, Novel, and Target stimuli. N100 peak amplitudes following each stimulus were measured at electrodes Cz and Fz. RESULTS: The CHR subjects had smaller N100 absolute amplitudes than HC subjects at Fz (F(1,786) = 4.00, p 0.046). A model comparing three groups (CHRC, CHR-NC, HC) was significant for Group at the Cz electrode (F(2,531) = 3.58, p = 0.029). Both Standard (p = 0.019) and Novel (p = 0.017) stimuli showed N100 absolute amplitude reductions in CHR-C relative to HC. A smaller N100 amplitude at Cz predicted conversion to psychosis in the CHR cohort (Standard: p = 0.009; Novel: p = 0.001) and predicted shorter time to conversion (Standard: p = 0.013; Novel: p = 0.001). CONCLUSION: N100 amplitudes are reduced in CHR individuals which precedes the onset of psychosis. N100 deficits in CHR individuals predict a greater likelihood of conversion to psychosis. Our results highlight N100's utility as a biomarker of psychosis risk.
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Trastornos Psicóticos , Esquizofrenia , Humanos , Estudios Longitudinales , Potenciales Evocados , América del Norte , Síntomas ProdrómicosRESUMEN
Stress is a risk factor in the development and maintenance of psychopathology, particularly anxiety. Despite theory suggesting differences in stress responsivity may explain heterogeneity in anxiety, findings remain contradictory. This may be due to failure to account for individuals' neurobiological states and outdated methodologic analyses which confound conceptually and biologically distinct stress response pathways. In 145 adolescents, this study examined whether individual differences in neural activation underlying motivational states, indexed by resting frontal alpha asymmetry (FAA) before and after the Trier Social Stress Test (TSST), moderate the relationship between stress responsivity (measured by cortisol) and anxiety. Adolescents with rightward FAA activation (indexed by changes in resting FAA pre-to-post TSST) and high trait anxiety showed blunted cortisol reactivities while those with leftward FAA activation and high state anxiety showed prolonged cortisol recoveries. Our work reveals individual differences in vulnerability to psychosocial stressors and is the first study to show that FAA activation moderates the relationships between anxiety and distinct phases of the stress response in adolescents.
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Hidrocortisona , Individualidad , Adolescente , Ansiedad/metabolismo , Humanos , Hidrocortisona/metabolismo , Sistema Hipotálamo-Hipofisario/metabolismo , Sistema Hipófiso-Suprarrenal/metabolismo , Saliva/metabolismo , Estrés Psicológico/psicologíaRESUMEN
Importance: Although clinical criteria for identifying youth at risk for psychosis have been validated, they are not sufficiently accurate for predicting outcomes to inform major treatment decisions. The identification of biomarkers may improve outcome prediction among individuals at clinical high risk for psychosis (CHR-P). Objective: To examine whether mismatch negativity (MMN) event-related potential amplitude, which is deficient in schizophrenia, is reduced in young people with the CHR-P syndrome and associated with outcomes, accounting for effects of antipsychotic medication use. Design, Setting, and Participants: MMN data were collected as part of the multisite case-control North American Prodrome Longitudinal Study (NAPLS-2) from 8 university-based outpatient research programs. Baseline MMN data were collected from June 2009 through April 2013. Clinical outcomes were assessed throughout 24 months. Participants were individuals with the CHR-P syndrome and healthy controls with MMN data. Participants with the CHR-P syndrome who developed psychosis (ie, converters) were compared with those who did not develop psychosis (ie, nonconverters) who were followed up for 24 months. Analysis took place between December 2019 and December 2021. Main Outcomes and Measures: Electroencephalography was recorded during a passive auditory oddball paradigm. MMN elicited by duration-, pitch-, and duration + pitch double-deviant tones was measured. Results: The CHR-P group (n = 580; mean [SD] age, 19.24 [4.39] years) included 247 female individuals (42.6%) and the healthy control group (n = 241; mean age, 20.33 [4.74] years) included 114 female individuals (47.3%). In the CHR-P group, 450 (77.6%) were not taking antipsychotic medication at baseline. Baseline MMN amplitudes, irrespective of deviant type, were deficient in future CHR-P converters to psychosis (n = 77, unmedicated n = 54) compared with nonconverters (n = 238, unmedicated n = 190) in both the full sample (d = 0.27) and the unmedicated subsample (d = 0.33). In the full sample, baseline medication status interacted with group and deviant type indicating that double-deviant MMN, compared with single deviants, was reduced in unmedicated converters compared with nonconverters (d = 0.43). Further, within the unmedicated subsample, deficits in double-deviant MMN were most strongly associated with earlier conversion to psychosis (hazard ratio, 1.40 [95% CI, 1.03-1.90]; P = .03], which persisted over and above positive symptom severity. Conclusions and Relevance: This study found that MMN amplitude deficits were sensitive to future psychosis conversion among individuals at risk of CHR-P, particularly those not taking antipsychotic medication at baseline, although associations were modest. While MMN shows limited promise as a biomarker of psychosis onset on its own, it may contribute novel risk information to multivariate prediction algorithms and serve as a translational neurophysiological target for novel treatment development in a subgroup of at-risk individuals.
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Antipsicóticos , Trastornos Psicóticos , Esquizofrenia , Estimulación Acústica , Adolescente , Adulto , Biomarcadores , Electroencefalografía , Potenciales Evocados Auditivos/fisiología , Femenino , Humanos , Estudios Longitudinales , Trastornos Psicóticos/diagnóstico , Trastornos Psicóticos/tratamiento farmacológico , Esquizofrenia/diagnóstico , Adulto JovenRESUMEN
We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.
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In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.
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Esquizofrenia , Corteza Visual , Encéfalo/diagnóstico por imagen , Mapeo Encefálico , Humanos , Imagen por Resonancia Magnética/métodos , Descanso , Esquizofrenia/diagnóstico por imagen , Corteza Visual/diagnóstico por imagenRESUMEN
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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Trastorno del Espectro Autista , Esquizofrenia , Trastorno del Espectro Autista/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Mapeo Encefálico/métodos , Humanos , Imagen por Resonancia Magnética/métodos , Imagen MultimodalRESUMEN
Dysregulations in autonomic and endocrine stress responses are linked to the emergence of psychopathology in adolescence. However, most studies fail to consider the interplay between these systems giving rise to conflicting findings and a gap in understanding adolescent stress response regulation. A multisystem framework-investigation of parasympathetic (PNS), sympathetic (SNS), and hypothalamic pituitary adrenal (HPA) axis components and their coordination-is necessary to understand individual differences in stress response coordination which contribute to stress vulnerabilities. As the first investigation to comprehensively evaluate these three systems in adolescence, the current study employed the Trier Social Stress Test in 72 typically developing adolescents (mean age = 13) to address how PNS, SNS, and HPA stress responses are coordinated in adolescence. Hypotheses tested key predictions of the Adaptive Calibration Model (ACM) of stress response coordination. PNS and SNS responses were assessed via heart rate variability (HRV) and salivary alpha amylase (sAA) respectively. HPA responses were indexed by salivary cortisol. Analyses utilized piecewise growth curve modeling to investigate these aims. Supporting the ACM theory, there was significant hierarchical coordination between the systems such that those with low HRV had higher sAA and cortisol reactivity and those with high HRV had low-to-moderate sAA and cortisol responsivity. Our novel results reveal the necessity of studying multisystem dynamics in an integrative fashion to uncover the true mechanisms of stress response and regulation during development. Additionally, our findings support the existence of characteristic stress response profiles as predicted by the ACM model.
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Hidrocortisona , alfa-Amilasas Salivales , Adolescente , Humanos , Sistema Hipotálamo-Hipofisario/metabolismo , Sistema Hipófiso-Suprarrenal/metabolismo , Pruebas Psicológicas , Saliva/metabolismo , alfa-Amilasas Salivales/metabolismo , Estrés PsicológicoRESUMEN
BACKGROUND: Exposure to both chronic and acute stressors can disrupt functional connectivity (FC) of the default mode network (DMN), salience network (SN), and central executive network (CEN), increasing risk for negative health outcomes. During adolescence, these stress-sensitive triple networks undergo critical neuromaturation that is altered by chronic exposure to general forms of trauma or victimization. However, no work has directly examined how acute stress affects triple network FC in adolescents or whether polyvictimization-exposure to multiple categories/subtypes of victimization-influences adolescent triple network neural acute stress response. METHODS: This functional magnetic resonance imaging study examined seed-to-voxel FC of the DMN, SN, and CEN during the Montreal Imaging Stress Task. Complete data from 73 participants aged 9 to 16 years (31 female) are reported. RESULTS: During acute stress, FC was increased between DMN and CEN regions and decreased between the SN and the DMN and CEN. Greater polyvictimization was associated with reduced FC during acute stress exposure between the DMN seed and a cluster containing the left insula of the SN. CONCLUSIONS: These results indicate that acute stress exposure alters FC between the DMN, SN, and CEN in adolescents. In addition, FC changes during stress between the DMN and SN are further moderated by polyvictimization exposure.