Background

Impulsivity is associated with bipolar disorder as a clinical feature during and between manic episodes and is considered a potential endophenotype for the disorder. Schizophrenia and major depressive disorder share substantial genetic overlap with bipolar disorder, and these two disorders have also been associated with elevations in impulsivity. However, little is known about the degree of overlap among these disorders in discrete subfacets of impulsivity and whether any overlap is purely phenotypic or due to shared genetic diathesis.

Method

We focused on five subfacets of impulsivity: self-reported attentional, motor, and non-planning impulsivity, self-reported sensation seeking, and a behavioral measure of motor inhibition (stop signal reaction time; SSRT). We examined these facets within and across disorder proband and co-twin groups, modeled heritability, and tested for endophenotypic patterning in a sample of twin pairs recruited from the Swedish Twin Registry (N = 420).

Results

We found evidence of moderate to high levels of heritability for all five subfacets. All three proband groups and their unaffected co-twins showed elevations on attentional, motor, and non-planning impulsivity. Schizophrenia probands (but not their co-twins) showed significantly lower sensation seeking, and schizophrenia and bipolar disorder probands (but not in their co-twins) had significantly longer SSRTs, compared with healthy controls and the other groups.

Conclusions

Attentional, motor, and non-planning impulsivity emerged as potential shared endophenotypes for the three disorders, whereas sensation seeking and SSRT were associated with phenotypic affection but not genetic loading for these disorders.

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery.

Structural magnetic resonance imaging (MRI) studies have started to address the contributions of genetic and environmental factors to brain morphology in healthy individuals. These studies have largely been limited to assessing heritability for single and mostly gross anatomical structures. To avoid this limitation, we developed a method using high-dimensional 3D nonlinear registration to apply an elastic deformation vector field in the cortical parameter space and assess regional cortical gray matter density on the entire cortical surface in healthy monozygotic (MZ) and dizygotic (DZ) twins. Once aligned across subjects, surface maps of gray matter density were subjected to variance component analyses. By fitting structural equation models, we generated cortical surface maps to reflect the regional percent variance explained by genetic, shared and unique environmental factors. Initial analyses suggest that cortical structure in primary motor and sensory areas is highly heritable with variance components for additive genetic influences reaching as high as 70%. ©2004 IEEE.

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.

BACKGROUND: Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. METHODS: We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. RESULTS: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. CONCLUSIONS: Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

Huntington's disease (HD), a progressive neurodegenerative disease, is caused by an expanded CAG triplet repeat producing a mutant huntingtin protein (mHTT) with a polyglutamine-repeat expansion. Onset of symptoms in mutant huntingtin gene-carrying individuals remains unpredictable. We report that synthetic polyglutamine oligomers and cerebrospinal fluid (CSF) from BACHD transgenic rats and from human HD subjects can seed mutant huntingtin aggregation in a cell model and its cell lysate. Our studies demonstrate that seeding requires the mutant huntingtin template and may reflect an underlying prion-like protein propagation mechanism. Light and cryo-electron microscopy show that synthetic seeds nucleate and enhance mutant huntingtin aggregation. This seeding assay distinguishes HD subjects from healthy and non-HD dementia controls without overlap (blinded samples). Ultimately, this seeding property in HD patient CSF may form the basis of a molecular biomarker assay to monitor HD and evaluate therapies that target mHTT.

Huntington's disease (HD), a progressive neurodegenerative disease, is caused by an expanded CAG triplet repeat producing a mutant huntingtin protein (mHTT) with a polyglutamine-repeat expansion. Onset of symptoms in mutant huntingtin gene-carrying individuals remains unpredictable. We report that synthetic polyglutamine oligomers and cerebrospinal fluid (CSF) from BACHD transgenic rats and from human HD subjects can seed mutant huntingtin aggregation in a cell model and its cell lysate. Our studies demonstrate that seeding requires the mutant huntingtin template and may reflect an underlying prion-like protein propagation mechanism. Light and cryo-electron microscopy show that synthetic seeds nucleate and enhance mutant huntingtin aggregation. This seeding assay distinguishes HD subjects from healthy and non-HD dementia controls without overlap (blinded samples). Ultimately, this seeding property in HD patient CSF may form the basis of a molecular biomarker assay to monitor HD and evaluate therapies that target mHTT.

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Subcortical structures, which include the basal ganglia and parts of the limbic system, have key roles in learning, motor control and emotion, but also contribute to higher-order executive functions. Prior studies have reported volumetric alterations in subcortical regions in schizophrenia. Reported results have sometimes been heterogeneous, and few large-scale investigations have been conducted. Moreover, few large-scale studies have assessed asymmetries of subcortical volumes in schizophrenia. Here, as a work completely independent of a study performed by the ENIGMA consortium, we conducted a large-scale multisite study of subcortical volumetric differences between patients with schizophrenia and controls. We also explored the laterality of subcortical regions to identify characteristic similarities and differences between them. T1-weighted images from 1680 healthy individuals and 884 patients with schizophrenia, obtained with 15 imaging protocols at 11 sites, were processed with FreeSurfer. Group differences were calculated for each protocol and meta-analyzed. Compared with controls, patients with schizophrenia demonstrated smaller bilateral hippocampus, amygdala, thalamus and accumbens volumes as well as intracranial volume, but larger bilateral caudate, putamen, pallidum and lateral ventricle volumes. We replicated the rank order of effect sizes for subcortical volumetric changes in schizophrenia reported by the ENIGMA consortium. Further, we revealed leftward asymmetry for thalamus, lateral ventricle, caudate and putamen volumes, and rightward asymmetry for amygdala and hippocampal volumes in both controls and patients with schizophrenia. Also, we demonstrated a schizophrenia-specific leftward asymmetry for pallidum volume. These findings suggest the possibility of aberrant laterality in neural pathways and connectivity patterns related to the pallidum in schizophrenia.