Dataset Information

Brain Biomarkers of Vulnerability and Progression to Psychosis.

ABSTRACT: Identifying predictors and elucidating the fundamental mechanisms underlying onset of psychosis are critical for the development of targeted preemptive interventions. This article presents a selective review of findings on risk prediction algorithms and potential mechanisms of onset in youth at clinical high-risk for psychosis, focusing principally on recent findings of the North American Prodrome Longitudinal Study (NAPLS). Multivariate models incorporating risk factors from clinical, demographic, neurocognitive, and psychosocial assessments achieve high levels of predictive accuracy when applied to individuals who meet criteria for a prodromal risk syndrome. An individualized risk calculator is available to scale the risk for newly ascertained cases, which could aid in clinical decision making. At risk individuals who convert to psychosis show elevated levels of proinflammatory cytokines, as well as disrupted resting state thalamo-cortical functional connectivity at baseline, compared with those who do not. Further, converters show a steeper rate of gray matter reduction, most prominent in prefrontal cortex, that in turn is predicted by higher levels of inflammatory markers at baseline. Microglia, resident immune cells in the brain, have recently been discovered to influence synaptic plasticity in health and impair plasticity in disease. Processes that modulate microglial activation may represent convergent mechanisms that influence brain dysconnectivity and risk for onset of psychosis and thus may be targetable in developing and testing preventive interventions.

SUBMITTER: Cannon TD

PROVIDER: S-EPMC4960430 | biostudies-other | 2016 Jul

REPOSITORIES: biostudies-other

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Brain Biomarkers of Vulnerability and Progression to Psychosis.

Cannon Tyrone D TD

Schizophrenia bulletin 20151209

Identifying predictors and elucidating the fundamental mechanisms underlying onset of psychosis are critical for the development of targeted preemptive interventions. This article presents a selective review of findings on risk prediction algorithms and potential mechanisms of onset in youth at clinical high-risk for psychosis, focusing principally on recent findings of the North American Prodrome Longitudinal Study (NAPLS). Multivariate models incorporating risk factors from clinical, demograph ...[more]

PMID: 26656880

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Project description:Clinical phenomenology remains the primary means for classifying psychoses despite considerable evidence that this method incompletely captures biologically meaningful differentiations. Rather than relying on clinical diagnoses as the gold standard, this project drew on neurobiological heterogeneity among psychosis cases to delineate subgroups independent of their phenomenological manifestations.A large biomarker panel (neuropsychological, stop signal, saccadic control, and auditory stimulation paradigms) characterizing diverse aspects of brain function was collected on individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis (N=711), their first-degree relatives (N=883), and demographically comparable healthy subjects (N=278). Biomarker variance across paradigms was exploited to create nine integrated variables that were used to capture neurobiological variance among the psychosis cases. Data on external validating measures (social functioning, structural magnetic resonance imaging, family biomarkers, and clinical information) were collected.Multivariate taxometric analyses identified three neurobiologically distinct psychosis biotypes that did not respect clinical diagnosis boundaries. The same analysis procedure using clinical DSM diagnoses as the criteria was best described by a single severity continuum (schizophrenia worse than schizoaffective disorder worse than bipolar psychosis); this was not the case for biotypes. The external validating measures supported the distinctiveness of these subgroups compared with clinical diagnosis, highlighting a possible advantage of neurobiological versus clinical categorization schemes for differentiating psychotic disorders.These data illustrate how multiple pathways may lead to clinically similar psychosis manifestations, and they provide explanations for the marked heterogeneity observed across laboratories on the same biomarker variables when DSM diagnoses are used as the gold standard.

Project description:Brain atrophy has been reported in the early stages of Parkinson's disease, but there have been few longitudinal studies. How intrinsic properties of the brain, such as anatomical connectivity, local cell-type distribution and gene expression combine to determine the pattern of disease progression also remains unknown. One hypothesis proposes that the disease stems from prion-like propagation of misfolded alpha-synuclein via the connectome that might cause varying degrees of tissue damage based on local properties. Here, we used MRI data from the Parkinson Progression Markers Initiative to map the progression of brain atrophy over 1, 2 and 4 years compared with baseline. We derived atrophy maps for four time points using deformation-based morphometry applied to T1-weighted MRI from 120 de novo Parkinson's disease patients, 74 of whom had imaging at all four time points (50 Men: 24 Women) and 157 healthy control participants (115 Men: 42 Women). In order to determine factors that may influence neurodegeneration, we related atrophy progression to brain structural and functional connectivity, cell-type expression and gene ontology enrichment analyses. After regressing out the expected age and sex effects associated with normal ageing, we found that atrophy significantly progressed over 2 and 4 years in the caudate, nucleus accumbens, hippocampus and posterior cortical regions. This progression was shaped by both structural and functional brain connectivity. Also, the progression of atrophy was more pronounced in regions with a higher expression of genes related to synapses and was inversely related to the prevalence of oligodendrocytes and endothelial cells. In sum, we demonstrate that the progression of atrophy in Parkinson's disease is in line with the prion-like propagation hypothesis of alpha-synuclein and provide evidence that synapses may be especially vulnerable to synucleinopathy. In addition to identifying vulnerable brain regions, this study reveals different factors that may be implicated in the neurotoxic mechanisms leading to progression in Parkinson's disease. All brain maps generated here are available on request.

Project description:BackgroundNeuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques.MethodsVoxel-based morphometry and partial least squares were employed to study system-level covariance patterns between whole-brain morphological data and processing speed, working memory, verbal learning/IQ, and executive functions in 40 ARMS subjects and 30 healthy controls (HC). The detected structure-cognition covariance patterns were tested for significance and reliability using non-parametric permutation and bootstrap resampling.ResultsWe identified ARMS-specific covariance patterns that described a generalized association of neurocognitive measures with predominantly prefronto-temporo-limbic and subcortical structures as well as the interconnecting white matter. In the conversion group, this generalized profile particularly involved working memory and verbal IQ and was positively correlated with limbic, insular and subcortical volumes as well as negatively related to prefrontal, temporal, parietal, and occipital cortices. Conversely, the neurocognitive profiles in the HC group were confined to working memory, learning and IQ, which were diffusely associated with cortical and subcortical brain regions.ConclusionsThese findings suggest that the ARMS and prodromal phase of psychosis are characterized by a convergent mapping from multi-domain neurocognitive measures to a set of prefronto-temporo-limbic and subcortical structures. Furthermore, a neuroanatomical separation between positive and negative brain-cognition correlations may not only point to a biological process determining the clinical risk for disease transition, but also to possible compensatory or dysmaturational neural processes.

Project description:BackgroundThe current definitions of psychotic illness lack biological validity, motivating alternative biomarker-driven disease entities. Building on experimental constructs-Biotypes-that were previously developed from cognitive and neurophysiologic measures, we contrast brain anatomy characteristics across Biotypes alongside conventional diagnoses, examining gray matter density (GMD) as an independent validator for the Biotypes.MethodsWhole brain GMD measures were examined in probands, their relatives, and healthy subjects organized by Biotype and then by DSM-IV-TR diagnosis (n = 1409) using voxel-based morphometry with subsequent subject-level regional characterization and distribution analyses.ResultsProbands grouped by Biotype versus healthy controls showed a stepwise pattern of GMD reductions as follows: Biotype1, extensive and diffusely distributed GMD loss, with the largest effects in frontal, anterior/middle cingulate cortex, and temporal regions; Biotype2, intermediate and more localized reductions, with the largest effects in insula and frontotemporal regions; and Biotype3, small reductions localized to anterior limbic regions. Relatives showed regionally distinct GMD reductions versus healthy controls, with primarily anterior (frontotemporal) effects in Biotype1; posterior (temporo-parieto-cerebellar) in Biotype2; and normal GMD in Biotype3. Schizophrenia and schizoaffective probands versus healthy controls showed overlapping GMD reductions, with the largest effects in frontotemporal and parietal regions; psychotic bipolar probands had small reductions, primarily in frontal regions. GMD changes in relatives followed regional patterns observed in probands, albeit less extensive. Biotypes showed stronger between-group separation based on GMD than the conventional diagnoses and were the strongest predictor of GMD change.ConclusionsGMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.

Project description:There is substantial interest in the potential for traumatic brain injury to result in progressive neurological deterioration. While blood biomarkers such as glial fibrillary acid protein (GFAP) and neurofilament light have been widely explored in characterizing acute traumatic brain injury (TBI), their use in the chronic phase is limited. Given increasing evidence that these proteins may be markers of ongoing neurodegeneration in a range of diseases, we examined their relationship to imaging changes and functional outcome in the months to years following TBI. Two-hundred and three patients were recruited in two separate cohorts; 6 months post-injury (n = 165); and >5 years post-injury (n = 38; 12 of whom also provided data ∼8 months post-TBI). Subjects underwent blood biomarker sampling (n = 199) and MRI (n = 172; including diffusion tensor imaging). Data from patient cohorts were compared to 59 healthy volunteers and 21 non-brain injury trauma controls. Mean diffusivity and fractional anisotropy were calculated in cortical grey matter, deep grey matter and whole brain white matter. Accelerated brain ageing was calculated at a whole brain level as the predicted age difference defined using T1-weighted images, and at a voxel-based level as the annualized Jacobian determinants in white matter and grey matter, referenced to a population of 652 healthy control subjects. Serum neurofilament light concentrations were elevated in the early chronic phase. While GFAP values were within the normal range at ∼8 months, many patients showed a secondary and temporally distinct elevations up to >5 years after injury. Biomarker elevation at 6 months was significantly related to metrics of microstructural injury on diffusion tensor imaging. Biomarker levels at ∼8 months predicted white matter volume loss at >5 years, and annualized brain volume loss between ∼8 months and 5 years. Patients who worsened functionally between ∼8 months and >5 years showed higher than predicted brain age and elevated neurofilament light levels. GFAP and neurofilament light levels can remain elevated months to years after TBI, and show distinct temporal profiles. These elevations correlate closely with microstructural injury in both grey and white matter on contemporaneous quantitative diffusion tensor imaging. Neurofilament light elevations at ∼8 months may predict ongoing white matter and brain volume loss over >5 years of follow-up. If confirmed, these findings suggest that blood biomarker levels at late time points could be used to identify TBI survivors who are at high risk of progressive neurological damage.

Dataset Information

Brain Biomarkers of Vulnerability and Progression to Psychosis.

Publications

Brain Biomarkers of Vulnerability and Progression to Psychosis.

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