Brain structural and functional correlates of resilience to Bipolar Disorder

Background: Resilient adaptation can be construed in different ways, but as used here it refers to adaptive brain responses associated with avoidance of psychopathology despite expressed genetic predisposition to Bipolar Disorder (BD). Although family history of BD is associated with elevated risk of affective morbidity a significant proportion of first-degree relatives remain free of psychopathology. Examination of brain structure and function in these individuals may inform on adaptive responses that pre-empt disease expression. Methods: Data presented here are derived from the Vulnerability to Bipolar Disorders Study (VIBES) which includes BD patients, asymptomatic relatives and controls. Participants underwent extensive investigations including brain structural (sMRI) and functional magnetic resonance imaging (fMRI). We present results from sMRI voxel-based-morphometry and from conventional and connectivity analyses of fMRI data obtained during the Stroop Colour Word Test (SCWT), a task of cognitive control during conflict resolution. All analyses were implemented using Statistical Parametric Mapping software version 5 (SPM5). Resilience in relatives was operationalized as the lifetime absence of clinical-range symptoms. Results: Resilient relatives of BD patients expressed structural, functional, and connectivity changes reflecting the effect of genetic risk on the brain. These included increased insular volume, decreased activation within the posterior and inferior parietal regions involved in selective attention during the SCWT, and reduced fronto-insular and fronto-cingulate connectivity. Resilience was associated with increased cerebellar vermal volume and enhanced functional coupling between the dorsal and the ventral prefrontal cortex during the SCWT. Conclusions: Our findings suggests the presence of biological mechanisms associated with resilient adaptation of brain networks and pave the way for the identification of outcome-specific trajectories given a bipolar genotype

Cognitive Function in Early Onset Schizophrenia: A Selective Review

Schizophrenia is widely regarded as the clinical outcome of aberrant neurodevelopment caused by a combination of genetic and non-genetic factors. Early Onset Schizophrenia (EOS) manifests in childhood or adolescence and represents a more severe variant of the Adult Onset form of the disorder (AOS). EOS offers a unique opportunity of exploring the impact of disease related mechanisms on the developmental trajectory of cognitive function. The present review focused on the domains of general intellectual ability (IQ), attention, executive function and memory. Significant methodological variability was noted across the different studies that examined these aspects of cognition in EOS patients. Despite this, a consistent pattern emergent from the data suggesting that (a) EOS patients compared to healthy children and adolescents show impairments of medium to large effect size in IQ, attention, memory and executive function (b) despite increased clinical severity, the cognitive profile of EOS patients is comparable to that of AOS patients (c) healthy adolescents show age-related improvement in their ability to perform tests of attention, memory and executive function; this is not present in EOS patients thus resulting in increased age-related deviance in patients’ performance. This apparent decline is mostly attributable to patients’ failure to acquire new information and to use more sophisticated cognitive strategies

Quantitative evaluation of simulated functional brain networks in graph theoretical analysis.

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.This work was supported by the National Institute of Mental Health under Grant R01MH104284.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2016.08.05

Autism Spectrum Disorders and Schizophrenia: Meta-Analysis of the Neural Correlates of Social Cognition

CONTEXT: Impaired social cognition is a cardinal feature of Autism Spectrum Disorders (ASD) and Schizophrenia (SZ). However, the functional neuroanatomy of social cognition in either disorder remains unclear due to variability in primary literature. Additionally, it is not known whether deficits in ASD and SZ arise from similar or disease-specific disruption of the social cognition network. OBJECTIVE: To identify regions most robustly implicated in social cognition processing in SZ and ASD. DATA SOURCES: Systematic review of English language articles using MEDLINE (1995-2010) and reference lists. STUDY SELECTION: Studies were required to use fMRI to compare ASD or SZ subjects to a matched healthy control group, provide coordinates in standard stereotactic space, and employ standardized facial emotion recognition (FER) or theory of mind (TOM) paradigms. DATA EXTRACTION: Activation foci from studies meeting inclusion criteria (n = 33) were subjected to a quantitative voxel-based meta-analysis using activation likelihood estimation, and encompassed 146 subjects with ASD, 336 SZ patients and 492 healthy controls. RESULTS: Both SZ and ASD showed medial prefrontal hypoactivation, which was more pronounced in ASD, while ventrolateral prefrontal dysfunction was associated mostly with SZ. Amygdala hypoactivation was observed in SZ patients during FER and in ASD during more complex ToM tasks. Both disorders were associated with hypoactivation within the Superior Temporal Sulcus (STS) during ToM tasks, but activation in these regions was increased in ASD during affect processing. Disease-specific differences were noted in somatosensory engagement, which was increased in SZ and decreased in ASD. Reduced thalamic activation was uniquely seen in SZ. CONCLUSIONS: Reduced frontolimbic and STS engagement emerged as a shared feature of social cognition deficits in SZ and ASD. However, there were disease- and stimulus-specific differences. These findings may aid future studies on SZ and ASD and facilitate the formulation of new hypotheses regarding their pathophysiology

Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders

Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically-informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key grey matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson’s disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only grey matter landmarks. Finally we describe how this approach could be used in planning DBS treatment of psychiatric disorders

Linked patterns of biological and environmental covariation with brain structure in adolescence : a population-based longitudinal study

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all P-FDR <0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|rho| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|rho| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|rho| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.Peer reviewe

Abnormal auditory tonotopy in patients with schizophrenia

Auditory hallucinations are among the most prevalent and most distressing symptoms of schizophrenia. Despite significant progress, it is still unclear whether auditory hallucinations arise from abnormalities in primary sensory processing or whether they represent failures of higher-order functions. To address this knowledge gap, we capitalized on the increased spatial resolution afforded by ultra-high field imaging at 7 Tesla to investigate the tonotopic organization of the auditory cortex in patients with schizophrenia with a history of recurrent hallucinations. Tonotopy is a fundamental feature of the functional organization of the auditory cortex that is established very early in development and predates the onset of symptoms by decades. Compared to healthy participants, patients showed abnormally increased activation and altered tonotopic organization of the auditory cortex during a purely perceptual task, which involved passive listening to tones across a range of frequencies (88-8000 Hz). These findings suggest that the predisposition to auditory hallucinations is likely to be predicated on abnormalities in the functional organization of the auditory cortex and which may serve as a biomarker for the early identification of vulnerable individuals

Towards a clinical staging for bipolar disorder: defining patient subtypes based on functional outcome.

BACKGROUND: The functional outcome of Bipolar Disorder (BD) is highly variable. This variability has been attributed to multiple demographic, clinical and cognitive factors. The critical next step is to identify combinations of predictors that can be used to specify prognostic subtypes, thus providing a basis for a staging classification in BD. METHODS: Latent Class Analysis was applied to multiple predictors of functional outcome in a sample of 106 remitted adults with BD. RESULTS: We identified two subtypes of patients presenting "good" (n=50; 47.6%) and "poor" (n=56; 52.4%) outcome. Episode density, level of residual depressive symptoms, estimated verbal intelligence and inhibitory control emerged as the most significant predictors of subtype membership at the p<0.05 level. Their odds ratio (OR) and confidence interval (CI) with reference to the "good" outcome group were: episode density (OR=4.622, CI 1.592-13.418), level of residual depressive symptoms (OR=1.543, CI 1.210-1.969), estimated verbal intelligence (OR=0.969; CI 0.945-0.995), and inhibitory control (OR=0.771, CI 0.656-0.907). Age, age of onset and duration of illness were comparable between prognostic groups. LIMITATIONS: The longitudinal stability or evolution of the subtypes was not tested. CONCLUSIONS: Our findings provide the first empirically derived staging classification of BD based on two underlying dimensions, one for illness severity and another for cognitive function. This approach can be further developed by expanding the dimensions included and testing the reproducibility and prospective prognostic value of the emerging classes. Developing a disease staging system for BD will allow individualised treatment planning for patients and selection of more homogeneous patient groups for research purposes

Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis

Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples.; Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals.; There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology.; Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data

A Computational Assessment of Target Engagement in the Treatment of Auditory Hallucinations with Transcranial Direct Current Stimulation

We use auditory verbal hallucinations (AVH) to illustrate the challenges in defining and assessing target engagement in the context of transcranial direct current stimulation (tDCS) for psychiatric disorders. We defined the target network as the cluster of regions of interest (ROIs) that are consistently implicated in AVH based on the conjunction of multimodal meta-analytic neuroimaging data. These were prescribed in the New York Head (a population derived model) and head models of four single individuals. We appraised two potential measures of target engagement, tDCS-induced peak electric field strength and tDCS-modulated volume defined as the percentage of the volume of the AVH network exposed to electric field magnitude stronger than the postulated threshold for neuronal excitability. We examined a left unilateral (LUL) montage targeting the prefrontal cortex (PFC) and temporoparietal junction (TPJ), a bilateral (BL) prefrontal montage, and a 2 × 1 montage targeting the left PFC and the TPJ bilaterally. Using computational modeling, we estimated the peak electric field strength and modulated volume induced by each montage for current amplitudes ranging 1–4 mA. We found that the LUL montage was inferior to both other montages in terms of peak electric field strength in right-sided AVH-ROIs. The BL montage was inferior to both other montages in terms of modulated volume of the left-sided AVH-ROIs. As the modulated volume is non-linear, its variability between montages reduced for current amplitudes above 3 mA. These findings illustrate how computational target engagement for tDCS can be tailored to specific networks and provide a principled approach for future study desig