Genetic determinants of target and novelty-related event-related potentials in the auditory oddball response

The Role of the LC-NE System in Attention: From Cells, to Systems, to Sensory-Motor Control

Attention control is a fundamental cognitive function that enables individuals to sustain focus, shift attention flexibly, and filter distractions in a goal-directed manner. The locus coeruleus-norepinephrine (LC-NE) system plays a pivotal role in this process by dynamically regulating arousal, prioritizing salient stimuli, and optimizing cognitive performance. This review synthesizes evidence from molecular, cellular, systems, cognitive neuroscience, and behavioral studies to elucidate the LC-NE system's role in attention control. We first examine the neurophysiological mechanisms of the LC, highlighting its distinct firing patterns-tonic and phasic activity-and their impact on attention. Next, we integrate findings from animal models, human neuroimaging, electrophysiology, and computational modeling, demonstrating how LC-NE activity influences sensory processing, cognitive flexibility, and executive function. We interpret these findings through the lens of three major theoretical frameworks: Adaptive Gain Theory (AGT), which describes how LC activity optimizes task engagement, the Network Reset Hypothesis (NRH), which describes how optimizes network connectivity, and the Glutamate Amplifies NE Effects (GANE) model, which explains how NE enhances neural selectivity and suppresses irrelevant signals. Collectively, the evidence underscores the LC-NE system's role in modulating the signal-to-noise ratio in cortical and subcortical circuits, thereby shaping attention and behavior. We conclude by discussing implications for individual differences, age-related cognitive decline, and emphasizing the need for interdisciplinary research that integrates emerging technologies to further unravel the complexities of LC function.

Modulating cortical excitability and cortical arousal by pupil self-regulation

The brain's arousal state (i.e., central arousal) is regulated by multiple neuromodulatory nuclei in the brainstem and significantly influences high-level cognitive processes. By exploiting the mechanistic connection between the locus coeruleus, a key regulator of central arousal, and pupil dynamics, we recently demonstrated that participants could gain volitional control over arousal-regulating centers including the locus coeruleus using a pupil-based biofeedback approach. Here, we test whether pupil-based biofeedback modulates electrophysiological markers of cortical excitability, cortical arousal, and P300 responses. Combining pupil-based biofeedback with single-pulse transcranial magnetic stimulation, electroencephalography, and an auditory oddball task reveals three main results: pupil self-regulation significantly modulates (i) cortical excitability, (ii) the electroencephalogram spectral slope, a marker of cortical arousal, and (iii) the P300 response to target tones, an event-related potential suggested to be linked to phasic locus coeruleus activity. Here, we show that pupil-based biofeedback modulates fundamental aspects of brain function. Whether this method can further be used to modulate these aspects in case of disturbances associated with neurological and psychiatric disorders needs to be investigated in future studies.

Similarities and differences between chronic primary pain and depression in brain activities: Evidence from resting-state microstates and auditory Oddball task

BACKGROUND

In 2019, the International Association for the Study of Pain introduced the concept of 'chronic primary pain (CPP)', characterized by persistent non-organic pain with emotional and functional abnormalities. Underdiagnosed and linked to depression, CPP has poorly understood neural characteristics. Electroencephalogram (EEG) microstates enable detailed examination of brain network dynamics at the millisecond level. Incorporating task-related EEG features offers a comprehensive neurophysiological signature of brain dysfunction, facilitating exploration of potential neural mechanisms.

METHODS

This study employed resting-state and task-related auditory Oddball EEG paradigm to evaluate 20 healthy controls, 20 patients with depression, and 20 patients with CPP. An 8-minute recording of resting-state EEG was conducted to identify four typical microstates (A-D). Additionally, power spectral density (PSD) features were examined during an auditory Oddball paradigm.

RESULTS

Both CPP and Major Depressive Disorder (MDD) patients exhibited reduced occurrence rate and transition probabilities of other microstates to microstate C during resting-state EEG. Furthermore, more pronounced increase in Gamma PSD was observed in the occipital region of CPP during the Oddball task. In CPP, both resting-state microstate C and task-related Gamma PSD correlated with pain and emotional indicators. Notably, microstate C occurrence positively correlated with occipital Gamma PSD in MDD.

CONCLUSION

Conclusively, both CPP and MDD display dynamic abnormalities within the salient network, closely associated with pain and depressive symptoms in CPP. Unlike MDD, CPPs' dynamic network changes appear unrelated to perceptual integration function, indicating differing microstate functional impacts. Combining resting-state microstates and Oddball tasks may offer a promising avenue for identifying potential biomarkers in objectively assessing chronic primary pain.

The Common Structure of the Major Psychoses: More Similarities Than Differences in the Network Structures of Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder

BACKGROUND AND HYPOTHESIS

There has been a century-long debate about whether the major psychoses (eg, bipolar disorder, schizophrenia, and schizoaffective disorder) are one disorder with various manifestations or different disease entities. Traditional approaches using dimensional models have not provided decisive findings. Here, we address this question by examining the network constellation of affective and psychotic syndromes.

DESIGN

Comparable symptom data of 1882 patients with psychotic bipolar disorder, schizoaffective disorders, and schizophrenia were extracted from three datasets: B-SNIP 1, B-SNIP2, and PARDIP. Twenty-six items from the Positive and Negative Syndrome Scale, YMRS, and the Montgomery-Asberg Depression Rating Scale were selected for the analysis using a principled approach to eliminate overlapping/redundant items. Gaussian graphical models were estimated and assessed for stability, and their communities were identified using bootstrapped exploratory graph analysis. The structures and global densities of the networks were compared with network comparison tests.

RESULTS

The network structures were highly similar (r >. 80) across diagnostic groups. For all diagnoses, manic symptoms were more connected with positive symptoms while depressive symptoms were more linked with negative symptoms. The depressive and negative symptoms were the strongest indicators of depressive and psychotic communities. Theoretically interesting variability in network edge weights between symptoms was found relating to thought disorder and pessimistic thinking.

CONCLUSIONS

The same broad structure of psychopathology underlies the symptom expressions of bipolar disorder, schizoaffective disorder, and schizophrenia. Future studies should build on the present finding by comparing specific inter-relations between symptoms in the different diagnostic groups using methods capable of detecting causality.

Differences in Children and Adolescents with Depression before and after a Remediation Program: An Event-Related Potential Study

Depression is clinically diagnosed when a defined constellation of symptoms manifests over a specific duration with notable severity. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Major Depressive Disorder (MDD) is characterized by the presence of five or more symptoms persisting for at least two weeks. As a profound mental health condition affecting millions globally, depression presents a considerable challenge for researchers and clinicians alike. In pediatric and adolescent populations, depression can precipitate adverse outcomes, including substance abuse, academic difficulties, risky sexual behaviors, physical health problems, impaired social relationships, and a markedly elevated risk of suicide-up to thirty times higher than the general population. This paper details a study that evaluated the efficacy of Cognitive Behavioral Therapy (CBT) alone vs. CBT combined with selective serotonin reuptake inhibitors (SSRIs) in a treatment program. The study cohort comprised sixteen (16) children and adolescents diagnosed with depression (eight males and eight females) and sixteen (16) typically developing peers (eight males and eight females) aged from 9 to 15 years (Mean age = 11.94, standard deviation = 2.02). Initial assessments employed Event-Related Potentials (ERPs), the Children's Depression Inventory (CDI), and reaction time measurements. The results reveal that participants with depression exhibit cognitive deficits in attention and memory, as evidenced by prolonged P300 latencies. Following intervention with either CBT alone or CBT combined with medication, the depressed participants demonstrated significant improvements, evidenced by lower CDI scores, reduced P300 latencies, and faster reaction times, both compared to their pre-treatment status and relative to the control group.

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

Cognitive domain-independent aberrant frontoparietal network strength in individuals with excessive smartphone use

Excessive smartphone use (ESU) may fulfill criteria for addictive behavior. In contrast to other related behavioral addictions, particularly Internet Gaming Disorder, little is known about the neural correlates underlying ESU. In this study, we used functional magnetic resonance imaging (fMRI) to acquire task data from three distinct behavioral paradigms, i.e. cue-reactivity, inhibition, and working memory, in individuals with psychometrically defined ESU (n = 19) compared to controls (n-ESU; n = 20). The Smartphone Addiction Inventory (SPAI) was used to quantify ESU-severity according to a novel five-factor model (SPAI-I). A multivariate data fusion approach, i.e. joint Independent Component Analysis (jICA) was employed to analyze fMRI-data derived from three separate experimental conditions, but analyzed jointly to detect converging and domain-independent neural signatures that differ between persons with vs. those without ESU. Across the three functional tasks, jICA identified a predominantly frontoparietal system that showed lower network strength in individuals with ESU compared to n-ESU (p < 0.05 FDR-corrected). Furthermore, significant associations between frontoparietal network strength and SPAI-I's dimensions "time spent" and "craving" were found. The data suggest a frontoparietal cognitive control network as cognitive domain-independent neural signature of excessive and potentially addictive smartphone use.

Aberrant intrinsic neural network strength in individuals with "smartphone addiction": An MRI data fusion study

BACKGROUND AND OBJECTIVES

Excessive smartphone use, also referred to as "smartphone addiction" (SPA), has increasingly attracted neuroscientific interest due to its similarities with other behavioral addictions, particularly internet gaming disorder. Little is known about the neural mechanisms underlying smartphone addiction. We explored interrelationships between brain structure and function to specify neurobiological correlates of SPA on a neural system level.

METHODS

Gray matter volume (GMV) and intrinsic neural activity (INA) were investigated in individuals with SPA (n = 20) and controls (n = 24), using multimodal magnetic resonance imaging and multivariate data fusion techniques, that is, parallel independent component analysis.

RESULTS

The joint analysis of both data modalities explored shared information between GMV and INA. In particular, two amplitudes of low frequency fluctuations-based independent neural systems significantly differed between individuals with SPA and controls. A medial/dorsolateral prefrontal system exhibited lower functional network strength in individuals with SPA versus controls, whereas the opposite pattern was detected in a parietal cortical/cerebellar system. Neural network strength was significantly related to duration of smartphone use and sleep difficulties.

DISCUSSION AND CONCLUSIONS

We show modality-specific associations of the brain's resting-state activity with distinct and shared SPA symptom dimensions. In particular, the data suggest contributions of aberrant prefrontal and parietal neural network strength as a possible signature of deficient executive control in SPA.

SCIENTIFIC SIGNIFICANCE

This study suggests distinct neural mechanisms underlying specific biological and behavioral dimensions of excessive smartphone use.

Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls: A narrative review

This narrative review brings together the findings regarding the differences in the auditory event-related potentials (ERPs) between patients with depressive disorder and non-depressed control subjects. These studies' results can inform us of the possible alterations in sensory-cognitive processing in depressive disorders and the potential of using these ERPs in clinical applications. Auditory P3, mismatch negativity (MMN) and loudness dependence of auditory evoked potentials (LDAEP) were the subjects of the investigation. A search in PubMed yielded 84 studies. The findings of the reviewed studies were not highly consistent, but some patterns could be identified. For auditory P3b, the common findings were attenuated amplitude and prolonged latency among depressed patients. Regarding auditory MMN, especially the amplitude of duration deviance MMN was commonly attenuated, and the amplitude of frequency deviance MMN was increased in depressed patients. In LDAEP studies, generally, no differences between depressed patients and non-depressed controls were reported, although some group differences concerning specific depression subtypes were found. This review posits that future research should investigate whether certain stimulus conditions are particularly efficient at separating depressed and non-depressed participant groups. Future studies should contrast responses in different subpopulations of depressed patients, as well as different clinical groups (e.g., depressive disorder and anxiety disorder patients), to investigate the specificity of the auditory ERP alterations for depressive disorders.

Multimodal MRI data fusion reveals distinct structural, functional and neurochemical correlates of heavy cannabis use

Heavy cannabis use (HCU) is frequently associated with a plethora of cognitive, psychopathological and sensorimotor phenomena. Although HCU is frequent, specific patterns of abnormal brain structure and function underlying HCU in individuals presenting without cannabis-use disorder or other current and life-time major mental disorders are unclear at present. This multimodal magnetic resonance imaging (MRI) study examined resting-state functional MRI (rs-fMRI) and structural MRI (sMRI) data from 24 persons with HCU and 16 controls. Parallel independent component analysis (p-ICA) was used to examine covarying components among grey matter volume (GMV) maps computed from sMRI and intrinsic neural activity (INA), as derived from amplitude of low-frequency fluctuations (ALFF) maps computed from rs-fMRI data. Further, we used JuSpace toolbox for cross-modal correlations between MRI-based modalities with nuclear imaging derived estimates, to examine specific neurotransmitter system changes underlying HCU. We identified two transmodal components, which significantly differed between the HCU and controls (GMV: p = 0.01, ALFF p = 0.03, respectively). The GMV component comprised predominantly cerebello-temporo-thalamic regions, whereas the INA component included fronto-parietal regions. Across HCU, loading parameters of both components were significantly associated with distinct HCU behavior. Finally, significant associations between GMV and the serotonergic system as well as between INA and the serotonergic, dopaminergic and μ-opioid receptor system were detected. This study provides novel multimodal neuromechanistic insights into HCU suggesting co-altered structure/function-interactions in neural systems subserving cognitive and sensorimotor functions.

Higher Cognitive Reserve Is Associated with Better Working Memory Performance and Working-Memory-Related P300 Modulation

This study aims to examine how two levels of cognitive reserve, as evidenced by reading syntactic skill, modify performance and neural activity in a two-load-level (high vs. low) working memory (WM) task. Two groups of participants with different reading skills, high and low, were obtained from clustering analysis. We collected the P300 event-related potential component during the performance of the WM Sternberg task. The high reading performance (HRP) group showed a higher percentage of correct answers than the low reading performance (LRP) group in the negative probes of the WM task, which were probe stimuli not included in the memory set presented immediately before. Both groups showed P300 amplitude modulations, that is, larger WM-related P300 amplitudes for low than for high WM loads. Following the behavioral results, the HRP group displayed smaller WM-related amplitude modulations than the LRP group in the negative probes. The findings together suggest that higher levels of reading skill are associated with improved neural efficiency, which reflects in a better working memory performance.

Inter- and intra-individual coupling between pupillary, electrophysiological, and behavioral responses in a visual oddball task

Although the P3b component of the event-related brain potential is one of the most widely studied components, its underlying generators are not currently well understood. Recent theories have suggested that the P3b is triggered by phasic activation of the locus-coeruleus norepinephrine (LC-NE) system, an important control center implicated in facilitating optimal task-relevant behavior. Previous research has reported strong correlations between pupil dilation and LC activity, suggesting that pupil diameter is a useful indicator for ongoing LC-NE activity. Given the strong relationship between LC activity and pupil dilation, if the P3b is driven by phasic LC activity, there should be a robust trial-to-trial relationship with the phasic pupillary dilation response (PDR). However, previous work examining relationships between concurrently recorded pupillary and P3b responses has not supported this. One possibility is that the relationship between the measures might be carried primarily by either inter-individual (i.e., between-participant) or intra-individual (i.e., within-participant) contributions to coupling, and prior work has not systematically delineated these relationships. Doing so in the current study, we do not find evidence for either inter-individual or intra-individual relationships between the PDR and P3b responses. However, baseline pupil dilation did predict the P3b. Interestingly, both the PDR and P3b independently predicted inter-individual and intra-individual variability in decision response time. Implications for the LC-P3b hypothesis are discussed.

Covariation Between Brain Function (MEG) and Structure (DTI) Differentiates Adolescents with Fetal Alcohol Spectrum Disorder from Typically Developing Controls

The behavioral, cognitive, and sensory difficulties experienced by individuals exposed to alcohol prenatally currently fail to provide early identification for fetal alcohol spectrum disorder (FASD). Attempting to advance this pursuit through a multivariate analysis, we collected magnetoencephalography (MEG) data during auditory, somatosensory, visual paradigms, DTI, and behavior in adolescents ages 12-21 years (FASD: N = 13; HC: N = 20). We assessed the relationship between brain function (MEG) and structure (fractional anisotropy (FA)) utilizing joint independent component analysis (jICA), and examined how this measure relates to behavior. We identified 5 components that reveal group differences in co-variation between MEG and FA. For example, component 5 (t = 3.162, p = 0.003, Hedges' g = 1.13) contained MEG activity corresponding to all three sensory modalities, most robustly in occipital lobes, and DTI-derived cerebellar FA, underlying the role of the cerebellum in sensory processing. Further, in HCs component 5's loading factor was positively correlated with verbal ability (r = 0.646, p = 0.002), indicating higher covariation was associated with better verbal performance. Interestingly, this relationship is lacking in FASD (r = 0.009, p = 0.979). Also, component 5 loading factor negatively correlated with impulsivity (r = -0.527, p = 0.002), indicating that stronger function-structure associations were associated with individuals with lower impulsivity. These findings suggest that multimodal integration of MEG and FA provides novel associations between structure and function that may help differentiate adolescents with FASD from HC.

A Neural Signature of Parkinsonism in Patients With Schizophrenia Spectrum Disorders: A Multimodal MRI Study Using Parallel ICA

Motor abnormalities in schizophrenia spectrum disorders (SSD) have increasingly attracted scientific interest in the past years. However, the neural mechanisms underlying parkinsonism in SSD are unclear. The present multimodal magnetic resonance imaging (MRI) study examined SSD patients with and without parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥4 (SAS group, n = 22) or <4 (non-SAS group, n = 22). Parallel independent component analysis (p-ICA) was used to examine the covarying components among gray matter volume maps computed from structural MRI (sMRI) and fractional amplitude of low-frequency fluctuations (fALFF) maps computed from resting-state functional MRI (rs-fMRI) patient data. We found a significant correlation (P = .020, false discovery rate [FDR] corrected) between an sMRI component and an rs-fMRI component, which also significantly differed between the SAS and non-SAS group (P = .042, z = -2.04). The rs-fMRI component comprised the cortical sensorimotor network, and the sMRI component included predominantly a frontothalamic/cerebellar network. Across the patient sample, correlations adjusted for the Positive and Negative Syndrome Scale (PANSS) total scores showed a significant relationship between tremor score and loadings of the cortical sensorimotor network, as well as between glabella-salivation score, frontothalamic/cerebellar and cortical sensorimotor network loadings. These data provide novel insights into neural mechanisms of parkinsonism in SSD. Aberrant bottom-up modulation of cortical motor regions may account for these specific motor symptoms, at least in patients with SSD.

Structure/function interrelationships in patients with schizophrenia who have persistent auditory verbal hallucinations: A multimodal MRI study using parallel ICA

There is accumulating neuroimaging evidence for both structural and functional abnormalities in schizophrenia patients with persistent auditory verbal hallucinations (AVH). So far, the direct interrelationships between altered structural and functional changes underlying AVH are unknown. Recently, it has become possible to reveal hidden patterns of neural dysfunction not sufficiently captured by separate analysis of these two modalities. A data-driven fusion method called parallel independent component analysis (p-ICA) is able to identify maximally independent components of each imaging modality as well as the link between them. In the present study, we utilized p-ICA to study covarying components among gray matter volume maps computed from structural MRI (sMRI) and fractional amplitude of low-frequency fluctuations (fALFF) maps computed from resting-state functional MRI (rs-fMRI) data of 15 schizophrenia patients with AVH, 16 non-hallucinating schizophrenia patients (nAVH), and 19 healthy controls (HC). We found a significant correlation (r = 0.548, n = 50, p < .001) between a sMRI component and a rs-fMRI component, which was significantly different between the AVH and non AVH group (nAVH). The rs-fMRI component comprised temporal cortex and cortical midline regions, the sMRI component included predominantly fronto-temporo-parietal regions. Distinct clinical features, as measured by the Psychotic Symptoms Rating Scale (PSYRATS), were associated with two different modality specific rs-fMRI components. There was a significant correlation between a predominantly parietal resting-state network and the physical dimension of PSYRATS and the posterior cingulate/temporal cortex network and the emotional dimension of PSYRATS. These data suggest AVH-specific interrelationships between intrinsic network activity and GMV, together with modality-specific associations with distinct symptom dimensions of AVH.

Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data

Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns, i.e., biomarkers. In this paper, EEG, fMRI, and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along the subject mode, and jointly analyzed using structure-revealing CMTF methods [also known as advanced CMTF (ACMTF)] focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role.

Noradrenergic and Cholinergic Modulation of Belief Updating

To make optimal predictions in a dynamic environment, the impact of new observations on existing beliefs-that is, the learning rate-should be guided by ongoing estimates of change and uncertainty. Theoretical work has proposed specific computational roles for various neuromodulatory systems in the control of learning rate, but empirical evidence is still sparse. The aim of the current research was to examine the role of the noradrenergic and cholinergic systems in learning rate regulation. First, we replicated our recent findings that the centroparietal P3 component of the EEG-an index of phasic catecholamine release in the cortex-predicts trial-to-trial variability in learning rate and mediates the effects of surprise and belief uncertainty on learning rate (Study 1, n = 17). Second, we found that pharmacological suppression of either norepinephrine or acetylcholine activity produced baseline-dependent effects on learning rate following nonobvious changes in an outcome-generating process (Study 1). Third, we identified two genes, coding for α2A receptor sensitivity (ADRA2A) and norepinephrine reuptake (NET), as promising targets for future research on the genetic basis of individual differences in learning rate (Study 2, n = 137). Our findings suggest a role for the noradrenergic and cholinergic systems in belief updating and underline the importance of studying interactions between different neuromodulatory systems.

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study

OBJECTIVE

The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored.

METHODS

We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools.

RESULTS

Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP.

CONCLUSIONS

Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis.

Cholinergic modulation of auditory P3 event-related potentials as indexed by CHRNA4 and CHRNA7 genotype variation in healthy volunteers

Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunction within the realm of attentional processing. Reduced P3a and P3b event-related potentials (ERPs), indexing involuntary and voluntary attentional processing respectively, have been consistently observed in SZ patients who also express prominent cholinergic deficiencies. The involvement of the brain's cholinergic system in attention has been examined for several decades; however, further inquiry is required to further comprehend how abnormalities in this system affect neighbouring neurotransmitter systems and contribute to neurocognitive deficits. The objective of this pilot study was to examine the moderating role of the CHRNA4 (rs1044396), CHRNA7 (rs3087454), and SLC5A7 (rs1013940) genes on ERP indices of attentional processing in healthy volunteers (N=99; Caucasians and non-Caucasians) stratified by genotype and assessed using the auditory P300 "oddball" paradigm. Results indicated significantly greater P3a and P3b-indexed attentional processing for CT (vs. CC) CHRNA4 carriers and greater P3b for AA (vs. CC) CHRNA7 carriers. SLC5A7 allelic variants did not show significant differences in P3a and P3b processing. These findings expand our knowledge on the moderating effect of cholinergic genes on attention and could help inform targeted drug developments aimed at restoring attention deficits in SZ patients.

The anatomical and functional relationship between the P3 and autonomic components of the orienting response

Many psychophysiologists have noted the striking similarities between the antecedent conditions for the P3 component of the event-related potential and the orienting response: both are typically elicited by salient, unexpected, novel, task-relevant, and other motivationally significant stimuli. Although the close coupling of the P3 and orienting response has been well documented, the neural basis and functional role of this relationship is still poorly understood. Here we propose that the simultaneous occurrence of the P3 and autonomic components of the orienting response reflects the co-activation of the locus coeruleus-norepinephrine system and the peripheral sympathetic nervous system by their common major afferent: the rostral ventrolateral medulla, a key sympathoexcitatory region. A comparison of the functional significance of the locus coeruleus-norepinephrine system and the peripheral sympathetic nervous system suggests that the P3 and orienting response reflect complementary cognitive and physical contributions to the mobilization for action following motivationally significant stimuli.

Modulation of ERP components by task instructions in a cued go/no-go task

The present study investigated how components of ERPs are modulated when participants optimize speed versus accuracy in a cued go/no-go task. Using a crossover design, 35 participants received instructions to complete the task prioritizing response speed in half of the task, and accurate responding in the other half of the task. Analysis was performed on the contingent negative variation (CNV), P3go, and P3no-go and the corresponding independent components (IC), as identified by group independent component analysis. After speed instructions, the IC CNV(late), P3go(anterior), P3no-go(early), and P3no-go(late) all had larger amplitudes than after accuracy instructions. Furthermore, both the IC P3go(posterior) and IC P3go(anterior) had shorter latencies after speed than after accuracy instructions. The results demonstrate that components derived from the CNV and P3 components are facilitated when participants optimize response speed. These findings indicate that these ERP components reflect executive processes enabling adjustment of behavior to changing demands.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Inefficient stimulus processing at encoding affects formation of high-order general representation: A study on cross-modal word-stem completion task

Priming is an implicit memory effect in which previous exposure to one stimulus influences the response to another stimulus. The main characteristic of priming is that it occurs without awareness. Priming takes place also when the physical attributes of previously studied and test stimuli do not match; in fact, it greatly refers to a general stimulus representation activated at encoding independently of the sensory modality engaged. Our aim was to evaluate whether, in a cross-modal word-stem completion task, negative priming scores could depend on inefficient word processing at study and therefore on an altered stimulus representation. Words were presented in the auditory modality, and word-stems to be completed in the visual modality. At study, we recorded auditory ERPs, and compared the P300 (attention/memory) and N400 (meaning processing) of individuals with positive and negative priming. Besides classical averaging-based ERPs analysis, we used an ICA-based method (ErpICASSO) to separate the potentials related to different processes contributing to ERPs. Classical analysis yielded significant difference between the two waves across the whole scalp. ErpICASSO allowed separating the novelty-related P3a and the top-down control-related P3b sub-components of P300. Specifically, in the component C3, the positive deflection identifiable as P3b, was significantly greater in the positive than in the negative priming group, while the late negative deflection corresponding to the parietal N400, was reduced in the positive priming group. In conclusion, inadequacy of specific processes at encoding, such as attention and/or meaning retrieval, could generate weak semantic representations, making words less accessible in subsequent implicit retrieval.

Multivariate genetic determinants of EEG oscillations in schizophrenia and psychotic bipolar disorder from the BSNIP study

Schizophrenia (SZ) and psychotic bipolar disorder (PBP) are disabling psychiatric illnesses with complex and unclear etiologies. Electroencephalogram (EEG) oscillatory abnormalities in SZ and PBP probands are heritable and expressed in their relatives, but the neurobiology and genetic factors mediating these abnormalities in the psychosis dimension of either disorder are less explored. We examined the polygenic architecture of eyes-open resting state EEG frequency activity (intrinsic frequency) from 64 channels in 105 SZ, 145 PBP probands and 56 healthy controls (HCs) from the multisite BSNIP (Bipolar-Schizophrenia Network on Intermediate Phenotypes) study. One million single-nucleotide polymorphisms (SNPs) were derived from DNA. We assessed eight data-driven EEG frequency activity derived from group-independent component analysis (ICA) in conjunction with a reduced subset of 10,422 SNPs through novel multivariate association using parallel ICA (para-ICA). Genes contributing to the association were examined collectively using pathway analysis tools. Para-ICA extracted five frequency and nine SNP components, of which theta and delta activities were significantly correlated with two different gene components, comprising genes participating extensively in brain development, neurogenesis and synaptogenesis. Delta and theta abnormality was present in both SZ and PBP, while theta differed between the two disorders. Theta abnormalities were also mediated by gene clusters involved in glutamic acid pathways, cadherin and synaptic contact-based cell adhesion processes. Our data suggest plausible multifactorial genetic networks, including novel and several previously identified (DISC1) candidate risk genes, mediating low frequency delta and theta abnormalities in psychoses. The gene clusters were enriched for biological properties affecting neural circuitry and involved in brain function and/or development.

Genetic Sources of Subcomponents of Event-Related Potential in the Dimension of Psychosis Analyzed From the B-SNIP Study

OBJECTIVE

Biological risk factors underlying psychosis are poorly understood. Biological underpinnings of the dimension of psychosis can be derived using genetic associations with intermediate phenotypes such as subcomponents of auditory event-related potentials (ERPs). Various ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder are heritable and are expressed in unaffected relatives, although studies investigating genetic contributions to ERP abnormalities are limited. The authors used a novel parallel independent component analysis (para-ICA) to determine which empirically derived gene clusters are associated with data-driven ERP subcomponents, assuming a complex etiology underlying psychosis.

METHOD

The authors examined the multivariate polygenic association of ERP subcomponents from 64-channel auditory oddball data in 144 individuals with schizophrenia, 210 psychotic bipolar disorder probands, and 95 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Data were reduced by principal components analysis to two target and one standard ERP waveforms. Multivariate association of compressed ERP waveforms with a set of 20,329 single-nucleotide polymorphisms (SNPs) (reduced from a 1-million-SNP array) was examined using para-ICA. Genes associated with SNPs were further examined using pathway analysis tools.

RESULTS

Para-ICA identified four ERP components that were significantly correlated with three genetic components. Enrichment analysis revealed complement immune response pathway and multiple processes that significantly mediate ERP abnormalities in psychosis, including synaptic cell adhesion, axon guidance, and neurogenesis.

CONCLUSIONS

This study identified three genetic components comprising multiple genes mediating ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder. The data suggest a possible polygenic structure comprising genes influencing key neurodevelopmental processes, neural circuitry, and brain function mediating biological pathways plausibly associated with psychosis.

Event-related potential and time-frequency endophenotypes for schizophrenia and psychotic bipolar disorder

BACKGROUND

The investigators compared event-related potential (ERP) amplitudes and event-related oscillations across a broad frequency range during an auditory oddball task using a comprehensive analysis approach to describe shared and unique neural auditory processing characteristics among healthy subjects (HP), schizophrenia probands (SZ) and their first-degree relatives, and bipolar disorder I with psychosis probands (BDP) and their first-degree relatives.

METHODS

This Bipolar-Schizophrenia Network on Intermediate Phenotypes sample consisted of clinically stable SZ (n = 229) and BDP (n = 188), HP (n = 284), first-degree relatives of schizophrenia probands (n = 264), and first-degree relatives of bipolar disorder I with psychosis probands (n = 239). They were administered an auditory oddball task in the electroencephalography environment. Principal components analysis derived data-driven frequency bands evoked power. Spatial principal components analysis reduced ERP and frequency data to component waveforms for each subject. Clusters of time bins with significant group differences on response magnitude were assessed for proband/relative differences from HP and familiality.

RESULTS

Nine variables survived a linear discriminant analysis between HP, SZ, and BDP. Of those, two showed evidence (deficit in relatives and familiality) as genetic risk markers more specific to SZ (N1, P3b), one was specific to BDP (P2) and one for psychosis in general (N2).

CONCLUSIONS

This study supports for both shared and unique deficits in early sensory and late cognitive processing across psychotic diagnostic groups. Additional ERP and time-frequency component alterations (frontal N2/P2, late high, early, mid, and low frequency) may provide insight into deficits in underlying neural architecture and potential protective/compensatory mechanisms in unaffected relatives.

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Genetic association analysis of ATP binding cassette protein family reveals a novel association of ABCB1 genetic variants with epilepsy risk, but not with drug-resistance

Epilepsy constitutes a heterogeneous group of disorders that is characterized by recurrent unprovoked seizures due to widely different etiologies. Multidrug resistance remains a major issue in clinical epileptology, where one third of patients with epilepsy continue to have seizures. Role of efflux transporters in multidrug resistant epilepsy has been attributed to drug-resistant epilepsy although, with discrepant observation in genetic studies. These discrepancies could be attributed to variety of factors such as variable definition of the anti-epileptic drug (AED)-resistance, variable epilepsy phenotypes and ethnicities among the studies. In the present study we inquired the role of multidrug transporters ABCB1 and ABCG2 variants in determining AED-resistance and susceptibility to epilepsy in three well-characterized cohorts comprising of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) (prototype for AED-resistant epilepsy); juvenile myoclonic epilepsy (JME) (prototype for AED-responsive epilepsy); and healthy non-epileptic controls, in 738 subjects of Malayalam speaking south Indian ancestry. ABCB1 and ABCG2 variants were not found to be associated with drug resistance when AED-resistant and AED-responsive cohorts were compared. However, a significant association was observed between ABCB1 (C3435T) rs1045642 and risk of having epilepsy (MTLE-HS and JME pooled cohort; genotypic p-value = 0.0002; allelic p-value = 0.004). This association was seen persistent with MTLE-HS (genotypic p-value = 0.0008; allelic p-value = 0.004) and also with JME (genotypic p-value = 0.01; allelic p-value = 0.05) cohort individually. In-silico functional prediction indicated that ABCB1 rs1045642 has a deleterious impact on protein coding function and in splicing regulation. We conclude that the ABCB1 and ABCG2 variants do not confer to AED-resistance in the study population. However, ABCB1 rs1045642 increases vulnerability to epilepsy with greater tendency for MTLE-HS in south Indian ancestry from Kerala.

Right hemisphere role in cognitive reserve

High levels of education, occupational complexity, and/or premorbid intelligence are associated with lower levels of cognitive impairment than would be expected from a given brain pathology. This has been observed across a range of conditions including Alzheimer's disease (Roe et al., 2010), stroke (Ojala-Oksala et al., 2012), traumatic brain injury (Kesler et al., 2003), and penetrating brain injury (Grafman, 1986). This cluster of factors, which seemingly protect the brain from expressing symptoms of damage, has been termed "cognitive reserve" (Stern, 2012). The current review considers one possible neural network, which may contribute to cognitive reserve. Based on the evidence that the neurotransmitter, noradrenaline mediates cognitive reserve's protective effects (Robertson, 2013) this review identifies the neurocognitive correlates of noradrenergic (NA) activity. These involve a set of inter-related cognitive processes (arousal, sustained attention, response to novelty, and awareness) with a strongly right hemisphere, fronto-parietal localization, along with working memory, which is also strongly modulated by NA. It is proposed that this set of processes is one plausible candidate for partially mediating the protective effects of cognitive reserve. In addition to its biological effects on brain structure and function, NA function may also facilitate networks for arousal, novelty, attention, awareness, and working memory, which collectively provide for a set of additional, cognitive, mechanisms that help the brain adapt to age-related changes and disease. It is hypothesized that to the extent that the lateral surface of the right prefrontal lobe and/or the right inferior parietal lobe maintain structural (white and gray matter) and functional integrity and connectivity, cognitive reserve should benefit and behavioral expression of pathologic damage should thus be mitigated.

Multifactorial determinants of target and novelty-evoked P300 amplitudes in children of addicted parents

Background

Although P300 amplitude reductions constitute a persistent finding in children of addicted parents, relatively little is known about the specificity of this finding. The major aim of this study was to investigate the association between parental rearing, adverse life events, stress-reactivity, substance use and psychopathology on the one hand, and P300 amplitude in response to both target and novel distracter stimuli on the other hand. Moreover, we assessed whether risk group status (i.e., having a parental history of Substance Use Disorders [SUD]) uniquely contributed to P300 amplitude variation above and beyond these other variables.

Methods

Event-related potentials were recorded in high-risk adolescents with a parental history of SUD (HR;n=80) and normal-risk controls (NR;n=100) while performing a visual Novelty Oddball paradigm. Stress-evoked cortisol levels were assessed and parenting, life adversities, substance use and psychopathology were examined by using self-reports.

Results

HR adolescents displayed smaller P300 amplitudes in response to novel- and to target stimuli than NR controls, while the latter only approached significance. Interestingly, the effect of having a parental history of SUD on target-P300 disappeared when all other variables were taken into account. Externalizing problem behavior was a powerful predictor of target-P300. In contrast, risk group status uniquely predicted novelty-P300 amplitude reductions above and beyond all other factors.

Conclusion

Overall, the present findings suggest that the P300 amplitude reduction to novel stimuli might be a more specific endophenotype for SUD than the target-P300 amplitude. This pattern of results underscores the importance of conducting multifactorial assessments when examining important cognitive processes in at-risk adolescents.

Using joint ICA to link function and structure using MEG and DTI in schizophrenia

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p<0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (neurophysiology) and structure (white matter integrity) and demonstrates that this multivariate, multimodal integration technique is sensitive to group differences in function and structure.

Reimagining psychoses: an agnostic approach to diagnosis

OBJECTIVES

Current approaches to defining and classifying psychotic disorders are compromised by substantive heterogeneity within, blurred boundaries between, as well as overlaps across the various disorders in outcome, treatment response, emerging evidence regarding pathophysiology and presumed etiology.

METHODS

We herein review the evolution, current status and the constraints posed by classic symptom-based diagnostic approaches. We compare the continuing constructs that underlie the current classification of psychoses, and contrast those to evolving new thinking in other areas of medicine.

RESULTS

An important limitation in current psychiatric nosology may stem from the fact that symptom-based diagnoses do not "carve nature at its joints"; while symptom-based classifications have improved our reliability, they may lack validity. Next steps in developing a more valid scientific nosology for psychoses include a) agnostic deconstruction of disease dimensions, identifying disease markers and endophenotypes; b) mapping such markers across translational domains from behaviors to molecules, c) reclustering cross-cutting bio-behavioral data using modern phenotypic and biometric approaches, and finally d) validating such entities using etio-pathology, outcome and treatment-response measures.

CONCLUSIONS

The proposed steps of deconstruction and "bottom-up" disease definition, as elsewhere in medicine, may well provide a better foundation for developing a nosology for psychotic disorders that may have better utility in predicting outcome, treatment response and etiology, and identifying novel treatment approaches.

Auditory event-related potentials (P3a, P3b) and genetic variants within the dopamine and serotonin system in healthy females

The late positive components of the human event-related brain potential comprise electrocortical reflections of stimulus-driven attentional capture (the anteriorly distributed P3a) and top-down control detection of relevant events (the posteriorly distributed P3b). As of yet, the neuropharmacologic and neurogenetic origin of the P3a and P3b is not fully understood. In this study, we address the contribution of dopaminergic and serotoninergic mechanisms. Sixty healthy females completed an active auditory novelty oddball paradigm while EEG was recorded. In all subjects, genetic polymorphisms within the dopamine system (dopamine transporter [DAT1], catecholamine-O-methyltransferase val158met [COMT val158met]) and the serotonin system (serotonin transporter [5HTTLPR]) were assessed. Across genotypes, novels (relative to standards) elicited a fronto-centrally distributed P3a, and targets (relative to standards) a parieto-centrally distributed P3b. Genotypes effects were observed for both P3a (COMT, 5HTTPLR) and P3b (DAT1, COMT, 5HTTLPR) only at prefrontal electrode location (Fz). Specifically, the frontal P3a was enhanced in COMT met/met homozygotes, but not in DAT1 9R. The target-related P3b was enhanced in COMT met/met and DAT1 9R relative to its genetic counterparts, but only at frontal electrodes. This 'anteriorized' enhancement may reflect either an additional frontal component in the target-related P3 dependent on dopamine, or a more subtle shift in the neural ensemble that generates the target-related P3. Results for 5HTTLPR short allele homozygotes mimicked those in COMT met/met homozygotes. In all, the present findings suggest involvement of frontal-cortical dopaminergic and serotoninergic mechanisms in bottom-up attentional capture (COMT val158met, 5HTTLPR), with an additional top-down component sensitive to striatal signals (DAT1).

Neural activations during auditory oddball processing discriminating schizophrenia and psychotic bipolar disorder

BACKGROUND

Reduced amplitude of the P300 event-related potential in auditory oddball tasks may characterize schizophrenia (SZ) but is also reported in bipolar disorder. Similarity of auditory processing abnormalities between these diagnoses is uncertain, given the frequent combination of both psychotic and nonpsychotic patients in bipolar samples; abnormalities may be restricted to psychosis. In addition, typically only latency and amplitude of brain responses at selected sensors and singular time points are used to characterize neural responses. Comprehensive quantification of brain activations involving both spatiotemporal and time-frequency analyses could better identify unique auditory oddball responses among patients with different psychotic disorders.

METHODS

Sixty SZ, 60 bipolar I with psychosis (BPP), and 60 healthy subjects (H) were compared on neural responses during an auditory oddball task using multisensor electroencephalography. Principal components analysis was used to reduce multisensor data before evaluating group differences on voltage and frequency of neural responses over time.

RESULTS

Linear discriminant analysis revealed five variables that best differentiated groups: 1) late beta activity to standard stimuli; 2) late beta/gamma activity to targets discriminated BPP from other groups; 3) midlatency theta/alpha activity to standards; 4) target-related voltage at the late N2 response discriminated both psychosis groups from H; and 5) target-related voltage during early N2 discriminated BPP from H.

CONCLUSIONS

Although the P300 significantly differentiated psychotic groups from H, it did not uniquely discriminate groups beyond the above variables. No variable uniquely discriminated SZ, perhaps indicating utility of this task for studying psychosis-associated neurophysiology generally and BPP specifically.

Genetic association study of the P300 endophenotype in schizophrenia

OBJECTIVE

Although reduced amplitude of the P300 event-related potential is a well-documented intermediate phenotype of schizophrenia, little is known about its genetic underpinnings in patients with schizophrenia. This study aims to examine associations between P300 and a range of candidate genetic variants, selected from either candidate gene studies or genome-wide association studies, in a large sample of patients with schizophrenia.

METHODS

P300 amplitude at the midline parietal electrode and 193 single nucleotide polymorphisms (SNPs) in 67 genes were assessed in 336 patients with schizophrenia. The association between each SNP and P300 amplitude, controlled for illness duration and gender, was evaluated. Associations at p<.01 were considered of potential relevance, while Bonferroni correction was applied to determine formal statistical significance (Bonferroni-corrected threshold of significance p=.0003).

RESULTS

Of the 193 selected SNPs, 4 SNPs showed potentially relevant association with P300 amplitude at a significance level of p<.01. One of these SNPs, rs1045642 in ABCB1, was most convincingly associated with P300 amplitude, reaching formal (Bonferroni-corrected) significance, while there was evidence for possible association with rs1572899 in DISC-1, rs6265 in BDNF and rs1625579 in MIR137.

CONCLUSION

Genetic variation in ABCB1 may be associated with P300 amplitude in patients with schizophrenia. This result may encourage further efforts to elucidate the genetic underpinnings of P300 generation.

An ICA with reference approach in identification of genetic variation and associated brain networks

To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p < 4 × 10⁻¹⁷), and discovered a brain functional network that was significantly associated with this genetic component (p < 1 × 10⁻⁴). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.

Dexamphetamine-induced reduction of P3a and P3b in healthy participants

The reduced P3 is one of the most robust deficits involved in schizophrenia. Previous research with catecholaminergic agonists or releasers such as amphetamines have used doses too small to adequately demonstrate an effect on P3. In this study, we gave 0.45 mg/kg dexamphetamine to healthy volunteers (final n = 18) using both auditory and visual three-stimulus P3 procedures. Dexamphetamine significantly reduced P3 amplitudes to auditory target, rare non-target and standard stimulus amplitudes. The reduction in auditory P3 induced by dexamphetamine was proportional across stimulus types to placebo P3 values. There were no effects of dexamphetamine on visual P3. We demonstrate a reduced auditory P3 similar to that seen in schizophrenia and other psychotic illnesses. This possibly reflects a common pathology which is hypothesized within the P3 literature to be related to attention and working memory. Differences between auditory and visual P3 modulation may be related to regional variations in catecholamine or specifically dopamine receptor densities. One specific auditory P3 generator is the superior temporal cortex, an area with dopamine D(2) receptor enriched bands. This is contrasted with visual specific generators, such as the inferior temporal cortex and superior parietal cortex, which do not have these enriched bands.

Pupillometry and P3 index the locus coeruleus-noradrenergic arousal function in humans

The adaptive gain theory highlights the pivotal role of the locus coeruleus-noradrenergic (LC-NE) system in regulating task engagement. In humans, however, LC-NE functional dynamics remain largely unknown. We evaluated the utility of two candidate psychophysiological markers of LC-NE activity: the P3 event-related potential and pupil diameter. Electroencephalogram and pupillometry data were collected from 24 participants who performed a 37-min auditory oddball task. As predicted by the adaptive gain theory, prestimulus pupil diameter exhibited an inverted U-shaped relationship to P3 and task performance such that largest P3 amplitudes and optimal performance occurred at the same intermediate level of pupil diameter. Large phasic pupil dilations, by contrast, were elicited during periods of poor performance and were followed by reengagement in the task and increased P3 amplitudes. These results support recent proposals that pupil diameter and the P3 are sensitive to LC-NE mode.

Components of cross-frequency modulation in health and disease

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F_1,175 = 9.25, P < 0.005), while modulation at fronto-temporal electrodes was greater in patients (F_1,175 = 17.5, P < 0.0001). We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cfM decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = −0.20, P < 0.01) across all subjects. Additionally, greater “aberrant” fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABA_A receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

Neurocognitive function in dopamine-β-hydroxylase deficiency

Dopamine-β-hydroxylase (DβH) deficiency is a rare genetic syndrome characterized by the complete absence of norepinephrine in the peripheral and the central nervous system. DβH-deficient patients suffer from several physical symptoms, which can be treated successfully with L-threo-3,4-dihydroxyphenylserine, a synthetic precursor of norepinephrine. Informal clinical observations suggest that DβH-deficient patients do not have obvious cognitive impairments, even when they are not medicated, which is remarkable given the important role of norepinephrine in normal neurocognitive function. This study provided the first systematic investigation of neurocognitive function in human DβH deficiency. We tested 5 DβH-deficient patients and 10 matched healthy control participants on a comprehensive cognitive task battery, and examined their pupil dynamics, brain structure, and the P3 component of the electroencephalogram. All participants were tested twice; the patients were tested once ON and once OFF medication. Magnetic resonance imaging scans of the brain revealed that the patients had a smaller total brain volume than the control group, which is in line with the recent hypothesis that norepinephrine has a neurotrophic effect. In addition, the patients showed an abnormally small or absent task-evoked pupil dilation. However, we found no substantial differences in cognitive performance or P3 amplitude between the patients and the control participants, with the exception of a temporal-attention deficit in the patients OFF medication. The largely spared neurocognitive function in DβH-deficient patients suggests that other neuromodulators have taken over the function of norepinephrine in the brains of these patients.

Genetic influence demonstrated for MEG-recorded somatosensory evoked responses

We tested for a genetic influence on magnetoencephalogram (MEG)-recorded somatosensory evoked fields (SEFs) in 20 monozygotic (MZ) and 14 dizygotic (DZ) twin pairs. Previous electroencephalogram (EEG) studies that demonstrated a genetic contribution to evoked responses generally focused on characteristics of representative brain potentials. Here we demonstrate significantly smaller amplitude differences within MZ compared to DZ twin pairs for the complete SEF time series (across left and right hand SEFs: 0.37 vs. 0.60 pT(2) and 0.28 vs. 0.39 pT(2) for primary [SI] and secondary [SII] sensory cortex activation) and higher MZ than DZ wave shape correlations (.71 vs. .44 and .52 vs. .35 for SI and SII activation). Our findings indicate a genetic influence on MEG-recorded evoked brain activity and also confirm our recent conclusion (van 't Ent, van Soelen, Stam, De Geus, & Boomsma, 2009) that higher MZ resemblance for EEG amplitudes is not trivially reflecting greater MZ concordance in intervening biological tissues.

A pilot multivariate parallel ICA study to investigate differential linkage between neural networks and genetic profiles in schizophrenia

Understanding genetic influences on both healthy and disordered brain function is a major focus in psychiatric neuroimaging. We utilized task-related imaging findings from an fMRI auditory oddball task known to be robustly associated with abnormal activation in schizophrenia, to investigate genomic factors derived from multiple single nucleotide polymorphisms (SNPs) from genes previously shown to be associated with schizophrenia. Our major aim was to investigate the relationship of these genomic factors to normal/abnormal brain functionality between controls and schizophrenia patients. We studied a Caucasian-only sample of 35 healthy controls and 31 schizophrenia patients. All subjects performed an auditory oddball task, which consists of detecting an infrequent sound within a series of frequent sounds. Each subject was characterized on 24 different SNP markers spanning multiple risk genes previously associated with schizophrenia. We used a recently developed technique named parallel independent component analysis (para-ICA) to analyze this multimodal data set (Liu et al., 2008). The method aims to identify simultaneously independent components of each modality (functional imaging, genetics) and the relationships between them. We detected three fMRI components significantly correlated with two distinct gene components. The fMRI components, along with their significant genetic profile (dominant SNP) correlations were as follows: (1) Inferior frontal-anterior/posterior cingulate-thalamus-caudate with SNPs from Brain derived neurotropic factor (BDNF) and dopamine transporter (DAT) [r=-0.51; p<0.0001], (2) superior/middle temporal gyrus-cingulate-premotor with SLC6A4_PR and SLC6A4_PR_AG (serotonin transporter promoter; 5HTTLPR) [r=0.27; p=0.03], and (3) default mode-fronto-temporal gyrus with Brain derived neurotropic factor and dopamine transporter (BDNF, DAT) [r=-0.25; p=0.04]. Functional components comprised task-relevant regions (including PFC, ACC, STG and MTG) frequently identified as abnormal in schizophrenia. Further, gene-fMRI combinations 1 (Z=1.75; p=0.03), 2 (Z=1.84; p=0.03) and 3 (Z=1.67; p=0.04) listed above showed significant differences between controls and patients, based on their correlated loading coefficients. We demonstrate a framework to identify interactions between "clusters" of brain function and of genetic information. Our results reveal the effect/influence of specific interactions, (perhaps epistastatic in nature), between schizophrenia risk genes on imaging endophenotypes representing attention/working memory and goal directed related brain function, thus establishing a useful methodology to probe multivariate genotype-phenotype relationships.

Genetic associations of brain structural networks in schizophrenia: a preliminary study

BACKGROUND

Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia's pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays.

METHOD

We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes.

RESULTS

We identified an sMRI component that significantly correlated with a genetic component (r = -.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A.

CONCLUSIONS

Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided.