Auditory oddball deficits in schizophrenia: an independent component analysis of the fMRI multisite function BIRN study

Imaging Transcriptomics of the Brain for Schizophrenia

Schizophrenia is a severe mental disorder with a neurodevelopmental origin. Although schizophrenia results from changes in the brain, the underlying biological mechanisms are unknown. Transcriptomics studies quantitative expression changes or qualitative changes of all genes and isoforms, providing a more meaningful biological insight. Magnetic resonance imaging (MRI) techniques play roles in revealing brain structure and function. We give a narrative focused review on the current transcriptome combined with MRI studies related to schizophrenia and summarize the research methodology and content of these studies to identify the research commonalities as well as the implications for future research, in an attempt to provide new insights into the mechanism, clinical diagnosis, and treatments of schizophrenia.

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approach's ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.

Auditory oddball hypoactivation in schizophrenia

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia

The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. We first evaluate the performance of feature selection in terms of Receiver operating characteristic curve and the area under the ROC curve in the simulation experiment. We demonstrate that the JSCoReg model can achieve higher accuracy compared with similar models including Joint Sparse Canonical Correlation Analysis and Sparse Collaborative Regression. We then applied the JSCoReg model to the analysis of schizophrenia dataset collected from the Mind Clinical Imaging Consortium. The JSCoReg enables us to better identify biomarkers associated with schizophrenia, which are verified to be both biologically and statistically significant.

A frontosensory circuit for visual context processing is synchronous in the theta/alpha band

Visual processing is strongly influenced by context. Stimuli that deviate from contextual regularities elicit augmented responses in primary visual cortex (V1). These heightened responses, known as "deviance detection," require both inhibition local to V1 and top-down modulation from higher areas of cortex. Here we investigated the spatiotemporal mechanisms by which these circuit elements interact to support deviance detection. Local field potential recordings in mice in anterior cingulate area (ACa) and V1 during a visual oddball paradigm showed that interregional synchrony peaks in the theta/alpha band (6-12 Hz). Two-photon imaging in V1 revealed that mainly pyramidal neurons exhibited deviance detection, while vasointestinal peptide-positive interneurons (VIPs) increased activity and somatostatin-positive interneurons (SSTs) decreased activity (adapted) to redundant stimuli (prior to deviants). Optogenetic drive of ACa-V1 inputs at 6-12 Hz activated V1-VIPs but inhibited V1-SSTs, mirroring the dynamics present during the oddball paradigm. Chemogenetic inhibition of VIP interneurons disrupted ACa-V1 synchrony and deviance detection responses in V1. These results outline spatiotemporal and interneuron-specific mechanisms of top-down modulation that support visual context processing.

P50 inhibition defects, psychopathology and gray matter volume in patients with first-episode drug-naive schizophrenia

BACKGROUND

Sensory gating deficits and gray matter volume (GMV) abnormalities have been found to be associated with the pathogenesis and psychopathology of patients with schizophrenia (SCZ). However, no studies have investigated their interrelationship in first-episode treatment-naive (FETN) SCZ patients.

METHODS

We recruited 52 FETN SCZ patients and 57 healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to measure the psychopathology of the patients. We collected magnetic resonance imaging and P50 inhibition data from all participants.

RESULTS

Compared to healthy controls, patients had shorter S1 and S2 latencies but larger S2 amplitudes and P50 ratio (Bonferroni adjusted all p < 0.01). In patients, S2 latency was independently associated with PANSS total score, negative symptoms and general psychopathology (t = 2.26-2.58, both P < 0.05), whereas S1 (t = 2.44, P < 0.05) and S2 latencies (t = 2.13, P < 0.05) were associated with PANSS cognitive factor. Moreover, GMV in the left inferior temporal gyrus, left lingual gyrus and right superior occipital gyrus, and bilateral dorsolateral superior frontal gyrus were each associated with the P50 components (all p < 0.05). In addition, GMV associated with S2 latency was negatively correlated with PANSS general psychopathology (t = -2.46, p < 0.05) and total score (t = -2.34, p < 0.05).

CONCLUSIONS

Our findings indicate that FETN SCZ patients exhibit deficits in P50 inhibition and GMV of brain regions associated with these deficits may be associated with their psychopathological symptoms, suggesting that brain structures associated with P50 components may be important biomarkers of SCZ psychopathology. Future studies could use a prospective longitudinal design to investigate the potential causal relationship of brain structures associated with P50 components in the psychopathological symptoms of SCZ patients.

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.

Anticipatory cues in emotional processing shift the activation of a combined salience sensorimotor functional network in drug-naïve depressed patients

BACKGROUND

Major depressive disorder is characterized by a large-scale brain network dysfunction, contributing to impairments in cognitive and affective functioning. Core regions of default mode, limbic and salience networks are also impaired in emotional processing and anticipation. This study aimed to explore default mode, salience, and limbic networks modulation during the processing of emotional stimuli with and without anticipatory cues in depression, and further investigate how these networks were functionally coupled with the rest of the brain.

METHODS

Twenty-one drug-naïve depressed patients and 15 matched controls were included in the study. All participants completed a psychological assessment and the affective pictures paradigm during an fMRI acquisition. Group independent component analysis and psychophysiological interactions analyses were performed.

RESULTS

A significant interaction between Cue, Valence and Group was found for the salience/sensorimotor network. When processing uncued emotional stimuli, patients showed increased activation of this network for negative vs. neutral pictures, whereas when anticipatory cues were displayed previously to the picture presentation, they invert this pattern of activation (hyperactivating the salience/sensorimotor network for positive vs. neutral pictures). Patients showed increased functional connectivity between the salience/sensorimotor network and the left amygdala as well as the right inferior parietal lobule compared to controls when processing uncued negative pictures.

LIMITATIONS

The sample size was modest, and the salience/sensorimotor network included regions not typically identified as part of salience network. Thus, this study should be replicated to further interpret the results.

CONCLUSIONS

Anticipatory cues shift the pattern of activation of the salience/sensorimotor network in drug-naïve depressed patients.

Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study

Revealing associations among various structural and functional patterns of the brain can yield highly informative results about the healthy and disordered brain. Studies using neuroimaging data have more recently begun to utilize the information within as well as across various functional and anatomical domains (i.e., groups of brain networks). However, most whole-brain approaches assume similar complexity of interactions throughout the brain. Here we investigate the hypothesis that interactions between brain networks capture varying amounts of complexity, and that we can better capture this information by varying the complexity of the model subspace structure based on available training data. To do this, we employ a Bayesian optimization-based framework known as the Tree Parzen Estimator (TPE) to identify, exploit and analyze patterns of variation in the information encoded by temporal information extracted from functional magnetic resonance imaging (fMRI) subdomains of the brain. Using a repeated cross-validation procedure on a schizophrenia classification task, we demonstrate evidence that interactions between specific functional subdomains are better characterized by more sophisticated model architectures compared to less complicated ones required by the others for optimally contributing towards classification and understanding the brain's functional interactions. We show that functional subdomains known to be involved in schizophrenia require more complex architectures to optimally unravel discriminatory information about the disorder. Our study points to the need for adaptive, hierarchical learning frameworks that cater differently to the features from different subdomains, not only for a better prediction but also for enabling the identification of features predicting the outcome of interest.

Brain networks alterations in cocaine use and gambling disorders during emotion regulation

Background

Cocaine use disorder (CUD) and gambling disorder (GD) share clinical features and neural alterations, including emotion regulation deficits and dysfunctional activation in related networks. However, they also exhibit differential aspects, such as the neuroadaptive effects of long-term drug consumption in CUD as compared to GD. Neuroimaging research aimed at disentangling their shared and specific alterations can contribute to improve understanding of both disorders.

Methods

We compared CUD (N = 15), GD (N = 16) and healthy comparison (HC; N = 17) groups using a network-based approach for studying temporally coherent functional networks during functional magnetic resonance imaging (fMRI) of an emotion regulation task. We focused our analysis in limbic, ventral frontostriatal, dorsal attentional (DAN) and executive networks (FPN), given their involvement in emotion regulation and their alteration in CUD and GD. Correlations with measures of emotional experience and impulsivity (UPPS-P) were also performed.

Results

The limbic network was significantly decreased during emotional processing both for CUD and GD individuals compared to the HC group. Furthermore, GD participants compared to HC showed an increased activation in the ventral frontostriatal network during emotion regulation. Finally, networks' activation patterns were modulated by impulsivity traits.

Conclusions

Functional network analyses revealed both overlapping and unique effects of stimulant and gambling addictions on neural networks underpinning emotion regulation.

A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis

BACKGROUND AND OBJECTIVE

The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified.

METHODS

We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels' generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers.

RESULTS

According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works.

CONCLUSIONS

The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.

Tracking spatial dynamics of functional connectivity during a task

Functional connectivity (FC) measured from functional magnetic resonance imaging (fMRI) provides a powerful tool to explore brain organization. Studies of the temporal dynamics of brain organization have shown a large temporal variability of the functional connectome, which may be associated with mental status transitions and/or adaptive process. Most dynamic studies, e.g. functional connectome and functional network connectivity (FNC), have focused on the macroscopic FC changes, i.e. the changes of temporal coherence across various brain network sources, nodes and/or regions of interest, where it is assumed within the network or node that the FC is static. In this paper, we develop a novel method to examine the spatial dynamics of FC, without the assumption of its intra-network stationarity. We applied our approach to fMRI data during an auditory oddball task (AOD) from twenty-two subjects, in an attempt to capture/validate the approach by evaluating whether spatial connectivity varies with task condition. The results showed that connectivity networks exhibit spatial variability over time, in addition to participating in conventional temporal dynamics, i.e. cross-network variability or dynamic functional network connectivity (dFNC). Furthermore, we studied the relationship of spatial dynamic in FC to cognitive processes, by performing a cluster analysis to evaluate an individual's functional correspondence towards the 'target' (oddball) detection from AOD task, and extracting cognitive task correspondence states as well as their dynamic FC spatial maps segregated by such states. We found a clear trend in different task-guided states, particularly, a prominent reduction of task stimulus synchrony state along with strong anticorrelation between default mode network (DMN) and cognitive attentional networks. We also observed an increasing occurrence of the task desynchrony state which showed an absence of DMN anticorrelation. The results highlight the impact of a well-studied cognitive task on the observed spatial dynamic structure. We also showed that the FC spatial dynamic pattern from our method largely corresponds to macroscopic dFNC patterns, but with more details and specifications over space, meanwhile the connectivity within the source itself provides novel information and varies over time. Overall, we demonstrate clear evidence of the presence of the (usually ignored) spatial dynamics of connectivity, its links to the task and implications of cognition/mental status.

Neural Abnormalities in Fear Generalization in Schizophrenia and Associations With Negative Symptoms

BACKGROUND

Associative learning and memory processes, including the generalization of previously learned associations, may be altered in schizophrenia. Deficits in schizophrenia in stimulus generalization, one of the simplest forms of memory, could interfere with the ability to efficiently categorize related, similar information, potentially leading to impairments in daily functioning.

METHODS

To measure generalization in schizophrenia, 37 individuals with a nonaffective psychotic disorder and 32 demographically matched healthy control subjects underwent a Pavlovian fear conditioning and generalization procedure, which accounted for variation in perceptual ability across participants, while undergoing functional magnetic resonance imaging. Skin conductance and neural responses to conditioned (CS+), neutral (CS-), and generalization stimuli were measured. Explicit memory ratings reflecting successful generalization were also collected after the scanning, as well as measures of symptom severity.

RESULTS

Compared with healthy control subjects, individuals with nonaffective psychotic disorders showed significant deficits in fear generalization across multiple measurements, with impairments in memory ratings and reductions in activation and deactivation of the salience and default networks, respectively, during fear generalization. Moreover, in the psychotic disorder group, greater behavioral and neural abnormalities in generalization were associated with higher levels of negative symptoms.

CONCLUSIONS

Fear generalization is impaired in psychotic illness. Given that successful generalization relies on a dynamic balance between excitatory and inhibitory neurotransmission, these results reveal a potentially quantifiable mechanism linked to negative symptoms that can be investigated further in future human and experimental animal studies.

Default mode network modulation by mentalizing in young adults with autism spectrum disorder or schizophrenia

Schizophrenia and autism spectrum disorder (ASD) are nosologically distinct neurodevelopmental disorders with similar deficits in social cognition, including the ability to form mental representations of others (i.e., mentalizing). However, the extent of patient deficit overlap in underlying neural mechanisms is unclear. Our goal was to examine deficits in mentalizing task-related (MTR) activity modulation in schizophrenia and ASD and the relationship of such deficits with social functioning and psychotic symptoms in patients. Adults, ages 18-34, diagnosed with either ASD or schizophrenia, and typically developed controls (n = 30/group), performed an interactive functional MRI Domino task. Using independent component analysis, we analyzed game intervals known to stimulate mentalizing in the default mode network (DMN), i.e., medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precuneus, and temporoparietal junction (TPJ), for group differences in MTR activity and associations between MTR activity and social and psychosis measures. Compared to controls, both schizophrenia and ASD groups showed MTR activity deficits in PCC and TPJ. In TPJ and MPFC, MTR activity modulation was associated with social communication impairments only in ASD. In precuneus, MTR activity was associated with increased self-reported fantasizing only in schizophrenia. In schizophrenia, we found no indication of over-mentalizing activity or an association between MTR activity and psychotic symptoms. Results suggest shared neural deficits between ASD and schizophrenia in mentalizing-associated DMN regions; however, neural organization might correspond to different dimensional social deficits. Our results therefore indicate the importance of examining both categorical-clinical diagnosis and social functioning dimensional constructs when examining neural deficits in schizophrenia and ASD.

Motivational factors modulate left frontoparietal network during cognitive control in cocaine addiction

Cocaine addiction is characterized by alterations in motivational and cognitive processes involved in goal-directed behavior. Recent studies have shown that addictive behaviors can be attributed to alterations in the activity of large functional networks. The aim of this study was to investigate how cocaine addiction affected the left frontoparietal network during goal-directed behavior in a stop-signal task (SST) with reward contingencies by correct task performance. Twenty-eight healthy controls (HC) and 30 abstinent cocaine-dependent patients (ACD) performed SST with monetary reward contingencies while undergoing a functional magnetic resonance imaging scan. The results showed that the left frontoparietal network (FPN) displayed an effect of cocaine addiction depending on reward contingencies rather than inhibition accuracy; and, second, we observed a negative correlation between dependence severity and the modulation of the left FPN network by the monetary reward in ACD. These findings highlight the role of the left FPN in the motivational effects of cocaine dependence.

Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.

Biomarker Identification Through Integrating fMRI and Epigenetics

OBJECTIVE

Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ).

METHODS

We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data.

RESULT

After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is [Formula: see text]. By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon.

CONCLUSION

This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies.

SIGNIFICANCE

Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.

The influence of age and ASD on verbal fluency networks

BACKGROUND

The integrity and connectivity of the frontal lobe, which subserves fluency, may be compromised by both ASD and aging. Alternate networks often integrate to help compensate for compromised functions during aging. We used network analyses to study how compensation may overcome age-related compromised in individuals with ASD.

METHOD

Participants consisted of middle-aged (40-60; n=24) or young (18-25; n=18) right-handed males who have a diagnosis of ASD, and age- and IQ-matched control participants (n=20, 14, respectively). All performed tests of language and executive functioning and a fluency functional MRI task. We first used group individual component analysis (ICA) for each of the 4 groups to determine whether different networks were engaged. An SPM analysis was used to compare activity detected in the network nodes from the ICA analyses.

RESULTS

The individuals with ASD performed more slowly on two cognitive tasks (Stroop word reading and Trailmaking Part A). The 4 groups engaged different networks during the fluency fMRI task despite equivalent performance. Comparisons of specific regions within these networks indicated younger individuals had greater engagement of the thalamus and supplementary speech area, while older adults engaged the superior temporal gyrus. Individuals with ASD did not disengage from the Default Mode Network during word generation.

CONCLUSION

Interactions between diagnosis and aging were not found in this study of young and middle-aged men, but evidence for differential engagement of compensatory networks was observed.

Age Group Differences in Executive Network Functional Connectivity and Relationships with Social Behavior in Men with Autism Spectrum Disorder

BACKGROUND

Research suggests adults with autism spectrum disorder (ASD) may use executive functions to compensate for social difficulties. Given hallmark age-related declines in executive functioning and the executive brain network in normal aging, there is concern that older adults with ASD may experience further declines in social functioning as they age. In a male-only sample, we hypothesized: 1) older adults with ASD would demonstrate greater ASD-related social behavior than young adults with ASD, 2) adults with ASD would demonstrate a greater age group reduction in connectivity of the executive brain network than neurotypical (NT) adults, and 3) that behavioral and neural mechanisms of executive functioning would predict ASD-related social difficulties in adults with ASD.

METHODS

Participants were a cross-sectional sample of non-intellectually disabled young (ages 18-25) and middle-aged (ages 40-70) adult men with ASD and NT development (young adult ASD: n=24; middle-age ASD: n=25; young adult NT: n=15; middle-age NT: n=21). We assessed ASD-related social behavior via the self-report Social Responsiveness Scale-2 (SRS-2) Total Score, with exploratory analyses of the Social Cognition Subscale. We assessed neural executive function via connectivity of the resting-state executive network (EN) as measured by independent component analysis. Correlations were investigated between SRS-2 Total Scores (with exploratory analyses of the Social Cognition Subscale), EN functional connectivity of the dorsolateral prefrontal cortex (dlPFC), and a behavioral measure of executive function, Tower of London (ToL) Total Moves.

RESULTS

We did not confirm a significant age group difference for adults with ASD on the SRS-2 Total Score; however, exploratory analysis revealed middle-age men with ASD had higher scores on the SRS-2 Social Cognition Subscale than young adult men with ASD. Exacerbated age group reductions in EN functional connectivity were confirmed (left dlPFC) in men with ASD compared to NT, such that older adults with ASD demonstrated the greatest levels of hypoconnectivity. A significant correlation was confirmed between dlPFC connectivity and the SRS-2 Total Score in middle-age men with ASD, but not young adult men with ASD. Furthermore, exploratory analysis revealed a significant correlation with the SRS-2 Social Cognition Subscale for young and middle-aged ASD groups and ToL Total Moves.

CONCLUSIONS

Our findings suggest that ASD-related difficulties in social cognition and EN hypoconnectivity may get worse with age in men with ASD and is related to executive functioning. Further, exacerbated EN hypoconnectivity associated with older age in ASD may be a mechanism of increased ASD-related social cognition difficulties in older adults with ASD. Given the cross-sectional nature of this sample, longitudinal replication is needed.

Age-Related Atrophy and Compensatory Neural Networks in Reading Comprehension

OBJECTIVES

Despite changes to brain integrity with aging, some functions like basic language processes remain remarkably preserved. One theory for the maintenance of function in light of age-related brain atrophy is the engagement of compensatory brain networks. This study examined age-related changes in the neural networks recruited for simple language comprehension.

METHODS

Sixty-five adults (native English-speaking, right-handed, and cognitively normal) aged 17-85 years underwent a functional magnetic resonance imaging (fMRI) reading paradigm and structural scanning. The fMRI data were analyzed using independent component analysis to derive brain networks associated with reading comprehension.

RESULTS

Two typical frontotemporal language networks were identified, and these networks remained relatively stable across the wide age range. In contrast, three attention-related networks showed increased activation with increasing age. Furthermore, the increased recruitment of a dorsal attention network was negatively correlated to gray matter thickness in temporal regions, whereas an anterior frontoparietal network was positively correlated to gray matter thickness in insular regions.

CONCLUSIONS

We found evidence that older adults can exert increased effort and recruit additional attentional resources to maintain their reading abilities in light of increased cortical atrophy.

Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients

Background: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task.

Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls.

Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus.

Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.

Identification of brain regions associated with working memory deficit in schizophrenia

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected.

Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients.

Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Aberrant timing and oddball detection in Schizophrenia: findings from a signed differential mapping meta-analysis

Schizophrenia (SZ) is associated with deficits in both temporal and salience processing. The underlying neurological dysfunctions in both processes, which are interrelated and share neuroanatomical bases, remain poorly understood. The principal objective of this study was to elucidate whether there are any brain regions that show abnormal response during timing and oddball tasks in patients with SZ. To this end, we conducted a signed differential mapping (SDM) meta-analysis of functional magnetic resonance imaging (fMRI) studies assessing abnormal responses elicited by tasks based on the oddball paradigm in patients with SZ. We conducted a similar SDM meta-analysis of neuroimaging studies of timing tasks in SZ. Finally, we undertook a multimodal meta-analysis to detect the common findings of the two previous meta-analyses. We found that SZ patients showed hypoactivation in cortical and subcortical areas related to timing. The dysfunction observed during timing tasks partially coincided with deficiencies in change-detection functions (particularly in the case of preattentional processing in the mismatch negativity response). We hypothesize that a dysfunctional timing/change detection network underlies the cognitive impairment observed in SZ.

Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model

Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model - ψ-learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation.

Linked functional network abnormalities during intrinsic and extrinsic activity in schizophrenia as revealed by a data-fusion approach

Abnormalities in functional brain networks in schizophrenia have been studied by examining intrinsic and extrinsic brain activity under various experimental paradigms. However, the identified patterns of abnormal functional connectivity (FC) vary depending on the adopted paradigms. Thus, it is unclear whether and how these patterns are inter-related. In order to assess relationships between abnormal patterns of FC during intrinsic activity and those during extrinsic activity, we adopted a data-fusion approach and applied partial least square (PLS) analyses to FC datasets from 25 patients with chronic schizophrenia and 25 age- and sex-matched normal controls. For the input to the PLS analyses, we generated a pair of FC maps during the resting state (REST) and the auditory deviance response (ADR) from each participant using the common seed region in the left middle temporal gyrus, which is a focus of activity associated with auditory verbal hallucinations (AVHs). PLS correlation (PLS-C) analysis revealed that patients with schizophrenia have significantly lower loadings of a component containing positive FCs in default-mode network regions during REST and a component containing positive FCs in the auditory and attention-related networks during ADR. Specifically, loadings of the REST component were significantly correlated with the severities of positive symptoms and AVH in patients with schizophrenia. The co-occurrence of such altered FC patterns during REST and ADR was replicated using PLS regression, wherein FC patterns during REST are modeled to predict patterns during ADR. These findings provide an integrative understanding of altered FCs during intrinsic and extrinsic activity underlying core schizophrenia symptoms.

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A new fMRI analysis method for data fusion was applied to schizophrenia fMRI data.

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Multiple patterns of abnormal functional connectivity were linked in schizophrenia.

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Abnormal networks included the auditory language and saliency networks and DMN.

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The identified patterns of connectivity abnormalities were correlated with symptoms.

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI

BACKGROUND AND OBJECTIVES

Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia. But, fMRI data is huge in dimension. Therefore dimension reduction is indispensable. It is additionally required to identify the discriminative brain regions. Hence, we aim to build an effective decision model that incorporates suitable dimension reduction and also identifies discriminative brain regions.

METHODS

We propose a three-phase dimension reduction. First phase involves spatially-constrained fuzzy clustering of 3-dimensional spatial maps (obtained from general linear model and independent component analysis). In the second phase, non-linear features are extracted from each cluster using a generalized discriminant analysis. In the third phase, a novel fuzzy rough feature selection is proposed. The features obtained after the third phase are used for learning a decision model by the help of support vector machine classifier. This complete method is implemented within leave-one-out cross-validation on two balanced datasets (respectively acquired on 1.5Tesla and 3Tesla scanners). Both these datasets are created using Function Biomedical Informatics Research Network multisite data and contain fMRI data acquired during auditory oddball task performed by age-matched schizophrenia patients and healthy subjects. A permutation test is also carried out to ensure that no bias is involved in the learning.

RESULTS

The results indicate that the proposed method achieves maximum classification accuracy of 97.1% and 98.0% for the two datasets respectively. The proposed method outperforms the state-of-the-art methods. The results of the permutation test show that p-values are lesser than the significance level i.e. 0.05. Therefore, the classifier has found a significant class structure and does not involve any bias. Further, discriminative brain regions are identified and are in agreement with the findings in related literature.

CONCLUSION

The proposed method is able to derive suitable non-linear features and the related brain regions for effective computer-aided diagnosis. The fuzzy and rough set based approaches help in handling uncertainty and ambiguity in real data.

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease

BACKGROUND/OBJECTIVE

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. We applied longitudinal resting state functional magnetic resonance imaging (fMRI) to delineate functional brain connections relevant for disease progression and diagnostic accuracy.

METHODS

We used two-center resting state fMRI data of 20 AD patients (65.1±8.0 years), 12 bvFTD patients (64.7±5.4 years), and 22 control subjects (63.8±5.0 years) at baseline and 1.8-year follow-up. We used whole-network and voxel-based network-to-region analyses to study group differences in functional connectivity at baseline and follow-up, and longitudinal changes in connectivity within and between groups.

RESULTS

At baseline, connectivity between paracingulate gyrus and executive control network, between cuneal cortex and medial visual network, and between paracingulate gyrus and salience network was higher in AD compared with controls. These differences were also present after 1.8 years. At follow-up, connectivity between angular gyrus and right frontoparietal network, and between paracingulate gyrus and default mode network was lower in bvFTD compared with controls, and lower compared with AD between anterior cingulate gyrus and executive control network, and between lateral occipital cortex and medial visual network. Over time, connectivity decreased in AD between precuneus and right frontoparietal network and in bvFTD between inferior frontal gyrus and left frontoparietal network. Longitudinal changes in connectivity between supramarginal gyrus and right frontoparietal network differ between both patient groups and controls.

CONCLUSION

We found disease-specific brain regions with longitudinal connectivity changes. This suggests the potential of longitudinal resting state fMRI to delineate regions relevant for disease progression and for diagnostic accuracy, although no group differences in longitudinal changes in the direct comparison of AD and bvFTD were found.

Executive function and functional and structural brain differences in middle-age adults with autism spectrum disorder

There is a rapidly growing group of aging adults with autism spectrum disorder (ASD) who may have unique needs, yet cognitive and brain function in older adults with ASD is understudied. We combined functional and structural neuroimaging and neuropsychological tests to examine differences between middle-aged men with ASD and matched neurotypical (NT) men. Participants (ASD, n = 16; NT, n = 17) aged 40-64 years were well-matched according to age, IQ (range: 83-131), and education (range: 9-20 years). Middle-age adults with ASD made more errors on an executive function task (Wisconsin Card Sorting Test) but performed similarly to NT adults on tests of delayed verbal memory (Rey Auditory Verbal Learning Test) and local visual search (Embedded Figures Task). Independent component analysis of a functional MRI working memory task (n-back) completed by most participants (ASD = 14, NT = 17) showed decreased engagement of a cortico-striatal-thalamic-cortical neural network in older adults with ASD. Structurally, older adults with ASD had reduced bilateral hippocampal volumes, as measured by FreeSurfer. Findings expand our understanding of ASD as a lifelong condition with persistent cognitive and functional and structural brain differences evident at middle-age. Autism Res 2017, 10: 1945-1959. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

We compared cognitive abilities and brain measures between 16 middle-age men with high-functioning autism spectrum disorder (ASD) and 17 typical middle-age men to better understand how aging affects an older group of adults with ASD. Men with ASD made more errors on a test involving flexible thinking, had less activity in a flexible thinking brain network, and had smaller volume of a brain structure related to memory than typical men. We will follow these older adults over time to determine if aging changes are greater for individuals with ASD.

Neuronal correlates of cognitive function in patients with childhood cerebellar tumor lesions

While it has been shown that cerebellar tumor lesions have an impact on cognitive functions, the extent to which they shape distant neuronal pathways is still largely undescribed. Thus, the present neuroimaging study was designed to investigate different aspects of cognitive function and their neuronal correlates in patients after childhood cerebellar tumor surgery. An alertness task, a working memory task and an incompatibility task were performed by 11 patients after childhood cerebellar tumor surgery and 17 healthy controls. Neuronal correlates as reflected by alterations in functional networks during tasks were assessed using group independent component analysis. We were able to identify eight networks involved during task performance: default mode network, precuneus, anterior salience network, executive control network, visual network, auditory and sensorimotor network and a cerebellar network. For the most ‘basic’ cognitive tasks, a weaker task-modulation of default mode network, left executive control network and the cerebellar network was observed in patients compared to controls. Results for higher-order tasks are in line with a partial restoration of networks responsible for higher-order task execution. Our results provide tentative evidence that the synchronicity of brain activity in patients was at least partially restored in the course of neuroplastic reorganization, particularly for networks related to higher-order cognitive processes. The complex activation patterns underline the importance of testing several cognitive functions to assess the specificity of cognitive deficits and neuronal reorganization processes after brain lesions.

Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia

The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the data sets jointly as in data fusion or separately and then combining as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge. The primary reason for this is the difficulty in determining the optimal contribution of each data set to an analysis as well as the amount of potentially exploitable complementary information among data sets. In this paper, we propose a novel classification rate-based technique to unambiguously quantify the contribution of each data set to a fusion result as well as facilitate direct comparisons of fusion methods on real data and apply a new method, independent vector analysis (IVA), to multiset fusion. This classification rate-based technique is used on functional magnetic resonance imaging data collected from 121 patients with schizophrenia and 150 healthy controls during the performance of three tasks. Through this application, we find that though optimal performance is achieved by exploiting all tasks, each task does not contribute equally to the result and this framework enables effective quantification of the value added by each task. Our results also demonstrate that data fusion methods are more powerful than data integration methods, with the former achieving a classification rate of 73.5 % and the latter achieving one of 70.9 %, a difference which we show is significant when all three tasks are analyzed together. Finally, we show that IVA, due to its flexibility, has equivalent or superior performance compared with the popular data fusion method, joint independent component analysis.

Functional connectivity when detecting rare visual targets in schizophrenia

Individuals with schizophrenia demonstrate difficulties in attending to important stimuli (e.g., targets) and ignoring distractors (e.g., non-targets). We used a visual oddball task during fMRI to examine functional connectivity within and between the ventral and dorsal attention networks to determine the relative contribution of each network to detection of rare visual targets in schizophrenia. The sample comprised 25 schizophrenia patients and 27 healthy controls. Psychophysiological interaction analysis was used to examine whole-brain functional connectivity in response to targets. We used the right temporo parietal junction (TPJ) as the seed region for the ventral network and the right medial intraparietal sulcus (IPS) as the seed region for the dorsal network. We found that connectivity between right IPS and right anterior insula (AI; a component of the ventral network) was significantly greater in controls than patients. Expected patterns of within- and between-network connectivity for right TPJ were observed in controls, and not significantly different in patients. These findings indicate functional connectivity deficits between the dorsal and ventral attention networks in schizophrenia that may create problems in processing relevant versus irrelevant stimuli. Understanding the nature of network disruptions underlying cognitive deficits of schizophrenia may help shed light on the pathophysiology of this disorder.

Attenuated intrinsic connectivity within cognitive control network among individuals with remitted depression: Temporal stability and association with negative cognitive styles

Many individuals with major depressive disorder (MDD) experience cognitive dysfunction including impaired cognitive control and negative cognitive styles. Functional connectivity magnetic resonance imaging studies of individuals with current MDD have documented altered resting-state connectivity within the default-mode network and across networks. However, no studies to date have evaluated the extent to which impaired connectivity within the cognitive control network (CCN) may be present in remitted MDD (rMDD), nor have studies examined the temporal stability of such attenuation over time. This represents a major gap in understanding stable, trait-like depression risk phenotypes. In this study, resting-state functional connectivity data were collected from 52 unmedicated young adults with rMDD and 47 demographically matched healthy controls, using three bilateral seeds in the CCN (dorsolateral prefrontal cortex, inferior parietal lobule, and dorsal anterior cingulate cortex). Mean connectivity within the entire CCN was attenuated among individuals with rMDD, was stable and reliable over time, and was most pronounced with the right dorsolateral prefrontal cortex and right inferior parietal lobule, results that were corroborated by supplemental independent component analysis. Attenuated connectivity in rMDD appeared to be specific to the CCN as opposed to representing attenuated within-network coherence in other networks (e.g., default-mode, salience). In addition, attenuated connectivity within the CCN mediated relationships between rMDD status and cognitive risk factors for depression, including ruminative brooding, pessimistic attributional style, and negative automatic thoughts. Given that these cognitive markers are known predictors of relapse, these results suggest that attenuated connectivity within the CCN could represent a biomarker for trait phenotypes of depression risk. Hum Brain Mapp 38:2939-2954, 2017. © 2017 Wiley Periodicals, Inc.

Reduced activity in functional networks during reward processing is modulated by abstinence in cocaine addicts

Cocaine addiction is characterized by alterations in motivational and cognitive processes. Recent studies have shown that some alterations present in cocaine users may be related to the activity of large functional networks. The aim of this study was to investigate how these functional networks are modulated by non-drug rewarding stimuli in cocaine-dependent individuals. Twenty abstinent cocaine-dependent and 21 healthy matched male controls viewed erotic and neutral pictures while undergoing a functional magnetic resonance imaging scan. Group independent component analysis was then performed in order to investigate how functional networks were modulated by reward in cocaine addicts. The results showed that cocaine addicts, compared with healthy controls, displayed diminished modulation of the left frontoparietal network in response to erotic pictures, specifically when they were unpredicted. Additionally, a positive correlation between the length of cocaine abstinence and the modulation of the left frontoparietal network by unpredicted erotic images was found. In agreement with current addiction models, our results suggest that cocaine addiction contributes to reduce sensitivity to rewarding stimuli and that abstinence may mitigate this effect.

Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI

Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm provided by use of independent component analysis is a potential breakthrough that can inform ways in which prediction models can be integrated for use in clinical and experimental medicine studies.

A Review of the Functional and Anatomical Default Mode Network in Schizophrenia

Schizophrenia is a severe mental disorder characterized by impaired perception, delusions, thought disorder, abnormal emotion regulation, altered motor function, and impaired drive. The default mode network (DMN), since it was first proposed in 2001, has become a central research theme in neuropsychiatric disorders, including schizophrenia. In this review, first we define the DMN and describe its functional activity, functional and anatomical connectivity, heritability, and inverse correlation with the task positive network. Second, we review empirical studies of the anatomical and functional DMN, and anti-correlation between DMN and the task positive network in schizophrenia. Finally, we review preliminary evidence about the relationship between antipsychotic medications and regulation of the DMN, review the role of DMN as a treatment biomarker for this disease, and consider the DMN effects of individualized therapies for schizophrenia.

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Amplitude variability over trials in hemodynamic responses in adolescents with ADHD: The role of the anterior default mode network and the non-specific role of the striatum

It has been suggested that intra-individual variability (IIV) in performance on attention and other cognitive tasks might be a cognitive endophenotype in individuals with ADHD. Despite robust IIV findings in behavioral data, only sparse data exist on how what type of brain dysfunction underlies variable response times. In this study, we asked whether ADHD IIV in reaction time on a commonly-used test of attention might be related to variation in hemodynamic responses (HRs) observed trial-to-trial. Based on previous studies linking IIV to regions within the “default mode” network (DMN), we predicted that adolescents with ADHD would have higher HR variability in the DMN compared with controls, and this in turn would be related to behavioral IIV. We also explored the influence of social anxiety on HR variability in ADHD as means to test whether higher arousal associated with high trait anxiety would affect the neural abnormalities. We assessed single-trial variability of HRs, estimated from fMRI event-related responses elicited during an auditory oddball paradigm in adolescents with ADHD and healthy controls (11–18 years old; N = 46). Adolescents with ADHD had higher HR variability compared with controls in anterior regions of the DMN. This effect was specific to ADHD and not associated with traits of age, IQ and anxiety. However, an ADHD effect of higher HR variability also appeared in a basal ganglia network, but for these brain regions the relationships of HR variability and social anxiety levels were more complex. Performance IIV correlated significantly with variability of HRs in both networks. These results suggest that assessment of trial-to-trial HR variability in ADHD provides information beyond that detectable through analysis of behavioral data and average brain activation levels, revealing specific neural correlates of a possible ADHD IIV endophenotype.

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We studied if the behavioral variability in ADHD is also found on a neuronal level.

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Independent component analysis was combined with BOLD amplitude variability.

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Adolescents with ADHD had higher amplitude variability than healthy controls.

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Higher amplitude variability was shown in an anterior default mode network.

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Social anxiety in ADHD associated with high amplitude variability in the striatum

Impairment in subcortical suppression in schizophrenia: Evidence from the fBIRN Oddball Task

Schizophrenia patients show widespread impairments in brain activity during oddball tasks, which involve responding to infrequent target stimuli while refraining from responding during continuous non-target stimuli. In a network-based investigation comparing schizophrenia or schizoaffective patients to healthy controls, we sought to clarify which networks were specifically associated with target detection using a multivariate analysis technique that identifies task-specific functional brain networks. We acquired data from the publicly available function biomedical informatics research network collaboration, including 58 patients and 50 controls. Two task-based functional brain networks were identified: (1) a response modulation network including bilateral temporal pole, supramarginal gyrus, striatum, and thalamus, on which patients showed decreased activity relative to controls; and (2) an auditory-motor response activation network, on which patients showed a slower return to baseline than controls, but no difference in peak activation. For both groups, baseline to peak activation of the response modulation network correlated negatively with peak to baseline activity in the response activation network, suggesting a role in suppressing the motor response following targets. Patients' impaired activity in the response modulation network, and subsequent longer return to baseline in the response activation network, correspond with their later and less accurate behavioral performance, suggesting that impairment in suppression of the auditory-motor response activation network could underlie oddball task deficits in schizophrenia. In addition, the magnitude of the activity in the response modulation network was correlated with intensity of delusions of reference, supporting the notion that increased referential ideation is associated with hyperactivity within the subcortical striatal-limbic network. Hum Brain Mapp 37:4640-4653, 2016. © 2016 Wiley Periodicals, Inc.

Diffusion magnetic resonance imaging study of schizophrenia in the context of abnormal neurodevelopment using multiple site data in a Chinese Han population

Schizophrenia has increasingly been considered a neurodevelopmental disorder, and the advancement of neuroimaging techniques and associated computational methods has enabled quantitative re-examination of this important theory on the pathogenesis of the disease. Inspired by previous findings from neonatal brains, we proposed that an increase in diffusion magnetic resonance imaging (dMRI) mean diffusivity (MD) should be observed in the cerebral cortex of schizophrenia patients compared with healthy controls, corresponding to lower tissue complexity and potentially a failure to reach cortical maturation. We tested this hypothesis using dMRI data from a Chinese Han population comprising patients from four different hospital sites. Utilizing data-driven methods based on the state-of-the-art tensor-based registration algorithm, significantly increased MD measurements were consistently observed in the cortex of schizophrenia patients across all four sites, despite differences in psychopathology, exposure to antipsychotic medication and scanners used for image acquisition. Specifically, we found increased MD in the limbic system of the schizophrenic brain, mainly involving the bilateral insular and prefrontal cortices. In light of the existing literature, we speculate that this may represent a neuroanatomical signature of the disorder, reflecting microstructural deficits due to developmental abnormalities. Our findings not only provide strong support to the abnormal neurodevelopment theory of schizophrenia, but also highlight an important neuroimaging endophenotype for monitoring the developmental trajectory of high-risk subjects of the disease, thereby facilitating early detection and prevention.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.