Superior temporal gyrus and planum temporale in schizophrenia: a selective review

Size and Topography of the Brain's Functional Networks with Psychotic Experiences, Schizophrenia, and Bipolar Disorder

Background

Existing functional connectivity studies of psychosis use population-averaged functional network maps, despite highly variable topographies of these networks across the brain surface. We aimed to define the functional network areas and topographies in the general population and the changes associated with psychotic experiences (PEs) and disorders.

Methods

Maps of 8 functional networks were generated using an individual-specific template-matching procedure for each participant from the Human Connectome Project Young Adult cohort (n = 1003) and from a matched case cohort (schizophrenia [SCZ], n = 27; bipolar disorder, n = 35) scanned identically with the same Connectom scanner. In the Human Connectome Project Young Adult cohort, PEs were estimated based on scores from the Achenbach Self-Report Scale. The relationship of symptoms to the probability of network representation at each cortical vertex was assessed using logistic regression.

Results

In Human Connectome Project Young Adult participants, PE severity on the Achenbach thought problems scale was predicted by increased language network (LAN) and dorsal attention network (DAN) areas and decreased cingulo-opercular network area (r < 0.12). Significant effects were found in SCZ, with a larger DAN and LAN and a smaller frontoparietal network. Network pattern analysis in SCZ showed an increased probability of LAN in the posterior region of the left superior temporal gyrus and of the visual network in the left insula. Regression analyses in SCZ found that mood dysregulation was related to increased DAN surface area.

Conclusions

Those with PEs and SCZ showed abnormal functional network cortical topographies, particularly involving DAN and LAN. Network findings may predict psychosis progression and guide earlier intervention.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10-4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10-3; p = 7.67 × 10-4) and right insula (p = 0.02; p = 8.99 × 10-6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10-5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = -0.36, p = 8.45 × 10-3) and small-worldness (r = -0.35, p = 9.89 × 10-3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Long-term effects of childhood single-parent family structure on brain connectivity and psychological well-being

The high and increasing proportion of single-parent families is considered a risk factor associated with various childhood trauma experiences. Consequently, concerns have been raised regarding the potential long-term effects of the childhood single-parent family structure. In this study, we employed advanced magnetic resonance imaging technology, including morphometric similarity mapping, functional connectivity density, and network-based analysis, to investigate brain connectivity and behavioral differences among young adults who were raised in single-parent families. Our study also aimed to explore the relationship between these differences and childhood trauma experiences. The results showed that individuals who grew up in single-parent families exhibited higher levels of anxiety, depression, and harm-avoidant personality. The multimodal MRI analysis further showed differences in regional and network-based connectivity properties in the single-parent family group, including increased functional connectivity density in the left inferior parietal lobule, enhanced cortical structural connectivity between the left isthmus cingulate cortex and peri-calcarine cortex, and an increase in temporal functional connectivity. Moreover, elevated levels of anxiety and depression, along with heightened functional connectivity density in the left inferior parietal lobule and increased temporal functional connectivity, were found to be correlated with a greater number of childhood trauma experiences. Through analyzing multiple data patterns, our study provides objective neuropsychobiological evidence for the enduring impact of childhood single-parent family structure on psychiatric vulnerability in adulthood.

Association between degree centrality and neurocognitive impairments in patients with Schizophrenia: A Longitudinal rs-fMRI Study

BACKGROUND

Evidence indicates that patients with schizophrenia (SZ) experience significant changes in their functional connectivity during antipsychotic treatment. Despite previous reports of changes in brain network degree centrality (DC) in patients with schizophrenia, the relationship between brain DC changes and neurocognitive improvement in patients with SZ after antipsychotic treatment remains elusive.

METHODS

A total of 74 patients with acute episodes of chronic SZ and 53 age- and sex-matched healthy controls were recruited. The Positive and Negative Syndrome Scale (PANSS), Symbol Digit Modalities Test, digital span test (DST), and verbal fluency test were used to evaluate the clinical symptoms and cognitive performance of the patients with SZ. Patients with SZ were treated with antipsychotics for six weeks starting at baseline and underwent MRI and clinical interviews at baseline and after six weeks, respectively. We then divided the patients with SZ into responding (RS) and non-responding (NRS) groups based on the PANSS scores (reduction rate of PANSS ≥50%). DC was calculated and analyzed to determine its correlation with clinical symptoms and cognitive performance.

RESULTS

After antipsychotic treatment, the patients with SZ showed significant improvements in clinical symptoms, semantic fluency performance. Correlation analysis revealed that the degree of DC increase in the left anterior inferior parietal lobe (aIPL) after treatment was negatively correlated with changes in the excitement score (r = -0.256, p = 0.048, adjusted p = 0.080), but this correlation failed the multiple test correction. Patients with SZ showed a significant negative correlation between DC values in the left aIPL and DST scores after treatment, which was not observed at the baseline (r = -0.359, p = 0.005, adjusted p = 0.047). In addition, we did not find a significant difference in DC between the RS and NRS groups, neither at baseline nor after treatment.

CONCLUSIONS

The results suggested that DC changes in patients with SZ after antipsychotic treatment are correlated with neurocognitive performance. Our findings provide new insights into the neuropathological mechanisms underlying antipsychotic treatment of SZ.

Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia

BACKGROUND

Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites.

RESULTS

The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84.

CONCLUSIONS

This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

The hidden link: Investigating functional connectivity of rarely explored sub-regions of thalamus and superior temporal gyrus in Schizophrenia

Functional magnetic resonance imaging (fMRI) stands as a pivotal tool in advancing our comprehension of Schizophrenia, offering insights into functional segregations and integrations. Previous investigations employing either task-based or resting-state fMRI primarily focused on large main regions of interest (ROI), revealing the thalamus and superior temporal gyrus (STG) as prominently affected areas. Recent studies, however, unveiled the cytoarchitectural intricacies within these regions, prompting a more nuanced exploration. In this study, resting-state fMRI was conducted on 72 schizophrenic patients and 74 healthy controls to discern whether distinct thalamic nuclei and STG sub-regions exhibit varied functional integrational connectivity to main networks and to identify the most affected sub-regions in Schizophrenia. Employing seed-based analysis, six sub-ROIs - four in the thalamus and two in the STG - were selected. Our findings unveiled heightened positive functional connectivity in Schizophrenic patients, particularly toward the anterior STG (aSTG) and posterior STG (pSTG). Notably, positive connectivity emerged between the medial division of mediodorsal thalamic nuclei (MDm) and the visual network, while increased functional connectivity linked the ventral lateral nucleus of the thalamus with aSTG. This accentuated functional connectivity potentially influences these sub-regions, contributing to dysfunctions and manifesting symptoms such as language and learning difficulties alongside hallucinations. This study underscores the importance of delineating sub-regional dynamics to enhance our understanding of the nuanced neural alterations in Schizophrenia, paving the way for more targeted interventions and therapeutic approaches.

Structural brain imaging abnormalities correlate with positive symptom in schizophrenia

Accumulating evidence indicates neuroanatomical mechanisms underlying positive symptoms in schizophrenia; however, the exact structural determinants of positive symptoms remain unclear. This study aimed to investigate associations between positive symptoms and structural brain changes, including alterations in grey matter (GM) volume and cortical thickness, in patients with first-episode schizophrenia (FES). This study included 44 patients with FES and 48 healthy controls (HCs). Clinical symptoms of patients were evaluated and individual-level GM volume and cortical thickness were assessed. Patients with FES showed reduced GM volume in the right superior temporal gyrus (STG) and increased cortical thickness in the left inferior segment of the circular sulcus of the insula (S_circular_insula_inf) compared with HCs. Increased thickness of the left S_circular_insula_inf correlated positively with positive symptoms in patients with FES. Exploratory correlation analysis found that increased thickness of the left S_circular_insula_inf correlated positively with conceptual disorganization and excitement symptoms, and the right STG GM volume correlated negatively with hallucinations. This study suggests that GM abnormalities in the STG and altered cortical thickness of the S_circular_insula_inf, which were detected at the early stage of schizophrenia, may underlie positive symptoms in patients with FES.

Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data

Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.

The neurobiology of auditory and visual perceptual abnormalities in a clinical high-risk for psychosis cohort: A pilot morphometric magnetic resonance imaging study

Our goal was to examine the neurobiology of auditory and visual perceptual abnormalities in individuals at clinical high-risk for psychosis (CHR) using morphometric magnetic resonance imaging (MRI). We enrolled 72 CHR subjects as delineated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). Greater severity of visual perceptual abnormalities was associated with larger volumes in all regions tested (amygdala, hippocampus, and occipital cortex), while no relationships were observed between auditory perceptual abnormalities and brain volumes. These data support findings that while perceptual abnormalities may share a central set of neurobiological mechanisms, each type may also have distinct pathogeneses.

BNST and amygdala connectivity are altered during threat anticipation in schizophrenia

In schizophrenia, impairments in affect are prominent and anxiety disorders are prevalent. Neuroimaging studies of fear and anxiety in schizophrenia have focused on the amygdala and show alterations in connectivity. Emerging evidence suggests that the bed nucleus of the stria terminalis (BNST) also plays a critical role in anxiety, especially during anticipation of an unpredictable threat; however, previous studies have not examined the BNST in schizophrenia. In the present study, we examined BNST function and connectivity in people with schizophrenia (n = 31; n = 15 with comorbid anxiety) and controls (n = 15) during anticipation of unpredictable and predictable threat. A secondary analysis tested for differences in activation and connectivity of the central nucleus of the amygdala (CeA), which has also been implicated in threat anticipation. Analyses tested for group differences in both activation and connectivity during anticipation of unpredictable threat and predictable threat (p < .05). Relative to controls, individuals with schizophrenia showed stronger BNST-middle temporal gyrus (MTG) connectivity during unpredictable threat anticipation and stronger BNST-MTG and BNST-dorsolateral prefrontal connectivity during predictable threat anticipation. Comparing subgroups of individuals with schizophrenia and a comorbid anxiety disorder (SZ+ANX) to those without an anxiety disorder (SZ-ANX) revealed broader patterns of altered connectivity. During unpredictable threat anticipation, the SZ+ANX group had stronger BNST connectivity with regions of the salience network (insula, dorsal anterior cingulate cortex). During predictable threat anticipation, the SZ+ANX group had stronger BNST connectivity with regions associated with fear processing (insula, extended amygdala, prefrontal cortex). A secondary CeA analysis revealed a different pattern; the SZ+ANX group had weaker CeA connectivity across multiple brain regions during threat anticipation compared to the SZ-ANX group. These findings provide novel evidence for altered functional connectivity during threat anticipation in schizophrenia, especially in individuals with comorbid anxiety.

Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia

OBJECTIVE

Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).

METHODS

We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.

RESULTS

Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.

CONCLUSION

We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.

SIGNIFICANCE

The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.

Language-Related Neurophysiological Deficits in Schizophrenia

Schizophrenia is a severe psychiatric disorder that affects all aspects of one's life with several cognitive and social dysfunctions. However, there is still no objective and universal index for diagnosis and treatment of this disease. Many researchers have studied language processing in schizophrenia since most of the patients show symptoms related to language processing, such as thought disorder, auditory verbal hallucinations, or delusions. Electroencephalography (EEG) and magnetoencephalography (MEG) with millisecond order high temporal resolution, have been applied to reveal the abnormalities in language processing in schizophrenia. The aims of this review are (a) to provide an overview of recent findings in language processing in schizophrenia with EEG and MEG using neurophysiological indices, providing insights into underlying language related pathophysiological deficits in this disease and (b) to emphasize the advantage of EEG and MEG in research on language processing in schizophrenia.

Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence

Auditory hallucinations (AHs) are one of the most distressing symptoms of schizophrenia (SZ) and are often resistant to medication. Imaging studies of individuals with SZ show hyperactivation of the default mode network (DMN) and the superior temporal gyrus (STG). Studies in SZ show DMN hyperconnectivity and reduced anticorrelation between DMN and the central executive network (CEN). DMN hyperconnectivity has been associated with positive symptoms such as AHs while reduced DMN anticorrelations with cognitive impairment. Using real-time fMRI neurofeedback (rt-fMRI-NFB) we trained SZ patients to modulate DMN and CEN networks. Meditation is effective in reducing AHs in SZ and to modulate brain network integration and increase DMN anticorrelations. Consequently, patients were provided with meditation strategies to enhance their abilities to modulate DMN/CEN. Results show a reduction of DMN hyperconnectivity and increase in DMNCEN anticorrelation. Furthermore, the change in individual DMN connectivity significantly correlated with reductions in AHs. This is the first time that meditation enhanced through rt-fMRI-NFB is used to reduce AHs in SZ. Moreover, it provides the first empirical evidence for a direct causal relation between meditation enhanced rt-fMRI-NFB modulation of DMNCEN activity and post-intervention modulation of resting state networks ensuing in reductions in frequency and severity of AHs.

Altered resting-state functional connectivity in young children at familial high risk for psychotic illness: A preliminary study

Multiple lines of evidence suggest that illness development in schizophrenia and other psychotic disorders predates the first psychotic episode by many years. In this study, we examined a sample of 15 pre-adolescent children, ages 7 through 12 years, who are at familial high-risk (FHR) because they have a parent or sibling with a history of schizophrenia or related psychotic disorder. Using multi-voxel pattern analysis (MVPA), a data-driven fMRI analysis, we assessed whole-brain differences in functional connectivity in the FHR sample as compared to an age- and sex-matched control (CON) group of 15 children without a family history of psychosis. MVPA analysis yielded a single cluster in right posterior superior temporal gyrus (pSTG/BA 22) showing significant group-differences in functional connectivity. Post-hoc characterization of this cluster through seed-to-voxel analysis revealed mostly reduced functional connectivity of the pSTG seed to a set of language and default mode network (DMN) associated brain regions including Heschl's gyrus, inferior temporal gyrus extending into fusiform gyrus, (para)hippocampus, thalamus, and a cerebellar cluster encompassing mainly Crus I/II. A height-threshold of whole-brain p < .001 (two-sided), and FDR-corrected cluster-threshold of p < .05 (non-parametric statistics) was used for post-hoc characterization. These findings suggest that abnormalities in functional communication in a network encompassing right STG and associated brain regions are present before adolescence in at-risk children and may be a risk marker for psychosis. Subsequent changes in this functional network across development may contribute to either disease manifestation or resilience in children with a familial vulnerability for psychosis.

Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis

There is growing evidence that psychosis is characterized by brain network abnormalities. Analyzing morphological abnormalities with T1-weighted structural MRI may be limited in discovering the extent of deviations in cortical associations. We assess whether structural associations of either cortical white-gray contrast (WGC) or cortical thickness (CT) allow for a better understanding of brain structural relationships in first episode of psychosis (FEP) patients. Principal component and structural covariance analyses were applied to WGC and CT derived from T1-weighted MRI for 116 patients and 88 controls, to explore sets of brain regions that showed group differences, and associations with symptom severity and cognitive ability in patients. We focused on 2 principal components: one encompassed primary somatomotor regions, which showed trend-like group differences in WGC, and the second included heteromodal cortices. Patients' component scores were related to general psychopathology for WGC, but not CT. Structural covariance analyses with WGC revealed group differences in pairwise correlations across widespread brain regions, mirroring areas derived from PCA. More group differences were uncovered with WGC compared with CT. WGC holds potential as a proxy measure of myelin from commonly acquired T1-weighted MRI and may be sensitive in detecting systems-level aberrations in early psychosis, and relationships with clinical/cognitive profiles.

Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity

Background: Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. Methods: We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Results: Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Conclusions: Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.

Mismatch Negativity and Cortical Thickness in Patients With Schizophrenia and Bipolar Disorder

INTRODUCTION

Mismatch negativity (MMN) is a measure of automatic neurophysiological brain processes for detecting unexpected sensory stimuli. This study investigated MMN reduction in patients with schizophrenia and bipolar disorder and examined whether cortical thickness is associated with MMN, for exploratory purposes.

METHODS

Electroencephalograms were recorded in 38 patients with schizophrenia, 37 patients with bipolar disorder, and 32 healthy controls (HCs) performing a passive auditory oddball paradigm. All participants underwent T1 structural magnetic resonance imaging scanning to investigate the cortical thickness of MMN-generating regions. Average MMN amplitudes from the frontocentral electrodes were analyzed.

RESULTS

Patients with schizophrenia and bipolar disorder exhibited significantly reduced MMN amplitude compared with HCs. In bipolar disorder, we found intermediate MMN amplitude among the groups. Average MMN and cortical thickness of the right superior temporal gyrus (STG) were significantly negatively correlated in patients with schizophrenia. In patients with bipolar disorder, average MMN was significantly correlated with cortical thickness of the left anterior cingulate cortex and the right STG. MMN showed negative correlations with social and occupational functioning in schizophrenia, and with the Korean auditory verbal learning test for delayed recall in bipolar disorder.

CONCLUSIONS

MMN reduction was associated with cortical thinning in frontal and temporal areas in patients, particularly with an auditory verbal hallucination-related region in schizophrenia and emotion-related regions in bipolar disorder. MMN was associated with functional outcomes in schizophrenia, whereas it was associated with neurocognition in bipolar disorder.

Reduction in Alpha Peak Frequency and Coherence on Quantitative Electroencephalography in Patients with Schizophrenia

BACKGROUND

The aim of the study was to examine the characteristics of alpha wave peak frequency, power, and coherence in patients with schizophrenia.

METHODS

Thirty-one patients with schizophrenia and age- and sex-matched subjects with no psychopathology were enrolled. All study participants underwent quantitative electroencephalography (QEEG). Alpha-related values, including peak frequency, power, and coherence, were evaluated.

RESULTS

Alpha peak frequency on the Oz area was slower in the schizophrenia group than that in the control group. However, no differences in absolute or relative power were observed between the two groups. Significant reductions in absolute and relative coherence were observed at the C3-C4 and T3-T4 nodes in the patients with schizophrenia. Relative coherence was reduced at the P3-P4 nodes.

CONCLUSION

This study focused on alpha variables detected in QEEG as intrinsic values to distinguish schizophrenia from a healthy control. The results suggest decreased alpha peak frequency of the occipital lobe and decreased coherence between the two hemispheres in patients with schizophrenia. A further study could elucidate the causal relationship and biological meaning of the variations in alpha waves in patients with schizophrenia.

The function and failure of sensory predictions

Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions. We review studies supporting a prediction-failure hypothesis of perceptual and cognitive disturbances. We also outline neural correlates underlying prediction function and failure, highlighting similarities across the visual, auditory, and somatosensory systems. In linking basic psychophysical and psychophysiological evidence of visual, auditory, and somatosensory prediction failures to neuropsychiatric symptoms, our review furthers our understanding of disease mechanisms.

Connectomic markers of symptom severity in sport-related concussion: Whole-brain analysis of resting-state fMRI

Concussion is associated with significant adverse effects within the first week post-injury, including physical complaints and altered cognition, sleep and mood. It is currently unknown whether these subjective disturbances have reliable functional brain correlates. Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to measure functional connectivity of individuals after traumatic brain injury, but less is known about the relationship between functional connectivity and symptom assessments after a sport concussion. In this study, rs-fMRI was used to evaluate whole-brain functional connectivity for seventy (70) university-level athletes, including 35 with acute concussion and 35 healthy matched controls. Univariate analyses showed that greater symptom severity was mainly associated with lower pairwise connectivity in frontal, temporal and insular regions, along with higher connectivity in a sparser set of cerebellar regions. A novel multivariate approach also extracted two components that showed reliable covariation with symptom severity: (1) a network of frontal, temporal and insular regions where connectivity was negatively correlated with symptom severity (replicating the univariate findings); and (2) a network with anti-correlated elements of the default-mode network and sensorimotor system, where connectivity was positively correlated with symptom severity. These findings support the presence of connectomic signatures of symptom complaints following a sport-related concussion, including both increased and decreased functional connectivity within distinct functional brain networks.

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Analyzed relationship between resting brain function and symptoms of concussion

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Whole-brain analysis, using both univariate and multivariate methods

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Symptoms associated with lower connectivity for frontal/temporal/insular network

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Symptoms associated with higher connectivity for default-mode/sensorimotor network

The Abnormal Functional Connectivity between the Hypothalamus and the Temporal Gyrus Underlying Depression in Alzheimer's Disease Patients

Hypothalamic communication with the rest of the brain is critical for accomplishing a wide variety of physiological and psychological functions, including the maintenance of neuroendocrine circadian rhythms and the management of affective processes. Evidence has shown that major depressive disorder (MDD) patients exhibit increased functioning of the hypothalamic-pituitary-adrenal (HPA) axis. Neurofibrillary tangles are also found in the hypothalamus of Alzheimer’s disease (AD) patients, and AD patients exhibit abnormal changes in the HPA. However, little is known of how the hypothalamus interacts with other brain regions in AD patients with depression (D-AD). Functional connectivity (FC) analysis explores the connectivity between brain regions that share functional properties. Here, we used resting-state (rs) magnetic resonance imaging (MRI) technology and the FC method to measure hypothalamic connectivity across the whole brain in 22 D-AD patients and 21 non-depressed AD patients (nD-AD). Our results showed that D-AD patients had reduced FC among the hypothalamus, the right middle temporal gyrus (MTG) and the right superior temporal gyrus (STG) compared with the FC of nD-AD patients, suggesting that the abnormal FC between the hypothalamus and the temporal lobe may play a key role in the pathophysiology of depression in AD patients.

Abnormal Grey Matter Arteriolar Cerebral Blood Volume in Schizophrenia Measured With 3D Inflow-Based Vascular-Space-Occupancy MRI at 7T

Metabolic dysfunction and microvascular abnormality may contribute to the pathogenesis of schizophrenia. Most previous studies of cerebral perfusion in schizophrenia measured total cerebral blood volume (CBV) and cerebral blood flow (CBF) in the brain, which reflect the ensemble signal from the arteriolar, capillary, and venular compartments of the microvasculature. As the arterioles are the most actively regulated blood vessels among these compartments, they may be the most sensitive component of the microvasculature to metabolic disturbances. In this study, we adopted the inflow-based vascular-space-occupancy (iVASO) MRI approach to investigate alterations in the volume of small arterial (pial) and arteriolar vessels (arteriolar cerebral blood volume [CBVa]) in the brain of schizophrenia patients. The iVASO approach was extended to 3-dimensional (3D) whole brain coverage, and CBVa was measured in the brains of 12 schizophrenia patients and 12 matched controls at ultra-high magnetic field (7T). Significant reduction in grey matter (GM) CBVa was found in multiple areas across the whole brain in patients (relative changes of 14%-51% and effect sizes of 0.7-2.3). GM CBVa values in several regions in the temporal cortex showed significant negative correlations with disease duration in patients. GM CBVa increase was also found in a few brain regions. Our results imply that microvascular abnormality may play a role in schizophrenia, and suggest GM CBVa as a potential marker for the disease. Further investigation is needed to elucidate whether such effects are due to primary vascular impairment or secondary to other causes, such as metabolic dysfunction.

Structural alterations of the superior temporal gyrus in schizophrenia: Detailed subregional differences

BACKGROUND

Reduced gray matter volumes in the superior temporal gyrus (STG) have been reported in patients with schizophrenia. Such volumetric abnormalities might denote alterations in cortical thickness, surface area, local gyrification or all of these factors. The STG can be anatomically divided into five subregions using automatic parcellation in FreeSurfer: lateral aspect of the STG, anterior transverse temporal gyrus of Heschl gyrus (HG), planum polare (PP) of the STG, planum temporale (PT) of the STG and transverse temporal sulcus.

METHODS

We acquired magnetic resonance imaging (MRI) 3T scans from 40 age- and sex-matched patients with schizophrenia and 40 healthy subjects, and the scans were automatically processed using FreeSurfer. General linear models were used to assess group differences in regional volumes and detailed thickness, surface area and local gyrification.

RESULTS

As expected, patients with schizophrenia had significantly smaller bilateral STG volumes than healthy subjects. Of the five subregions in the STG, patients with schizophrenia showed significantly and marginally reduced volumes in the lateral aspect of the STG and PT of the STG bilaterally compared with healthy subjects. The volumetric alteration in bilateral lateral STG was derived from both the cortical thickness and surface area but not local gyrification. There was no significant laterality of the alteration in the lateral STG between patients and controls and no correlation among the structures and clinical characteristics.

CONCLUSIONS

These findings suggest that of five anatomical subregions in the STG, the lateral STG is one of the most meaningful regions for brain pathophysiology in schizophrenia.

Approaches to Capture Variance Differences in Rest fMRI Networks in the Spatial Geometric Features: Application to Schizophrenia

Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA). IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls). We postulate, that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior, and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly higher-order measures in resting state networks to better understand diseases such as schizophrenia. It furthers comprehension of how spatial characteristics can highlight previously unexplored differences between populations such as schizophrenia patients and healthy controls.

Coordinate-based (ALE) meta-analysis of brain activation in patients with fibromyalgia

There are an increasing number of neuroimaging studies that allow a better understanding of symptoms, neural correlates and associated conditions of fibromyalgia. However, the results of these studies are difficult to compare, as they include a heterogeneous group of patients, use different stimulation paradigms, tasks, and the statistical evaluation of neuroimaging data shows high variability. Therefore, this meta-analytic approach aimed at evaluating potential alterations in neuronal brain activity or structure related to pain processing in fibromyalgia syndrome (FMS) patients, using quantitative coordinate-based "activation likelihood estimation" (ALE) meta-analysis. 37 FMS papers met the inclusion criteria for an ALE analysis (1,264 subjects, 274 activation foci). A pooled ALE analysis of different modalities of neuroimaging and additional analyses according functional and structural changes indicated differences between FMS patients and controls in the insula, amygdala, anterior/mid cingulate cortex, superior temporal gyrus, the primary and secondary somatosensory cortex, and lingual gyrus. Our analysis showed consistent results across FMS studies with potential abnormalities especially in pain-related brain areas. Given that similar alterations have already been demonstrated in patients with other chronic pain conditions and the lack of adequate control groups of chronic pain subjects in most FMS studies, it is not clear however, whether these findings are associated with chronic pain in general or are unique features of patients with FMS. Hum Brain Mapp 37:1749-1758, 2016. © 2016 Wiley Periodicals, Inc.

Protein Expression of Proteasome Subunits in Elderly Patients with Schizophrenia

The ubiquitin proteasome system (UPS) is a major regulator of protein processing, trafficking, and degradation. While protein ubiquitination is utilized for many cellular processes, one major function of this system is to target proteins to the proteasome for degradation. In schizophrenia, studies have found UPS transcript abnormalities in both blood and brain, and we have previously reported decreased protein expression of ubiquitin-associated proteins in brain. To test whether the proteasome is similarly dysregulated, we measured the protein expression of proteasome catalytic subunits as well as essential subunits from proteasome regulatory complexes in 14 pair-matched schizophrenia and comparison subjects in superior temporal cortex. We found decreased expression of Rpt1, Rpt3, and Rpt6, subunits of the 19S regulatory particle essential for ubiquitin-dependent degradation by the proteasome. Additionally, the α subunit of the 11S αβ regulatory particle, which enhances proteasomal degradation of small peptides and unfolded proteins, was also decreased. Haloperidol-treated rats did not have altered expression of these subunits, suggesting the changes we observed in schizophrenia are likely not due to chronic antipsychotic treatment. Interestingly, expression of the catalytic subunits of both the standard and immunoproteasome were unchanged, suggesting the abnormalities we observed may be specific to the complexed state of the proteasome. Aging has significant effects on the proteasome, and several subunits (20S β2, Rpn10, Rpn13, 11Sβ, and 11Sγ) were significantly correlated with subject age. These data provide further evidence of dysfunction of the ubiquitin-proteasome system in schizophrenia, and suggest that altered proteasome activity may be associated with the pathophysiology of this illness.

Subcomponents of brain T2* relaxation in schizophrenia, bipolar disorder and siblings: A Gradient Echo Plural Contrast Imaging (GEPCI) study

Investigating brain tissue T2* relaxation properties in vivo can potentially guide the uncovering of neuropathology in psychiatric illness, which is traditionally examined post mortem. We use an MRI-based Gradient Echo Plural Contrast Imaging (GEPCI) technique that produces inherently co-registered images allowing quantitative assessment of tissue cellular and hemodynamic properties. Usually described as R2* (=1/T2*) relaxation rate constant, recent developments in GEPCI allow the separation of cellular-specific (R2*C) and hemodynamic (BOLD) contributions to the MRI signal decay. We characterize BOLD effect in terms of tissue concentration of deoxyhemoglobin, i.e. CDEOXY, which reflects brain activity. 17 control (CON), 17 bipolar disorder (BPD), 16 schizophrenia (SCZ), and 12 unaffected schizophrenia sibling (SIB) participants were scanned and post-processed using GEPCI protocols. A MANOVA of 38gray matter regions ROIs showed significant group effects for CDEOXY but not for R2*C. In the three non-control groups, 71-92% of brain regions had increased CDEOXY. Group effects were observed in the superior temporal cortex and the thalamus. Increased superior temporal cortex CDEOXY was found in SCZ (p=0.01), BPD (p=0.01) and SIB (p=0.02), with bilateral effects in SCZ and only left hemisphere effects in BPD and SIB. Thalamic CDEOXY abnormalities were observed in SCZ (p=0.003), BPD (p=0.03) and SIB (p=0.02). Our results suggest that increased activity in certain brain regions is part of the underlying pathophysiology of specific psychiatric disorders. High CDEOXY in the superior temporal cortex suggests abnormal activity with auditory, language and/or social cognitive processing. Larger studies are needed to clarify the clinical significance of relaxometric abnormalities.

A Hierarchical Generative Framework of Language Processing: Linking Language Perception, Interpretation, and Production Abnormalities in Schizophrenia

Language and thought dysfunction are central to the schizophrenia syndrome. They are evident in the major symptoms of psychosis itself, particularly as disorganized language output (positive thought disorder) and auditory verbal hallucinations (AVHs), and they also manifest as abnormalities in both high-level semantic and contextual processing and low-level perception. However, the literatures characterizing these abnormalities have largely been separate and have sometimes provided mutually exclusive accounts of aberrant language in schizophrenia. In this review, we propose that recent generative probabilistic frameworks of language processing can provide crucial insights that link these four lines of research. We first outline neural and cognitive evidence that real-time language comprehension and production normally involve internal generative circuits that propagate probabilistic predictions to perceptual cortices - predictions that are incrementally updated based on prediction error signals as new inputs are encountered. We then explain how disruptions to these circuits may compromise communicative abilities in schizophrenia by reducing the efficiency and robustness of both high-level language processing and low-level speech perception. We also argue that such disruptions may contribute to the phenomenology of thought-disordered speech and false perceptual inferences in the language system (i.e., AVHs). This perspective suggests a number of productive avenues for future research that may elucidate not only the mechanisms of language abnormalities in schizophrenia, but also promising directions for cognitive rehabilitation.

Changes in thalamic connectivity in the early and late stages of amnestic mild cognitive impairment: a resting-state functional magnetic resonance study from ADNI

We used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in the thalamus functional connectivity in early and late stages of amnestic mild cognitive impairment. Data of 25 late stages of amnestic mild cognitive impairment (LMCI) patients, 30 early stages of amnestic mild cognitive impairment (EMCI) patients and 30 well-matched healthy controls (HC) were analyzed from the Alzheimer’s disease Neuroimaging Initiative (ADNI). We focused on the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions. Compared to healthy controls, we found functional connectivity between the left/right thalamus and a set of brain areas was decreased in LMCI and/or EMCI including right fusiform gyrus (FG), left and right superior temporal gyrus, left medial frontal gyrus extending into supplementary motor area, right insula, left middle temporal gyrus (MTG) extending into middle occipital gyrus (MOG). We also observed increased functional connectivity between the left/right thalamus and several regions in LMCI and/or EMCI including left FG, right MOG, left and right precuneus, right MTG and left inferior temporal gyrus. In the direct comparison between the LMCI and EMCI groups, we obtained several brain regions showed thalamus-seeded functional connectivity differences such as the precentral gyrus, hippocampus, FG and MTG. Briefly, these brain regions mentioned above were mainly located in the thalamo-related networks including thalamo-hippocampus, thalamo-temporal, thalamo-visual, and thalamo-default mode network. The decreased functional connectivity of the thalamus might suggest reduced functional integrity of thalamo-related networks and increased functional connectivity indicated that aMCI patients could use additional brain resources to compensate for the loss of cognitive function. Our study provided a new sight to understand the two important states of aMCI and revealed resting-state fMRI is an appropriate method for exploring pathophysiological changes in aMCI.

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

Functional MRI of sentence-level language comprehension in schizophrenia: a coordinate-based analysis

Numerous authors have hypothesised that abnormal pathways for language play a key role in the pathophysiology of schizophrenia, a notion that is supported by structural imaging and post-mortem findings especially in patients with thought disorder and auditory verbal hallucinations. Recently, an increasing number of functional magnetic resonance imaging (fMRI) investigations addressed language comprehension schizophrenia. We present a systematic review of the fMRI-studies on sentence- and text-level language comprehension in schizophrenia. 13 studies met the inclusion criteria. Additional studies specifically addressed language lateralization. Coordinates for differential contrasts for healthy controls>patients reported in these studies indicate that the left fronto-temporal language network is altered in schizophrenia. 33 out of the 51 reported coordinates are located in the left hemisphere. Overactivation in schizophrenia extends into premotor areas and is about equally divided among the left and right hemispheres. Several negative studies indicate heterogeneity within schizophrenia, which could possibly be related to the severity of thought disorder or auditory verbal hallucinations of patients. Activation changes related to thought disorder within schizophrenia (n=4 studies) include the inferior frontal and superior temporal gyri and are moderately lateralized to the left hemisphere. Although current fMRI literature is still insufficient to draw decisive conclusions, results point towards functionally altered pathways for language in schizophrenia. This notion is also plausible from the viewpoint of psychopathology especially since hallmark symptoms of the disease, thought disorder, auditory verbal hallucinations and alogia, are expressed in terms of language or represent abnormalities of language function.

Neural correlates of the relationship between discourse coherence and sensory monitoring in schizophrenia

Unusual language use is a core feature of psychosis, but the nature and significance of this are not understood. In particular, thought disorder in schizophrenia (SZ) is characterized by markedly bizarre speech, but the cognitive components that contribute to this and the brain correlates of these components are unknown. A number of studies have demonstrated language abnormalities in single word processing, but few have examined speech in SZ at the discourse level. This has been at least partly due to the difficulty in quantifying content of discourse. Recently, methods in computational linguistics have been found to be useful for detecting differences in semantic coherence during discourse between different clinical groups. We build on this work by demonstrating how these methods can be combined with funtional magnetic resonance imaging (fMRI) in order to tease apart factors that underlie free discourse and its deviations, and how they relate to brain activity. Eleven volunteers with SZ and eleven controls participated in an interview during which they were asked to talk as much as they could about 'religious belief'. These same participants underwent fMRI during a word monitoring task, during which modality of monitoring was manipulated by varying the congruence of auditory and visual stimuli. Semantic coherence scores, measured from free discourse, were examined for their relationship to brain activations during fMRI. In healthy controls, regions associated with executive function were related to coherence. In persons with SZ, coherence was mainly related to auditory and visual regions, depending on the modality of monitoring, but superior/middle temporal cortex related to coherence regardless of task. These findings are consistent with existing evidence for a role of superior temporal cortex in thought disorder, and demonstrate that computational measures of semantic content capture objective measures of coherence in speech that can be usefully related to underlying neurophysiological processes.

Altered auditory processing in frontal and left temporal cortex in 22q11.2 deletion syndrome: a group at high genetic risk for schizophrenia

In order to investigate electroencephalographic (EEG) biomarkers of auditory processing for schizophrenia, we studied a group with a well known high-risk profile: patients with 22q11.2 deletion syndrome (22q11 DS) have a 30% risk of developing schizophrenia during adulthood. We performed high-density EEG source imaging to measure auditory gating of the P50 component of the evoked potential and middle to late latency auditory processing in 21 participants with the 22q11.2 deletion and 17 age-matched healthy controls. While we found no indication of altered P50 suppression in 22q11 DS, we observed marked differences for the first N1 component with increased amplitudes on central electrodes, corresponding to increased activations in dorsal anterior cingulate and medial frontal cortex. We also found a left lateralized reduction of activation of primary and secondary auditory cortex during the second N1 (120ms) and the P2 component in 22q11 DS. Our results show that sensory gating and activations until 50ms were preserved in 22q11 DS, while impairments appear at latencies that correspond to higher order auditory processing. While the increased activation of cingulate and medial frontal cortex could reflect developmental changes in 22q11 DS, the reduced activity seen in left auditory cortex might serve as a biomarker for the development of schizophrenia, if confirmed by longitudinal research protocols.

Greater neuronal responses during automatic semantic processing in schizophrenia

A core feature of schizophrenia is a disturbance of associative processes. To date, no functional MRI studies have investigated semantic priming in schizophrenia under experimental conditions that measure automatic, as opposed to strategic, processing. The present study's focus was to investigate hemodynamic responses during indirect semantic priming at a short stimulus onset asynchrony (i.e., 350 ms), conditions which are considered to be a particularly sensitive measure of automatic spreading activation during semantic processing and of the associative disturbances in schizophrenia. Seventeen individuals with DSM-IV, schizophrenia and 15 comparison participants underwent functional scanning while performing a lexical decision task on directly related, indirectly related, unrelated, and word/nonword pairs. A random-effects region of interest analysis within a priori temporal and frontal regions was performed. Whereas comparison individuals exhibited hemodynamic suppression in response to priming, individuals with schizophrenia exhibited hemodynamic enhancement. Relative to the comparison group, these enhancements were observed in the left fusiform and superior temporal gyri for indirectly related word pairs relative to unrelated pairs. Greater priming-related responses within temporal regions may reflect increased and prolonged automatic spreading activation during semantic processing in schizophrenia.

Structural imaging techniques in schizophrenia

OBJECTIVE

The aim of this overview study is to translate the technical terminology regarding structural Magnetic Resonance Imaging (sMRI) post-processing analysis into a clinical clear description.

METHOD

We resumed and explained the most popular post-processing methods for structural MRI (sMRI) data applied in psychiatry and their main contributions to the comprehension of the biological basis of schizophrenia.

RESULTS

The region-of-interest (ROI) technique allows to investigate specific brain region size by manual tracing; it is anatomically precise and requires a priori hypothesis, but also it is time-consuming and operator-dependent. The voxel-based morphometry (VBM) detects gray matter density across the whole brain by comparing voxel to voxel; it is operator-independent, does not require a priori hypothesis, and is relatively fast; however, it is limited by multiple comparisons and poor anatomical definition. Finally, computational neuroanatomical analyses have recently been applied to automatically discriminate subjects with schizophrenia from healthy subjects on the basis of MRI images.

CONCLUSION

Structural MRI represents a useful tool in understanding the biological underpinnings of schizophrenia and in planning focused interventions, thus assisting clinicians especially in the early phases of the illness.

Distinct structural alterations independently contributing to working memory deficits and symptomatology in paranoid schizophrenia

Schizophrenia is considered a brain disease with a quite heterogeneous clinical presentation. Studies in schizophrenia have yielded a wide array of correlations between structural and functional brain changes and clinical and cognitive symptoms. Reductions of grey matter volume (GMV) in the prefrontal and temporal cortex have been described which are crucial for the development of positive and negative symptoms and impaired working memory (WM). Associations between GMV reduction and positive and negative symptoms as well as WM impairment were assessed in schizophrenia patients (symptomatology in 34, WM in 26) and compared to healthy controls (36 total, WM in 26). GMV was determined by voxel-based morphometry and its relation to positive and negative symptoms as well as WM performance was assessed. In schizophrenia patients, reductions of GMV were evident in anterior cingulate cortex, ventrolateral prefrontal cortex (VLPFC), superior temporal cortex, and insula. GMV reductions in the superior temporal gyrus (STG) were associated with positive symptom severity as well as WM impairment. Furthermore, the absolute GMV of VLPFC was strongly related to negative symptoms. These predicted WM performance as well as processing speed. The present results support the assumption of two distinct pathomechanisms responsible for impaired WM in schizophrenia: (1) GMV reductions in the VLPFC predict the severity of negative symptoms. Increased negative symptoms in turn are associated with a slowing down of processing speed and predict an impaired WM. (2) GMV reductions in the temporal and mediofrontal cortex are involved in the development of positive symptoms and impair WM performance, too.

The biology of schizophrenia

Schizophrenia is an illness where the clinical signs and symptoms, course, and cognitive characteristics are well described. Successful pharmacological treatments do exist, even though they are likely palliative. However, this broad knowledge base has not yet led to the identification of its pathophysiology or etiology The risk factors for schizophrenia are most prominently genetic and scientists anticipate that contributions from the new genetic information in the human genome will help progress towards discovering a disease mechanism. Brain-imaging techniques have opened up the schizophrenic brain for direct inquiries, in terms of structure, neurochemisiry, and function. New proposals for diagnosis include grouping schizophrenia together with schizophrenia-related personality disorders into the same disease entity, and calling this schizophrenia spectrum disorder. New hypotheses of pathophysiology do not overlook dopamine as playing a major role, but do emphasize the participation of integrative neural systems in the expression of the illness and of the limbic system in generating symptoms. Critical observations for future discovery are likely to arise from molecular genetics, combined with hypothesis-generating experiments using brain imaging and human postmortem tissue.

Is neuregulin 1 involved in determining cerebral volumes in schizophrenia? Preliminary results showing a decrease in superior temporal gyrus volume

BACKGROUND/AIMS

Reduced left superior temporal gyrus (STG) volume is one of the most replicated imaging findings in schizophrenia. However, it remains unclear whether genes play any role in our understanding of such structural alteration. It has been proposed that Neuregulin 1 (NRG1) might be a promising gene involved in schizophrenia, because of its role in neurodevelopment and neuroplasticity. In this study, the association between NRG1 and STG anatomy in patients with schizophrenia was explored for the first time.

METHODS

We investigated a 1-year treated prevalence cohort of patients with schizophrenia in contact with the South Verona Community-Based Mental Health Service. A blood sample was collected for DNA extraction and brain structure was assessed with an MRI scan. A total of 27 subjects with schizophrenia underwent both assessments and were included in the study.

RESULTS

We investigated the association between the polymorphism SNP8NRG222662 (rs4623364) of NRG1 and volume of the STG. We found that patients homozygous for the C allele had reduced left STG gray and white matter volumes in comparison to those homozygous for the G allele (p < 0.01 and p < 0.001, respectively).

CONCLUSIONS

This exploratory study suggests that NRG1 may be involved in determining STG size in schizophrenia, and may play a role in the neurogenetic basis of the language disturbances seen in this disorder. However, due to our small sample size, the results should be regarded as preliminary and replicated in a larger sample.

Differences in resting-state functional magnetic resonance imaging functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives

BACKGROUND

Schizophrenia and bipolar disorder share overlapping symptoms and genetic etiology. Functional brain dysconnectivity is seen in both disorders.

METHODS

We compared 70 schizophrenia and 64 psychotic bipolar probands, their respective unaffected first-degree relatives (n = 70, and n = 52), and 118 healthy subjects, all group age-, gender-, and ethnicity-matched. We used functional network connectivity analysis to measure differential connectivity among 16 functional magnetic resonance imaging resting state networks. First, we examined connectivity differences between probands and control subjects. Next, we probed these dysfunctional connections in relatives for potential endophenotypes. Network connectivity was then correlated with Positive and Negative Syndrome Scale (PANSS) scores to reveal clinical relationships.

RESULTS

Three different network pairs were differentially connected in probands (false-discovery rate corrected q < .05) involving five individual resting-state networks: (A) fronto/occipital, (B) anterior default mode/prefrontal, (C) meso/paralimbic, (D) fronto-temporal/paralimbic, and (E) sensory-motor. One abnormal pair was unique to schizophrenia, (C-E), one unique to bipolar, (C-D), and one (A-B) was shared. Two of these three combinations (A-B, C-E) were also abnormal in bipolar relatives but none was normal in schizophrenia relatives (nonsignificant trend for C-E). The paralimbic circuit (C-D), which uniquely distinguished bipolar probands, contained multiple mood-relevant regions. Network relationship C-D correlated significantly with PANSS negative scores in bipolar probands, and A-B with PANSS positive and general scores in schizophrenia.

CONCLUSIONS

Schizophrenia and psychotic bipolar probands share several abnormal resting state network connections, but there are also unique neural network underpinnings between disorders. We identified specific connections that might also be candidate psychosis endophenotypes.

A selective review of multimodal fusion methods in schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

Structural angle and power images reveal interrelated gray and white matter abnormalities in schizophrenia

We present a feature extraction method to emphasize the interrelationship between gray and white matter and identify tissue distribution abnormalities in schizophrenia. This approach utilizes novel features called structural phase and magnitude images. The phase image indicates the relative contribution of gray and white matter, and the magnitude image reflects the overall tissue concentration. Three different analyses are applied to the phase and magnitude images obtained from 120 healthy controls and 120 schizophrenia patients. First, a single-subject subtraction analysis is computed for an initial evaluation. Second, we analyze the extracted features using voxel based morphometry (VBM) to detect voxelwise group differences. Third, source based morphometry (SBM) analysis was used to determine abnormalities in structural networks that co-vary in a similar way. Six networks were identified showing significantly lower white-to-gray matter in schizophrenia, including thalamus, right precentral-postcentral, left pre/post-central, parietal, right cuneus-frontal, and left cuneus-frontal sources. Interestingly, some networks look similar to functional patterns, such as sensory-motor and vision. Our findings demonstrate that structural phase and magnitude images can naturally and efficiently summarize the associated relationship between gray and white matter. Our approach has wide applicability for studying tissue distribution differences in the healthy and diseased brain.

Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence

The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.

Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model

Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICA", which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms.

Association of schizophrenia in 22q11.2 deletion syndrome and gray matter volumetric deficits in the superior temporal gyrus

OBJECTIVE

Individuals with 22q11.2 deletion syndrome are known to be at high risk of developing schizophrenia. Previous imaging studies have provided limited data on the relation of schizophrenia expression in 22q11.2 deletion syndrome to specific regional brain volumetric changes. The authors hypothesized that the main structural brain finding associated with schizophrenia expression in 22q11.2 deletion syndrome, as for schizophrenia in the general population, would be gray matter volumetric deficits, especially in the temporal lobes.

METHOD

MR brain images from 29 patients with 22q11.2 deletion syndrome and schizophrenia and 34 comparison subjects with 22q11.2 deletion syndrome and no history of psychosis were analyzed using a voxel-based morphometry method that also yielded volumes for related region-of-interest analyses. The authors compared data from the two groups using an analysis of covariance model correcting for total intracranial volume, age, sex, IQ, and history of congenital cardiac defects. The false discovery rate threshold was set at 0.05 to account for multiple comparisons.

RESULTS

Voxel-based morphometry analyses identified significant gray matter reductions in the left superior temporal gyrus (Brodmann's area 22) in the schizophrenia group. There were no significant between-group differences in white matter or CSF volumes. Region-of-interest analyses showed significant bilateral gray matter volume reductions in the temporal lobes and superior temporal gyri in the schizophrenia group.

CONCLUSIONS

The structural brain expression of schizophrenia associated with the highly penetrant 22q11.2 deletion involves lower gray matter volumes in temporal lobe regions. These structural MRI findings in a 22q11.2 deletion syndrome form of schizophrenia are consistent with those from studies involving schizophrenia samples from the general population. The results provide further support for 22q11.2 deletion syndrome as a genetic subtype and as a useful neurodevelopmental model of schizophrenia.

Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task

The functional architecture of the human brain has been extensively described in terms of complex networks characterized by efficient small-world features. Recent functional magnetic resonance imaging (fMRI) studies have found altered small-world topological properties of brain functional networks in patients with schizophrenia (SZ) during the resting state. However, little is known about the small-world properties of brain networks in the context of a task. In this study, we investigated the topological properties of human brain functional networks derived from fMRI during an auditory oddball (AOD) task. Data were obtained from 20 healthy controls and 20 SZ; A left and a right task-related network which consisted of the top activated voxels in temporal lobe of each hemisphere were analyzed separately. All voxels were detected by group independent component analysis. Connectivity of the left and right task-related networks were estimated by partial correlation analysis and thresholded to construct a set of undirected graphs. The small-worldness values were decreased in both hemispheres in SZ. In addition, SZ showed longer shortest path length and lower global efficiency only in the left task-related networks. These results suggested small-world attributes are altered during the AOD task-related networks in SZ which provided further evidences for brain dysfunction of connectivity in SZ.

Progressive alterations of the auditory association areas in young non-psychotic offspring of schizophrenia patients

BACKGROUND

Schizophrenia may involve progressive alterations of structure and hemispheric lateralization of auditory association areas (AAA) within the superior temporal gyrus. These alterations may be greater in male patients. It is unclear if these deficits are state-dependent or whether they predate illness onset and reflect familial diathesis.

AIMS

We sought to compare AAA cortical thickness, surface area and lateralization across adolescent and young adult non-psychotic offspring of schizophrenia patients (OS) and healthy controls at baseline and one year follow-up. We also assessed the moderating effect of gender on these measures.

METHODS

Fifty-six OS and thirty-six control subjects were assessed at baseline and at follow-up on AAA surface area and thickness using FreeSurfer to process T1-MRI-images. We used repeated measures ANCOVAs, controlling intra cranial volume and age with assessment-time and side as within-subject factors and gender and study group as between-subject factors.

RESULTS

Surface area deficit in OS was greater on the left than on the right, as reflected in a lower surface area laterality-index (left-right/left + right × 100) in OS compared to controls. Left, but not right surface area and surface area laterality-index showed a longitudinal decline in OS compared to controls. Male OS declined more than controls on surface area and thickness.

CONCLUSIONS

Left AAA surface area may progressively decline in young non-psychotic offspring at familial diathesis for schizophrenia causing a continuing reversal of the leftward AAA lateralization. Progressive surface area reduction and thinning of AAA may be more prominent in young non-psychotic male offspring at risk for schizophrenia.

Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder

Intrinsic functional brain networks (INs) are regions showing temporal coherence with one another. These INs are present in the context of a task (as opposed to an undirected task such as rest), albeit modulated to a degree both spatially and temporally. Prominent networks include the default mode, attentional fronto-parietal, executive control, bilateral temporal lobe, and motor networks. The characterization of INs has recently gained considerable momentum, however; most previous studies evaluate only a small subset of the INs (e.g., default mode). In this paper we use independent component analysis to study INs decomposed from functional magnetic resonance imaging data collected in a large group of schizophrenia patients, healthy controls, and individuals with bipolar disorder, while performing an auditory oddball task. Schizophrenia and bipolar disorder share significant overlap in clinical symptoms, brain characteristics, and risk genes which motivates our goal of identifying whether functional imaging data can differentiate the two disorders. We tested for group differences in properties of all identified INs including spatial maps, spectra, and functional network connectivity. A small set of default mode, temporal lobe, and frontal networks with default mode regions appearing to play a key role in all comparisons. Bipolar subjects showed more prominent changes in ventromedial and prefrontal default mode regions whereas schizophrenia patients showed changes in posterior default mode regions. Anti-correlations between left parietal areas and dorsolateral prefrontal cortical areas were different in bipolar and schizophrenia patients and amplitude was significantly different from healthy controls in both patient groups. Patients exhibited similar frequency behavior across multiple networks with decreased low frequency power. In summary, a comprehensive analysis of INs reveals a key role for the default mode in both schizophrenia and bipolar disorder.

Identification of imaging biomarkers in schizophrenia: a coefficient-constrained independent component analysis of the mind multi-site schizophrenia study

A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia. In this study, we extracted fMRI features maps from the analysis of three experimental paradigms (auditory oddball, Sternberg item recognition, sensorimotor) for a large number (n=154) of patients with schizophrenia and matched healthy controls. We used the general linear model (GLM) and independent component analysis (ICA) to extract feature maps (i.e. ICA component maps and GLM contrast maps), which were then subjected to a coefficient-constrained independent component analysis (CCICA) to identify potential neurobiological markers. A total of 29 different feature maps were extracted for each subject. Our results show a number of optimal feature combinations that reflect a set of brain regions that significantly discriminate between patients and controls in the spatial heterogeneity and amplitude of their feature signals. Spatial heterogeneity was seen in regions such as the superior/middle temporal and frontal gyri, bilateral parietal lobules, and regions of the thalamus. Most strikingly, an ICA feature representing a bilateral frontal pole network was consistently seen in the ten highest feature results when ranked on differences found in the amplitude of their feature signals. The implication of this frontal pole network and the spatial variability which spans regions comprising of bilateral frontal/temporal lobes and parietal lobules suggests that they might play a significant role in the pathophysiology of schizophrenia.

The contributions of disease and genetic factors towards regional cortical thinning in schizophrenia: the UCLA family study

OBJECTIVE

Cortical thickness reductions in prefrontal and temporal cortices have been repeatedly observed in patients with schizophrenia. However, it remains unclear whether regional variations in cortical thickness may be attributable to disease-related or genetic-liability factors.

METHOD

The structural magnetic resonance imaging data of 48 adult-onset schizophrenia patients, 66 first-degree non-psychotic relatives of schizophrenia patients, 27 community comparison (CC) probands and 77 CC relatives were examined using cortical pattern matching methods to map and compare highly localized changes in cortical gray matter thickness between groups defined by biological risk for schizophrenia.

RESULTS

Schizophrenia patients showed marked cortical thinning primarily in frontal and temporal cortices when compared to unrelated CC probands. Results were similar, though less pronounced when patients were compared with their non-psychotic relatives. Cortical thickness reductions observed in unaffected relatives compared to age-similar CC relatives suggestive of schizophrenia-related genetic liability were marginal, surviving correction for the left parahippocampal gyrus and inferior occipital cortex only.

CONCLUSIONS

Observations of pronounced fronto/temporal cortical thinning in schizophrenia patients replicate prior findings. The lack of marked cortical thickness alterations in non-psychotic relatives of patients, suggests that disease processes are primary contributors toward cortical thickness reductions in the disorder. However, genetic factors may have a larger influence on abnormalities in the medial temporal lobe.