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Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Background: Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis., New Method: Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined., Results: During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state)., Comparison With Existing Methods: Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit., Conclusions: Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series., (Copyright © 2019. Published by Elsevier B.V.)

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability., (© 2019 Wiley Periodicals, Inc.)

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches., (© 2018 Wiley Periodicals, Inc.)

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group., Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide., Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset., Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia., (Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.

Human birth presents an abrupt transition from intrauterine to extrauterine life. Here we introduce a novel Maturation Index (MI) that considers the relative importance of gestational age at birth and postnatal age at scan in a General Linear Model. The MI is then applied to Diffusion Tensor Imaging (DTI) in newborns for characterizing typical white matter development in neonates. DTI was performed cross-sectionally in 47 neonates (gestational age at birth=39.1±1.6 weeks [GA], postnatal age at scan=25.5±12.2days [SA]). Radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA) along 27 white matter fiber tracts were considered. The MI was used to characterize inflection in maturation at the time of birth using GLM estimated rates of change before and after birth. It is proposed that the sign (positive versus negative) of MI reflects the period of greatest maturation rate. Two general patterns emerged from the MI analysis. First, RD and AD (but not FA) had positive MI on average across the whole brain (average MI AD =0.31±0.42, average MI RD =0.22±0.34). Second, significant regions of negative MI in RD and FA (but not AD) were observed in the inferior corticospinal regions, areas known to myelinate early. Observations using the proposed method are consistent with proposed models of the white matter maturation process in which pre-myelination is described by changes in AD and RD due to oligodendrocyte proliferation while true myelination is characterized by changes in RD and FA due to myelin formation., (Copyright © 2016 ISDN. Published by Elsevier Ltd. All rights reserved.)

Children diagnosed with attention-deficit/hyperactivity disorder (ADHD) are at increased risk for substance abuse. Response inhibition is a hallmark of ADHD, yet the combined effects of ADHD and regular substance use on neural networks associated with response inhibition are unknown. Task-based functional Magnetic Resonance Imaging (fMRI) data from young adults with childhood ADHD with (n = 25) and without (n = 25) cannabis use ≥ monthly in the past year were compared with a local normative comparison group (LNCG) with (n = 11) and without (n = 12) cannabis use. Go/NoGo behavioral and fMRI data were evaluated for main and interaction effects of ADHD diagnosis and cannabis use. ADHD participants made significantly more commission errors on NoGo trials than controls. ADHD participants also had less frontoparietal and frontostriatal activity, independent of cannabis use. No main effects of cannabis use on response inhibition or functional brain activation were observed. An interaction of ADHD diagnosis and cannabis use was found in the right hippocampus and cerebellar vermis, with increased recruitment of these regions in cannabis-using controls during correct response inhibition. ADHD participants had impaired response inhibition combined with less fronto-parietal/striatal activity, regardless of cannabis use history. Cannabis use did not impact behavioral response inhibition. Cannabis use was associated with hippocampal and cerebellar activation, areas rich in cannabinoid receptors, in LNCG but not ADHD participants. This may reflect recruitment of compensatory circuitry in cannabis using controls but not ADHD participants. Future studies targeting hippocampal and cerebellar-dependent function in these groups may provide further insight into how this circuitry is altered by ADHD and cannabis use.

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

Schizophrenia patients show significant subcortical brain abnormalities. We examined these abnormalities using automated image analysis software and provide effect size estimates for prospective multi-scanner schizophrenia studies. Subcortical and intracranial volumes were obtained using FreeSurfer 5.0.0 from high-resolution structural imaging scans from 186 schizophrenia patients (mean age±S.D.=38.9±11.6, 78% males) and 176 demographically similar controls (mean age±S.D.=37.5±11.2, 72% males). Scans were acquired from seven 3-Tesla scanners. Univariate mixed model regression analyses compared between-group volume differences. Weighted mean effect sizes (and number of subjects needed for 80% power at α=0.05) were computed based on the individual single site studies as well as on the overall multi-site study. Schizophrenia patients have significantly smaller intracranial, amygdala, and hippocampus volumes and larger lateral ventricle, putamen and pallidum volumes compared with healthy volunteers. Weighted mean effect sizes based on single site studies were generally larger than effect sizes computed based on analysis of the overall multi-site sample. Prospectively collected structural imaging data can be combined across sites to increase statistical power for meaningful group comparisons. Even when using similar scan protocols at each scanner, some between-site variance remains. The multi-scanner effect sizes provided by this study should help in the design of future multi-scanner schizophrenia imaging studies., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

When using functional brain imaging to study neuropsychiatric patients an important challenge is determining whether the imaging task assesses individual differences with equal precision in healthy control and impaired patient groups. Classical test theory (CTT) requires separate reliability studies of patients and controls to determine equivalent measurement precision with additional studies to determine measurement precision for different levels of disease severity. Unlike CTT, item response theory (IRT) provides estimates of measurement error for different levels of ability, without the need for separate studies, and can determine if different tests are equivalently difficult when investigating differential deficits between groups. To determine the potential value of IRT in functional brain imaging, IRT was applied to behavioral data obtained during a multi-center functional MRI (fMRI) study of working memory (WM). Average item difficulty was approximately one standard deviation below the ability scale mean, supporting the task's sensitivity to individual differences within the ability range of patients with WM impairment, but not within the range of most controls. The correlation of IRT estimated ability with fMRI activation during the task recognition period supported the linkage of the latent IRT scale to brain activation data. IRT can meaningfully contribute to the design of fMRI tasks., (Published by Elsevier Ireland Ltd.)

Objectives: Our laboratory recently identified the P85 gating ratio as a candidate biomarker for bipolar disorder. In order to evaluate the phenomenological significance of P85 gating, the current study examined reports of perceptual anomalies and their relationship to the P50 and P85 physiological measures of sensory gating., Methods: Reports of perceptual anomalies on the Structured Clinical Interview to Assess Perceptual Anomalies were compared in patients meeting DSM-IV criteria for paranoid schizophrenia (n = 66), schizoaffective disorder (n = 45), or bipolar I disorder (n = 42), and controls (n = 56), as well as their relationship with P85 and P50 gating., Results: The bipolar disorder group reported significantly more auditory, visual, and total anomalies than both the schizophrenia and control groups. The schizophrenia group also had more anomalies than the control group. Comparison of psychiatric subgroups revealed that the bipolar depressed, bipolar disorder with psychosis, and schizoaffective bipolar type groups reported the most anomalies compared to the other patient groups (bipolar disorder without psychosis, schizoaffective, bipolar manic). The total perceptual anomalies score and the P85 ratio significantly differentiated the bipolar disorder, schizoaffective, and paranoid schizophrenia groups from each other., Conclusions: These findings provide evidence of the phenomenological significance of P85. The results also yield further support not only for the P85 ratio, but also for increased reports of perceptual anomalies as possible markers for bipolar disorder., (© 2013 John Wiley and Sons A/S Published by Blackwell Publishing Ltd.)

Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).

Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment.

A method was developed to quantify the effect of scanner instability on functional MRI data by comparing the instability noise to endogenous noise present when scanning a human. The instability noise was computed from agar phantom data collected with two flip angles, allowing for a separation of the instability from the background noise. This method was used on human data collected at four 3 T scanners, allowing the physiological noise level to be extracted from the data. In a "well-operating" scanner, the instability noise is generally less than 10% of physiological noise in white matter and only about 2% of physiological noise in cortex. This indicates that instability in a well-operating scanner adds very little noise to functional MRI results. This new method allows researchers to make informed decisions about the maximum instability level a scanner can have before it is taken off line for maintenance or rejected from a multisite consortium. This method also provides information about the background noise, which is generally larger in magnitude than the instability noise., (Copyright © 2010 Wiley-Liss, Inc.)

The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age+/-s.d.: 75.52+/-6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure., (Copyright 2010 Elsevier Inc. All rights reserved.)

A recently identified variant within the fat mass and obesity-associated (FTO) gene is carried by 46% of Western Europeans and is associated with an approximately 1.2 kg higher weight, on average, in adults and an approximately 1 cm greater waist circumference. With >1 billion overweight and 300 million obese persons worldwide, it is crucial to understand the implications of carrying this very common allele for the health of our aging population. FTO is highly expressed in the brain and elevated body mass index (BMI) is associated with brain atrophy, but it is unknown how the obesity-associated risk allele affects human brain structure. We therefore generated 3D maps of regional brain volume differences in 206 healthy elderly subjects scanned with MRI and genotyped as part of the Alzheimer's Disease Neuroimaging Initiative. We found a pattern of systematic brain volume deficits in carriers of the obesity-associated risk allele versus noncarriers. Relative to structure volumes in the mean template, FTO risk allele carriers versus noncarriers had an average brain volume difference of approximately 8% in the frontal lobes and 12% in the occipital lobes-these regions also showed significant volume deficits in subjects with higher BMI. These brain differences were not attributable to differences in cholesterol levels, hypertension, or the volume of white matter hyperintensities; which were not detectably higher in FTO risk allele carriers versus noncarriers. These brain maps reveal that a commonly carried susceptibility allele for obesity is associated with structural brain atrophy, with implications for the health of the elderly.

Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data-driven approach that can identify temporally coherent networks that underlie fMRI activity. We applied ICA to an fMRI dataset for 115 patients with chronic schizophrenia and 130 healthy controls by performing the Sternberg Item Recognition Paradigm. Here, we describe the first results using ICA to identify differences in the function of WM networks in schizophrenia compared to controls. ICA revealed six networks that showed significant differences between patients with schizophrenia and healthy controls. Four of these networks were negatively task-correlated and showed deactivation across the posterior cingulate, precuneus, medial prefrontal cortex, anterior cingulate, inferior parietal lobules, and parahippocampus. These networks comprise brain regions known as the default-mode network (DMN), a well-characterized set of regions shown to be active during internal modes of cognition and implicated in schizophrenia. Two networks were positively task-correlated, with one network engaging WM regions such as bilateral DLPFC and inferior parietal lobules while the other network engaged primarily the cerebellum. Our results suggest that DLPFC dysfunction in schizophrenia might be lateralized to the left and intrinsically tied to other regions such as the inferior parietal lobule and cingulate gyrus. Furthermore, we found that DMN dysfunction in schizophrenia exists across multiple subnetworks of the DMN and that these subnetworks are individually relevant to the pathophysiology of schizophrenia. In summary, this large multisite study identified multiple temporally coherent networks, which are aberrant in schizophrenia versus healthy controls and suggests that both task-correlated and task-anticorrelated networks may serve as potential biomarkers.

Correlations of cognitive functioning with brain activation during a sternberg item recognition paradigm (SIRP) were investigated in patients with schizophrenia and in healthy controls studied at 8 sites. To measure memory scanning times, 4 response time models were fit to SIRP data. The best fitting model assumed exhaustive serial memory scanning followed by self-terminating memory search and involved one intercept parameter to represent SIRP processes not contributing directly to memory scanning. Patients displayed significantly longer response times with increasing memory load and differed on the memory scanning, memory search, and intercept parameters of the best fitting probability model. Groups differed in the correlation between the memory scanning parameter and linear brain response to increasing memory load within left inferior and left middle frontal gyrus, bilateral caudate, and right precuneus. The pattern of findings in these regions indicated that high scanning capacity was associated with high neural capacity among healthy subjects but that scanning speed was uncoupled from brain response to increasing memory load among schizophrenia patients. Group differences in correlation of the best fitting model's scanning parameter with a quadratic trend in brain response to increasing memory load suggested inefficient or disordered patterns of neural inhibition among individuals with schizophrenia, especially in the left perirhinal and entorhinal cortices. The results show at both cognitive and neural levels that disordered memory scanning contributes to deficient SIRP performance among schizophrenia patients.

Background: Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia., Methods: Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items., Results: Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress., Conclusion: Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

Schizophrenia is associated with subtle structural and functional brain abnormalities. Both recent and classical data suggest that it is a heterogeneous disorder that is clearly heritable. The cause and course of schizophrenia are poorly understood, and classical categories of clinical symptoms have not been particularly useful in identifying its pathophysiology or predicting its treatment. The possible genetic risk factors for schizophrenia are numerous; however, the connection between the genotype and the time-course, or the multifaceted symptoms of the disease, has yet to be established. Brain imaging methods that study the structure or function of the cortical and subcortical regions have also identified distinct patterns that distinguish schizophrenics from controls, and that may identify meaningful subtypes of schizophrenia. The predictive relationship between these imaging phenotypes and disease characteristics such as treatment response is only beginning to be revealed. The emergence of the field of imaging genetics, combining genetic, and neuroimaging data, holds much promise for the deeper understanding and improved treatment of diseases such as schizophrenia. In this article we review some of the key findings in imaging phenotyping and genotyping of schizophrenia, and the initial endeavors at their combination into more meaningful and predictive patterns, or endophenotypes identifying the relationships among clinical symptoms, course, genes, and the underlying pathophysiology.

Unlabelled: Serotonin 5-HT1A receptors have been implicated in disorders of the central nervous system and, therefore, are being studied by PET. Efforts are under way to improve in vivo stability of 5-HT1A agents currently in human use (11C-labeled N-(2-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl-N-(2-pyridinyl)cyclohexanecarboxamide [11C-WAY-100635], 4-(2'-methoxyphenyl)-1-[2'-(N-2''-pyridinyl)-p-18F-fluorobenzamido]ethylpiperazine [18F-MPPF], and 18F-labeled trans-4-fluoro-N-(2-[4-(2-methoxyphenyl)piperazin-1-yl)ethyl]-N-(2-pyridyl)cyclohexanecarboxamide [18F-FCWAY]). We have synthesized N-{2-[4-(2-methoxyphenyl)piperazinyl]ethyl}-N-(2-pyridyl)-N-(4-18F-fluoromethylcyclohexane)carboxamide (18F-mefway), which contains a 18F on a primary carbon to make the compound more stable to defluorination., Methods: Radiosynthesis of 18F-mefway was performed in a single tosylate for 18F-fluoride exchange. In vitro binding studies on rat brain slices using 18F-mefway were read on a phosphor imager. Monkey PET studies were performed on a whole-body PET scanner., Results: Binding affinity (inhibitory concentration of 50% [IC50]) of mefway was 26 nmol/L and was comparable to that of WAY-100635, 23 nmol/L. Yields of 18F-mefway were 20%-30% in specific activities of 74-111 GBq/micromol at the end of synthesis. In vitro binding of 18F-mefway in the hippocampus (Hp), colliculus (Co), cortex (Ctx), and other brain regions-with limited binding in the cerebellum (Cer)--was observed, with ratios of Hp/Cer = 82.3, Co/Cer = 45.8, and Ctx/Cer = 40. Serotonin displaced 18F-mefway from various brain regions with IC50 values in the range of 169-243 nmol/L. PET studies in a rhesus monkey showed 18F-mefway binding in the fontal cortex (FC), temporal cortex (TC) including hippocampus, raphe (Rp), and other brain regions, with ratios of FC/Cer = 9.0, TC/Cer = 10, and Rp/Cer = 3.3. Plasma analysis indicated the presence of approximately 30% of 18F-mefway at 150-180 min after injection., Conclusion: The high ratios in specific brain regions such as the hippocampus suggest that 18F-mefway has potential as a PET agent for 5HT1A receptors.

The alpha4beta2 nicotinic acetylcholine receptor (nAChR) has been implicated in various neurodegenerative diseases. Optimal positron emission tomography (PET) imaging agents are therefore highly desired for this receptor. We report here the development and initial evaluation of 2-fluoro-3-[2-((S)-3-pyrrolinyl)methoxy]pyridine (nifene). In vitro binding affinity of nifene in rat brain homogenate using 3H-cytisine exhibited a K(i) = 0.50 nM for the alpha4beta2 sites. The radiosynthesis of 2-18F-fluoro-3-[2-((S)-3-pyrrolinyl)methoxy]pyridine (18F-nifene) was accomplished in 2.5 h with an overall radiochemical yield of 40-50%, decay corrected. The specific activity was estimated to be approx. 37-185 GBq/micromol. In vitro autoradiography in rat brain slices indicated selective binding of 18F-nifene to anteroventral thalamic (AVT) nucleus, thalamus, subiculum, striata, cortex and other regions consistent with alpha4beta2 receptor distribution. Rat cerebellum showed some binding, whereas regions in the hippocampus had the lowest binding. The highest ratio of >13 between AVT and cerebellum was measured for 18F-nifene in rat brain slices. The specific binding was reduced (>95%) by 300 microM nicotine in these brain regions. Positron emission tomography imaging study of 18F-nifene (130 MBq) in anesthetized rhesus monkey was carried out using an ECAT EXACT HR+ scanner. PET study showed selective maximal uptake in the regions of the anterior medial thalamus, ventro-lateral thalamus, lateral geniculate, cingulate gyrus, temporal cortex including the subiculum. The cerebellum in the monkeys showed lower binding than the other regions. Thalamus-to-cerebellum ratio peaked at 30-35 min postinjection to a value of 2.2 and subsequently reduced. The faster binding profile of 18F-nifene indicates promise as a PET imaging agent and thus needs further evaluation.

With the increased frequency of multisite, large-scale collaborative neuro-imaging studies, the need for a general, self-documenting framework for the storage and retrieval of activation maps and anatomical labels becomes evident. To address this need, we have developed and extensible markup language (XML) schema and associated tools for the storage of neuro-imaging activation maps and anatomical labels. This schema, as part of the XML-based Clinical Experiment Data Exchange (XCEDE) schema, provides storage capabilities for analysis annotations, activation threshold parameters, and cluster and voxel-level statistics. Activation parameters contain information describing the threshold, degrees of freedom, FWHM smoothness, search volumes, voxel sizes, expected voxels per cluster, and expected number of clusters in the statistical map. Cluster and voxel statistics can be stored along with the coordinates, threshold, and anatomical label information. Multiple threshold types can be documented for a given cluster or voxel along with the uncorrected and corrected probability values. Multiple atlases can be used to generate anatomical labels and stored for each significant voxel or cluter. Additionally, a toolbox for Statistical Parametric Mapping software (http://www. fil. ion.ucl.ac.uk/spm/) was created to capture the results from activation maps using the XML schema that supports both SPM99 and SPM2 versions (http://nbirn.net/Resources/Users/ Applications/xcede/SPM&#95;XMLTools.htm). Support for anatomical labeling is available via the Talairach Daemon (http://ric.uthscsa. edu/projects/talairachdaemon.html) and Automated Anatomical Labeling (http://www. cyceron.fr/freeware/).

Biological factors responsible for nicotine initiation and dependence are largely unknown. Men and women smoke differently, and may smoke for different reasons. Brain metabolic response to nicotine may explain gender differences in nicotine use. We used FDG-PET to measure brain metabolic response on placebo and following nicotine administered by patch in 42 females and 77 males (smokers and non-smokers) while performing a Continuous Performance Task (CPT) or the Bushman Competition and Retaliation Task (CRT). Nicotine administration affected brain metabolism much differently in males and females, and these differences were dependent on task and smoking history. In the placebo condition female smokers performing the CPT and female non-smokers performing the CRT consistently had higher brain metabolism than males, especially in the entire prefrontal system and the mid and anterior temporal lobe, language cortices, and related subcortical systems. The overall effect of nicotine was to decrease these gender differences in brain metabolism.

Unlabelled: Nicotine alpha(4)beta(2) receptor subtypes are implicated in the study of Alzheimer's disease, schizophrenia, substance abuse, lung cancer, and other disorders. We report the development and evaluation of a putative antagonist, 5-(3'-fluoropropyl)-3-(2-(S)-pyrrolidinylmethoxy)pyridine (nifrolidine) as a PET agent for nicotine alpha(4)beta(2) receptors., Methods: In vitro binding affinity of nifrolidine was measured in rat brain slices labeled with (125)I-iodoepibatidine or (125)I-bungaratoxin. Selectivity of binding was measured in the presence of cytisine. (18)F radiolabeling was performed by reacting the tosylate precursor with (18)F-fluoride followed by deprotection. In vitro autoradiographic studies in rat brain slices with 5-(3'-(18)F-fluoropropyl)-3-(2-(S)-pyrrolidinylmethoxy)pyridine ((18)F-nifrolidine) were read on a phosphor imager. Rats were injected with (18)F-nifrolidine (3.7 MBq each), and brain regions were counted at various times (2-120 min). Blocking studies were performed by subcutaneous injection of nicotine (10 mg/kg). A PET study of (18)F-nifrolidine (approximately 148 MBq) was performed on an anesthetized rhesus monkey using a high-resolution scanner., Results: In vitro binding affinity of nifrolidine exhibited an inhibition constant of 2.89 nmol/L for the alpha(4)beta(2) sites. Radiosynthesis and high-performance liquid chromatography purifications yielded the product in approximately 20%-40% decay-corrected radiochemical yield to provide (18)F-nifrolidine specific activities of approximately 111-185 GBq/mumol. In vitro autoradiography in rat brain slices revealed selective binding of (18)F-nifrolidine to the anteroventral thalamic nucleus, ventral posteriomedial thalamus, dorsolateral geniculate, and, to a lesser extent, cortex and striata, which are known to contain alpha(4)beta(2) sites. This specific binding was completely abolished by 300 mumol/L nicotine. Ex vivo rat brain distribution studies indicated selective binding in the thalamus with a maximal thalamus-to-cerebellum ratio of approximately 3. The PET study revealed selective maximal uptake (0.01% injected dose/mL) in regions of the thalamus (anteroventral and anteromedial thalamus, ventrolateral thalamus) and extrathalamic regions such as cingulate gyrus, lateral geniculate, temporal cortex, and frontal cortex., Conclusion: Binding of (18)F-nifrolidine to alpha(4)beta(2) receptor-rich regions in rats and monkeys indicates promise as a PET agent. Additionally, the thalamus-to-cerebellum ratio approached a plateau of 1.7 in 120 min, indicating relatively faster kinetics compared with previously reported imaging agents.

The brain mechanisms underlying the cause of nicotine dependence are unknown, however, hostility traits are associated with increased susceptibility to nicotine dependence. We used FDG PET to measure brain metabolic response to nicotine administered by patch while the subject performed the Bushman aggression task in 86 high- and low-hostility subjects. Low-hostility trait subjects demonstrated no significant change in brain metabolic response to nicotine. In marked contrast, high-hostility non-smokers subjects demonstrated dramatic metabolic changes to low dose (3.5 mg patch) as did high-hostility smokers to high dose nicotine (21 mg patch) throughout the brain bilaterally (p<0.025). Correlational analyses demonstrated greater metabolic changes in response to nicotine in subjects with greatest hostility trait measures. The observed differences were not a consequence of plasma nicotine or cotinine levels. These metabolic changes were not observed when subjects performed a sustained attentional task (continuous performance task; CPT). Behaviorally, high-hostility subjects had higher ratings of anger, impatience, irritability and nervousness, and lower ratings of happiness, relaxation and curiosity than low-hostility subjects. Smokers had significantly greater scores on impatience and restlessness than non-smokers. This PET study demonstrates a conspicuous lack of the brain metabolic response to nicotine in low-hostility non-smokers in contrast to a dramatic brain response to nicotine in high hostility subjects. This biological difference in brain metabolic response to nicotine between high and low hostility trait subjects may explain differences in susceptibility to nicotine dependence.

Neotrofin, a reported inducer of CNS neurotrophic factor synthesis and release, with memory-enhancing activity and demonstrated restoration of age-induced memory deficits in animals, was tested in patients with mild to moderate Alzheimer's disease. Nineteen subjects were treated with 1 week of low-dose (150 mg per day) and 1 week of high-dose (500 or 1000 mg per day) Neotrofin. Cognitive composite scores demonstrated improvement in memory (F=9.6, P=0.0004), executive functioning (P=0.004), and attention (P=0.004). PET scanning was obtained before, after low, and after high dosing. The brain areas most affected were the cerebellum, and sensory and prefrontal cortices, where increases in GMR (Glucose Metabolic Rate) were observed. Increases and decreases were observed in the posterior superior temporal (BA 22), parahippocampal, inferior temporal (BA 37, 20), and fusiform gyri as well as the superior parietal lobule and postcentral gyrus. There were strong hemispheric differences, producing opposite metabolic effects in homologous brain regions. Subcortically, the posterior thalamic region, meso-pontine tegmentum, and tectum had increases in GMR on the left side. At the low dose, GMR was generally increased, but to a lesser degree. The brain areas subserving memory, attention and executive functions were significantly altered in GMR by Neotrofin; however, the directions of these changes were complex. There were significant correlations between improvement in memory and executive function in brain areas involved in circuits subserving these functions. Thus, Neotrofin appears to induce metabolic changes in brain regions involved in circuits underlying memory, attention, and executive functioning.

Twenty seven drug-free schizophrenic patients and 25 normal controls were studied in an EEG photic driving paradigm with an improved harmonic resolution. Consistent with previous results, the present data show that schizophrenics have lower EEG responses to photic stimulation in alpha frequency. The differences between normal and schizophrenic subjects were distributed across major brain areas except centro-temporal regions. It also demonstrated that 1-Hz narrow-pulsed stimulation was feasible in EEG photic driving studies. The enhanced harmonic resolution produced significantly more information in the frequency domain about EEG oscillatory responses to visual stimulus. Using this new technique, the authors showed for the first time that alpha photic driving was a well defined resonant system and that the group differences in harmonic responses were primarily confined in the high frequency portion of alpha band, suggesting a peak frequency shift of EEG resonance in schizophrenia.

Postmortem neuropathological investigations in the last half decade provide increasing evidence compatible with a neurodevelopmental defect in schizophrenia. Basic and clinical data support hypotheses suggesting that disturbances in neurodevelopment in schizophrenia may involve the cortical subplate and a theorized second trimester "window of vulnerability". The focus of this paper is on (1) selected methodological issues involved in the collection, analyses and preservation of human postmortem brain tissue; (2) a review of evidence showing morphological defects particularly in prefrontal cortical regions of the schizophrenic brain; and (3) potential future research directions.

In an intensive single-subject design, electroencephalographic (EEG) alpha power and receptive and expressive language in dreaming were studied in 12 dreams during rapid eye movement (REM) sleep on 12 separate nights. Bilateral EEG was recorded continuously from 21 sites and digitized. We used the Fast Fourier transformation (FFT) for power spectral analysis to measure EEG power in the alpha frequency range (8-12 Hz) at each of the EEG sites. The subject was awakened after about 14 minutes into the second REM period, and dream reports were collected. We scored the dream reports for expressive and receptive language. The lower the alpha power on the left sides of those homologous pairs that roughly correspond to Broca's (C3) or Wernicke's area (P3), the more expressive or receptive language in dream reports. The largest difference between the correlation of the left and that of the right homologous pair of regions was found in the central (C3, C4) area for expressive language and in the parietal (P3, P4) area for receptive language. Our finding suggests lateralized and localized cortical activation in relation to language in dreaming.

Seventy-nine patients with schizophrenia and 47 healthy controls received positron-emission tomography (PET) with 18F-2-deoxyglucose uptake while executing the Continuous Performance Test (CPT). Patients had been off all psychoactive medication for at least four weeks. Patients' symptoms were assessed with the Brief Psychiatric Rating Scale and factor scale scores were obtained. These scores were used in cluster analysis to identify patients with predominantly delusional, negative, disorganized, and remitted symptoms. To address the interconnective nature of cerebral functioning, regions of interest were defined on the basis of the results of a factor analysis of metabolic rate in selected brain regions. This procedure identified six cortical and eight subcortical region of interest factors. Metabolic rate factor scale scores were compared between the patients' clusters and the healthy controls. The delusional cluster showed a significantly reduced hippocampal activity, while the negative symptoms cluster presented with a prominent hypofrontality and significantly increased left temporal cortex values. Concurrently, both clusters were associated with a decreased activity on the factor 'anterior cingulum and medial frontal gyrus'. The disorganized cluster was characterized by a significant overactivity in the parietal cortex and motor strip and a decreased activity in the corpus callosum. The subsyndromes of chronic schizophrenia are therefore characterized by deviant patterns of cerebral activity rather than deficits in a single location.

This article reviews evidence for morphological abnormalities in schizophrenia as assessed by brain imaging and neurohistochemical techniques including immunohistochemistry and in situ hybridization. Localized deficits in schizophrenic brain appear in many regions including frontal and temporal lobes, anterior cingulate, mediodorsal thalamic nucleus, and corpus callosum. These areas are interconnected and may provide the basis for a "psychosis circuitry." Neuronal disruption of elements in this circuitry may result in a hypothesized dysconnection syndrome. Evidence suggests an alteration in neuronal development related to either genetic and/or environmental factors. Primary and secondary anterograde and retrograde effects may accompany this neurodevelopmental defect and may further alter intrinsic and extrinsic neuronal communications. A number of studies are consistent with the second trimester of gestation being a critical period for fetal brain development, especially for neuronal migration. Fetal trauma due to environmental insults (e.g., influenza) during this trimester may increase the incidence of schizophrenia. Recent advances in the identification of factors that modulate neuronal development including axon guidance molecules, neurotrophins, and programmed cell death genes provide intriguing new areas for potential investigation. Future research may focus on the factors controlling neuronal migration and programmed cell death in the schizophrenic brain.

Objective: The cortical-striatal-thalamic circuit modulates cognitive processing and thus may be involved in the cognitive dysfunction in schizophrenia. The imaging of metabolic rate in the structures making up this circuit could reveal the correlates of schizophrenia and its main symptoms., Method: Seventy male schizophrenic patients underwent [18F]-fluorodeoxyglucose positron emission tomography after a period of at least 4 weeks during which they had not received neuroleptic medication and were compared to 30 age-matched male normal comparison subjects., Results: Analyses revealed decreased metabolism in medial frontal cortex, cingulate gyrus, medial temporal lobe, corpus callosum, and ventral caudate and increased metabolism in the left lateral temporal and occipital cortices in the schizophrenic cohort. Consistent with previous studies, the schizophrenic group had lower hypofrontality scores (ratios of lateral frontal to occipital metabolism) than did comparison subjects. The lateral frontal cortical metabolism of schizophrenic patients did not differ from that of comparison subjects, while occipital cortical metabolism was high, suggesting that lateral hypofrontality is due to abnormalities in occipital rather than lateral frontal activity. Hypofrontality was more prominent in medial than lateral frontal cortex. Brief Psychiatric Rating Scale (BPRS) scores, obtained for each schizophrenic patient on the scan day, were correlated with regional brain glucose metabolic rate. Medial frontal cortical and thalamic activity correlated negatively with total BPRS score and with positive and negative symptom scores. Lateral frontal cortical metabolism and hypofrontality scores did not significantly correlate with negative symptoms. Analyses of variance demonstrated a reduced right greater than left asymmetry in the schizophrenic patients for the lateral cortex as a whole, with simple interactions showing this effect specifically in temporal and frontal cortical regions., Conclusions: Low metabolic rates were confirmed in medial frontal cortical regions as well as in the basal ganglia, consistent with the importance of the cortical-striatal-thalamic pathways in schizophrenia. Loss of normal lateralization patterns was also observed on an exploratory basis. Correlations with negative symptoms and group differences were more prominent in medial than lateral frontal cortex, suggesting that medial regions may be more important in schizophrenic pathology.

A method is described that enables fixation of previously fresh-frozen and stored adult and fetal human or animal brains. The method involves fixing during thawing under controlled, cryoprotected conditions and is compatible with good histological quality and the preservation of enzymatic activity and immunoreactivity of many neural antigens. It offers considerable advantages for the storage of large amounts of tissue from which multiple samples can be taken and processed under fixation and other conditions that can be optimized for a variety of methods, many of which may be incompatible if the whole brain is fixed in a single fixative prior to storage.

Twelve patients with schizophrenia received positron emission tomography scans with 18F-deoxyglucose before and after 4 to 6 weeks of treatment with clozapine or thiothixene. Both stereotaxic and magnetic resonance image template methods were used to position regions of interest for metabolic rate analysis. Clozapine increased and thiothixene decreased metabolic rates in the basal ganglia; these effects were most marked on the right side. Within the basal ganglia, a superior to inferior gradient in drug effect was found for thiothixene but not clozapine. This gradient resembled in some respects observations on regional differences in D2 receptors in human autoradiography. Baseline metabolic rates also predicted clinical medication response, with right inferior caudate metabolic rates differentiating clozapine and thiothixene responders. Larger sample studies are needed to replicate and extend these initial findings.

Long-term administration of the atypical neuroleptic clozapine (CLZ) poses a much lower risk of extrapyramidal side effects (EPS) than does the use of typical neuroleptics such as haloperidol (HAL). To investigate the neural mechanisms of the differing CNS activities of these two drugs, we used quantitative autoradiography to measure changes in dopamine and serotonin receptors in rats after injection with CLZ or HAL for 21 days at clinically relevant dose ratios. Levels of D1, D2, and 5-HT2 receptors were determined in frontal cortex, caudate-putamen, and nucleus accumbens. Rats that received CLZ chronically showed CNS receptor changes markedly different from those in chronic HAL-treated animals. Whereas rats treated chronically with HAL showed enhanced striatal D2 binding (average increase of 42%), those treated with CLZ did not. In contrast, chronic CLZ, but not chronic HAL, induced enhanced striatal D1 binding (average increase of 43%). Finally, CLZ treatment decreased 5-HT2 receptor binding by an average of 37%, while HAL had no significant effect. The effects of chronic HAL or CLZ treatment on receptors were similar in all forebrain areas examined. However, since D1 and 5-HT2 receptors are more abundant than D2 sites in limbic and neocortical areas, the preferential modulation of D1 and 5-HT2 receptors by CLZ suggests a greater impact of this atypical neuroleptic on activity of the limbic system than that achieved by the typical neuroleptic, HAL. These findings suggest that the clinical profile of atypical neuroleptics such as CLZ may be attributed to their effects on a receptor profile differing in pharmacological characteristics and anatomical distribution from that affected by typical neuroleptics.

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.