Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study

Converting sounds to meaning with ventral semantic language networks: integration of interdisciplinary data on brain connectivity, direct electrical stimulation and clinical disconnection syndromes

Numerous traditional linguistic theories propose that semantic language pathways convert sounds to meaningful concepts, generating interpretations ranging from simple object descriptions to communicating complex, analytical thinking. Although the dual-stream model of Hickok and Poeppel is widely employed, proposing a dorsal stream, mapping speech sounds to articulatory/phonological networks, and a ventral stream, mapping speech sounds to semantic representations, other language models have been proposed. Indeed, despite seemingly congruent models of semantic language pathways, research outputs from varied specialisms contain only partially congruent data, secondary to the diversity of applied disciplines, ranging from fibre dissection, tract tracing, and functional neuroimaging to neuropsychiatry, stroke neurology, and intraoperative direct electrical stimulation. The current review presents a comprehensive, interdisciplinary synthesis of the ventral, semantic connectivity pathways consisting of the uncinate, middle longitudinal, inferior longitudinal, and inferior fronto-occipital fasciculi, with special reference to areas of controversies or consensus. This is achieved by describing, for each tract, historical concept evolution, terminations, lateralisation, and segmentation models. Clinical implications are presented in three forms: (a) functional considerations derived from normal subject investigations, (b) outputs of direct electrical stimulation during awake brain surgery, and (c) results of disconnection syndromes following disease-related lesioning. The current review unifies interpretation of related specialisms and serves as a framework/thinking model for additional research on language data acquisition and integration.

Opposing white matter microstructure abnormalities in 22q11.2 deletion and duplication carriers

Deletions and duplications at the 22q11.2 locus are associated with significant neurodevelopmental and psychiatric morbidity. Previous diffusion-weighted magnetic resonance imaging (MRI) studies in 22q11.2 deletion carriers (22q-del) found nonspecific white matter (WM) abnormalities, characterized by higher fractional anisotropy. Here, utilizing novel imaging and processing methods that allow separation of signal contribution from different tissue properties, we investigate whether higher anisotropy is driven by (1) extracellular changes, (2) selective degeneration of secondary fibers, or (3) volumetric differences. We further, for the first time, investigate WM microstructure in 22q11.2 duplication carriers (22q-dup). Multi-shell diffusion-weighted images were acquired from 26 22q-del, 19 22q-dup, and 18 healthy individuals (HC). Images were fitted with the free-water model to estimate anisotropy following extracellular free-water elimination and with the novel BedpostX model to estimate fractional volumes of primary and secondary fiber populations. Outcome measures were compared between groups, with and without correction for WM and cerebrospinal fluid (CSF) volumes. In 22q-del, anisotropy following free-water elimination remained significantly higher compared with controls. BedpostX did not identify selective secondary fiber degeneration. Higher anisotropy diminished when correcting for the higher CSF and lower WM volumes. In contrast, 22q-dup had lower anisotropy and greater extracellular space than HC, not influenced by macrostructural volumes. Our findings demonstrate opposing effects of reciprocal 22q11.2 copy-number variation on WM, which may arise from distinct pathologies. In 22q-del, microstructural abnormalities may be secondary to enlarged CSF space and more densely packed WM. In 22q-dup, we see evidence for demyelination similar to what is commonly observed in neuropsychiatric disorders.

Brain microstructural abnormalities in 22q11.2 deletion syndrome: A systematic review of diffusion tensor imaging studies

22q11.2 deletion syndrome (22q11DS) is a severe genetic syndrome characterized by cognitive deficits and neuropsychiatric disorders, particularly schizophrenia. Neuroimaging alterations have been extensively reported in 22q11DS, both in gray and white matter structures. However, a considerable variability among the results affects the generalizability of the findings to date. Herein, we reviewed diffusion tensor imaging (DTI) findings in 22q11DS, their association with psychosis and cognition, and the implications of DTI studies on neurodevelopment in 22q11DS. We also investigated differences between 22q11DS and schizophrenic patients without 22q11DS. Using an online search of PubMed and Embase, we identified studies investigating DTI findings in 22q11DS. After selecting eligible studies in accordance with the preferred reporting items for systematic reviews and meta-analyses guideline, we included thirty-one studies. Overall, 22q11DS patients show altered structural connectivity and disrupted microstructural organization of most cortical and subcortical structures and white matter tracts. Moreover, despite a significant heterogeneity in the results, reduced diffusivity measures and elevated fractional anisotropy were observed. However controversial, compared to typically developing children, 22q11DS patients reached the peak of fractional anisotropy (FA) and the trough of radial diffusivity (RD) at an older age, which shows neurodevelopmental delay. DTI measures were also associated with psychotic symptoms and cognitive deficits. In conclusion, this study provides a comprehensive review of microstructural alterations in 22q11DS. Future larger investigations on this syndrome could potentially lead to the detection of early diagnostic imaging markers for genetically induced schizophrenia, thus improving the treatment and, ultimately, the outcome.

Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.

A cross-comparison of cognitive ability across 8 genomic disorders

Genomic disorders result from rearrangement of the human genome. Most genomic disorders are caused by copy number variants (CNV), deletions or duplications of several hundred kilobases. Many CNV loci are associated with autism, schizophrenia, and most commonly, intellectual disability (ID). However, there is little comparison of cognitive ability measures across these CNV disorders. This study aims to understand whether existing data can be leveraged for a cross-comparison of cognitive ability among multiple CNV. We found there is a lack of harmonization among assessment instruments and little standardization for reporting summary data across studies. Despite these limitations, we identified a differential impact of CNV loci on cognitive ability. Our data suggest that future cross-comparisons of CNV disorders will reveal meaningful differences across the phenotypic spectrum, especially if standardized phenotypic assessment is achieved.

Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability

How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.

Extreme capsule is a bottleneck for ventral pathway

As neuroscience literature suggests, extreme capsule is considered a whiter matter tract. Nevertheless, it is not clear whether extreme capsule itself is an association fiber pathway or only a bottleneck for other association fibers to pass. Via our review, investigating anatomical position, connectivity and cognitive role of the bundles in extreme capsule, and by analyzing data from the dissection, it can be argued that extreme capsule is probably a bottleneck for the passage of uncinated fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF), and these fasciculi are responsible for the respective roles in language processing.

Altered white matter microstructure in 22q11.2 deletion syndrome: a multisite diffusion tensor imaging study

22q11.2 deletion syndrome (22q11DS)-a neurodevelopmental condition caused by a hemizygous deletion on chromosome 22-is associated with an elevated risk of psychosis and other developmental brain disorders. Prior single-site diffusion magnetic resonance imaging (dMRI) studies have reported altered white matter (WM) microstructure in 22q11DS, but small samples and variable methods have led to contradictory results. Here we present the largest study ever conducted of dMRI-derived measures of WM microstructure in 22q11DS (334 22q11.2 deletion carriers and 260 healthy age- and sex-matched controls; age range 6-52 years). Using harmonization protocols developed by the ENIGMA-DTI working group, we identified widespread reductions in mean, axial and radial diffusivities in 22q11DS, most pronounced in regions with major cortico-cortical and cortico-thalamic fibers: the corona radiata, corpus callosum, superior longitudinal fasciculus, posterior thalamic radiations, and sagittal stratum (Cohen's d's ranging from -0.9 to -1.3). Only the posterior limb of the internal capsule (IC), comprised primarily of corticofugal fibers, showed higher axial diffusivity in 22q11DS. 22q11DS patients showed higher mean fractional anisotropy (FA) in callosal and projection fibers (IC and corona radiata) relative to controls, but lower FA than controls in regions with predominantly association fibers. Psychotic illness in 22q11DS was associated with more substantial diffusivity reductions in multiple regions. Overall, these findings indicate large effects of the 22q11.2 deletion on WM microstructure, especially in major cortico-cortical connections. Taken together with findings from animal models, this pattern of abnormalities may reflect disrupted neurogenesis of projection neurons in outer cortical layers.

Abnormalities in white matter tracts in the fronto-striatal-thalamic circuit are associated with verbal performance in 22q11.2DS

BACKGROUND

Abnormalities in fronto-striatal-thalamic (FST) sub-circuits are present in schizophrenia and are associated with cognitive impairments. However, it remains unknown whether abnormalities in FST sub-circuits are present before psychosis onset. This may be elucidated by investigating 22q11.2 deletion syndrome (22q11DS), a genetic syndrome associated with a 30% risk for developing schizophrenia in adulthood and a decline in Verbal IQ (VIQ) preceding psychosis onset. Here, we examined white matter (WM) tracts in FST sub-circuits, especially those in the dorsolateral (DLPFC) and ventrolateral prefrontal cortex (VLPFC) sub-circuits, and their associations with VIQ in young adults with 22q11DS.

METHODS

Diffusion MRI scans were acquired from 21 individuals with 22q11DS with prodromal symptoms of schizophrenia, 30 individuals with 22q11DS without prodromal symptoms, and 30 healthy controls (mean age: 21 ± 2 years). WM tracts were reconstructed between striatum and thalamus with rostral middle frontal gyrus (rMFG) and inferior frontal gyrus (IFG), representing DLPFC and VLPFC respectively. Fractional anisotropy (FA) and radial diffusivity (RD) were used for group comparisons. VIQ was assessed and associations with the diffusion measures were evaluated.

RESULTS

FA was significantly increased and RD decreased in most tracts of the DLPFC and VLPFC sub-circuits in 22q11DS. Verbal IQ scores correlated negatively with FA and, at trend level, positively with RD in the right thalamus-IFG tract in 22q11DS with prodromal symptoms.

CONCLUSIONS

While abnormalities in FST sub-circuits are associated with schizophrenia, we observed that these abnormalities are also present in 22q11DS individuals with prodromal symptoms and are associated with verbal performance in the right thalamus-IFG tract.

Neuroimaging Phenotypes Associated With Risk and Resilience for Psychosis and Autism Spectrum Disorders in 22q11.2 Microdeletion Syndrome

Identification of biological risk factors that contribute to the development of complex neuropsychiatric disorders such as psychosis and autism spectrum disorder (ASD) is key for early intervention and detection. Furthermore, parsing the biological heterogeneity associated with these neuropsychiatric syndromes will help us understand the neural mechanisms underlying psychiatric symptom development. The 22q11.2 microdeletion syndrome (22q11DS) is caused by a recurrent genetic mutation that carries significantly increased risk for developing psychosis and/or ASD. In this review, I provide an brief introduction to 22q11DS and discuss common phenotyping strategies that are used to assess psychosis and ASD in this population. I then summarize neuroimaging phenotypes associated with psychosis and ASD in 22q11.DS. Next, I discuss challenges within the field and provide practical suggestions to overcome these obstacles. Finally, I discuss future directions for moving 22q11DS risk and resilience research forward.

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

BACKGROUND

The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied.

METHODS

We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality.

RESULTS

A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects.

CONCLUSIONS

Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

Improving the classification of neuropsychiatric conditions using gene ontology terms as features

Although neuropsychiatric disorders have an established genetic background, their molecular foundations remain elusive. This has prompted many investigators to search for explanatory biomarkers that can predict clinical outcomes. One approach uses machine learning to classify patients based on blood mRNA expression. However, these endeavors typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because not all genes have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the models of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that is accurate and stable. We also show how annotation features can improve the interpretability of classifiers: as annotations are used to assign biological importance to genes, the biological importance of annotation-based features are the features themselves. In evaluating the annotation features, we find that top ranked annotations tend contain top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

Abnormalities in gray matter microstructure in young adults with 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion Syndrome (22q11DS) is a genetic, neurodevelopmental disorder characterized by a chromosomal deletion and a distinct cognitive profile. Although abnormalities in the macrostructure of the cortex have been identified in individuals with 22q11DS, it is not known if there are additional microstructural changes in gray matter regions in this syndrome, and/or if such microstructural changes are associated with cognitive functioning.

METHODS

This study employed a novel diffusion MRI measure, the Heterogeneity of Fractional Anisotropy (HFA), to examine variability in the microstructural organization of the cortex in healthy young adults (N = 30) and those with 22q11DS (N = 56). Diffusion MRI, structural MRI, clinical and cognitive data were acquired.

RESULTS

Compared to controls, individuals with 22q11DS evinced increased HFA in cortical association (p = .003, d = 0.86) and paralimbic (p < .0001, d = 1.2) brain areas, whereas no significant differences were found between the two groups in primary cortical brain areas. Additionally, increased HFA of the right paralimbic area was associated with poorer performance on tests of response inhibition, i.e., the Stroop Test (rho = -0.37 p = .005) and the Gordon Diagnostic System Vigilance Commission (rho = -0.41 p = .002) in the 22q11DS group. No significant correlations were found between HFA and cognitive abilities in the healthy control group.

CONCLUSIONS

These findings suggest that cortical microstructural disorganization may be a neural correlate of response inhibition in individuals with 22q11DS. Given that the migration pattern of neural crest cells is disrupted at the time of early brain development in 22q11DS, we hypothesize that these neural alterations may be neurodevelopmental in origin, and reflect cortical dysfunction associated with cognitive deficits.

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

Functional Anatomy of the Inferior Longitudinal Fasciculus: From Historical Reports to Current Hypotheses

The inferior longitudinal fasciculus (ILF) is a long-range, associative white matter pathway that connects the occipital and temporal-occipital areas of the brain to the anterior temporal areas. In view of the ILF's anatomic connections, it has been suggested that this pathway has a major role in a relatively large array of brain functions. Until recently, however, the literature data on these potential functions were scarce. Here, we review the key findings of recent anatomic, neuromodulation, and neuropsychological studies. We also summarize reports on how this tract is disrupted in a wide range of brain disorders, including psychopathologic, neurodevelopmental, and neurologic diseases. Our review reveals that the ILF is a multilayered, bidirectional tract involved in processing and modulating visual cues and thus in visually guided decisions and behaviors. Accordingly, sudden disruption of the ILF by neurologic insult is mainly associated with neuropsychological impairments of visual cognition (e.g., visual agnosia, prosopagnosia, and alexia). Furthermore, disruption of the ILF may constitute the pathophysiologic basis for visual hallucinations and socio-emotional impairments in schizophrenia, as well as emotional difficulties in autism spectrum disorder. Degeneration of the ILF in neurodegenerative diseases affecting the temporal lobe may explain (at least in part) the gradual onset of semantic and lexical access difficulties. Although some of the functions mediated by the ILF appear to be relatively lateralized, observations from neurosurgery suggest that disruption of the tract's anterior portion can be dynamically compensated for by the contralateral portion. This might explain why bilateral disruption of the ILF in either acute or progressive disease is highly detrimental in neuropsychological terms.

White matter abnormalities in 22q11.2 deletion syndrome patients showing cognitive decline

BACKGROUND

Decline in cognitive functioning precedes the first psychotic episode in the course of schizophrenia and is considered a hallmark symptom of the disorder. Given the low incidence of schizophrenia, it remains a challenge to investigate whether cognitive decline coincides with disease-related changes in brain structure, such as white matter abnormalities. The 22q11.2 deletion syndrome (22q11DS) is an appealing model in this context, as 25% of patients develop psychosis. Furthermore, we recently showed that cognitive decline also precedes the onset of psychosis in individuals with 22q11DS. Here, we investigate whether the early cognitive decline in patients with 22q11DS is associated with alterations in white matter microstructure.

METHODS

We compared the fractional anisotropy (FA) of white matter in 22q11DS patients with cognitive decline [n = 16; -18.34 (15.8) VIQ percentile points over 6.80 (2.39) years] to 22q11DS patients without cognitive decline [n = 18; 17.71 (20.17) VIQ percentile points over 5.27 (2.03) years] by applying an atlas-based approach to diffusion-weighted imaging data.

RESULTS

FA was significantly increased (p < 0.05, FDR) in 22q11DS patients with a cognitive decline in the bilateral superior longitudinal fasciculus, the bilateral cingulum bundle, all subcomponents of the left internal capsule and the left superior frontal-occipital fasciculus as compared with 22q11DS patients without cognitive decline.

CONCLUSIONS

Within 22q11DS, the early cognitive decline is associated with microstructural differences in white matter. At the mean age of 17.8 years, these changes are reflected in increased FA in several tracts. We hypothesize that similar brain alterations associated with cognitive decline take place early in the trajectory of schizophrenia.

A comparison of three fiber tract delineation methods and their impact on white matter analysis

Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.

Frontal dysconnectivity in 22q11.2 deletion syndrome: an atlas-based functional connectivity analysis

BACKGROUND

22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental syndrome associated with deficits in cognitive and emotional processing. This syndrome represents one of the highest risk factors for the development of schizophrenia. Previous studies of functional connectivity (FC) in 22q11DS report aberrant connectivity patterns in large-scale networks that are associated with the development of psychotic symptoms.

METHODS

In this study, we performed a functional connectivity analysis using the CONN toolbox to test for differential connectivity patterns between 54 individuals with 22q11DS and 30 healthy controls, between the ages of 17-25 years old. We mapped resting-state fMRI data onto 68 atlas-based regions of interest (ROIs) generated by the Desikan-Killany atlas in FreeSurfer, resulting in 2278 ROI-to-ROI connections for which we determined total linear temporal associations between each. Within the group with 22q11DS only, we further tested the association between prodromal symptoms of psychosis and FC.

RESULTS

We observed that relative to controls, individuals with 22q11DS displayed increased FC in lobar networks involving the frontal-frontal, frontal-parietal, and frontal-occipital ROIs. In contrast, FC between ROIs in the parietal-temporal and occipital lobes was reduced in the 22q11DS group relative to healthy controls. Moreover, positive psychotic symptoms were positively associated with increased functional connections between the left precuneus and right superior frontal gyrus, as well as reduced functional connectivity between the bilateral pericalcarine. Positive symptoms were negatively associated with increased functional connectivity between the right pericalcarine and right postcentral gyrus.

CONCLUSIONS

Our results suggest that functional organization may be altered in 22q11DS, leading to disruption in connectivity between frontal and other lobar substructures, and potentially increasing risk for prodromal psychosis.

Genomic and Imaging Biomarkers in Schizophrenia

Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.