Functional and structural MR imaging in neuropsychiatric disorders, part 2: application in schizophrenia and autism

Examining the Dominant Presence of Brain Grey Matter in Autism During Functional Magnetic Resonance Imaging

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD.

Methods

To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975.

Results

We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation.

Conclusion

Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.

Genesis, modelling and methodological remedies to autism heterogeneity

Diagnostic criteria used in autism research have undergone a shift towards the inclusion of a larger population, paralleled by increasing, but variable, estimates of autism prevalence across clinical settings and continents. A categorical diagnosis of autism spectrum disorder is now consistent with large variations in language, intelligence, comorbidity, and severity, leading to a heterogeneous sample of individuals, increasingly distant from the initial prototypical descriptions. We review the history of autism diagnosis and subtyping, and the evidence of heterogeneity in autism at the cognitive, neurological, and genetic levels. We describe two strategies to address the problem of heterogeneity: clustering, and truncated-compartmentalized enrollment strategy based on prototype recognition. The advances made using clustering methods have been modest. We present an alternative, new strategy for dissecting autism heterogeneity, emphasizing incorporation of prototypical samples in research cohorts, comparison of subgroups defined by specific ranges of values for the clinical specifiers, and retesting the generality of neurobiological results considered to be acquired from the entire autism spectrum on prototypical cohorts defined by narrow specifiers values.

Association and epistatic analysis of white matter related genes across the continuum schizophrenia and autism spectrum disorders: The joint effect of NRG1-ErbB genes

BACKGROUND

Schizophrenia-spectrum disorders (SSD) and Autism spectrum disorders (ASD) are neurodevelopmental disorders that share clinical, cognitive, and genetic characteristics, as well as particular white matter (WM) abnormalities. In this study, we aimed to investigate the role of a set of oligodendrocyte/myelin-related (OMR) genes and their epistatic effect on the risk for SSD and ASD.

METHODS

We examined 108 SNPs in a set of 22 OMR genes in 1749 subjects divided into three independent samples (187 SSD trios, 915 SSD cases/control, and 91 ASD trios). Genetic association and gene-gene interaction analyses were conducted with PLINK and MB-MDR, and permutation procedures were implemented in both.

RESULTS

Some OMR genes showed an association trend with SSD, while after correction, the ones that remained significantly associated were MBP, ERBB3, and AKT1. Significant gene-gene interactions were found between (i) NRG1*MBP (perm p-value = 0.002) in the SSD trios sample, (ii) ERBB3*AKT1 (perm p-value = 0.001) in the SSD case-control sample, and (iii) ERBB3*QKI (perm p-value = 0.0006) in the ASD trios sample.

DISCUSSION

Our results suggest the implication of OMR genes in the risk for both SSD and ASD and highlight the role of NRG1 and ERBB genes. These findings are in line with the previous evidence and may suggest pathophysiological mechanisms related to NRG1/ERBBs signalling in these disorders.

Automated multiclass tissue segmentation of clinical brain MRIs with lesions

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A U-Net incorporating spatial prior information can successfully segment 6 brain tissue types.

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The U-Net was able to segment gray and white matter in the presence of lesions.

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The U-Net surpassed the performance of its source algorithm in an external dataset.

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Segmentations were produced in a hundredth of the time of its predecessor algorithm.

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

Heart Rate Variability in Schizophrenia and Autism

Suppressed heart rate variability (HRV) has been found in a number of psychiatric conditions, including schizophrenia and autism. HRV is a potential biomarker of altered autonomic functioning that can predict future physiological and cognitive health. Understanding the HRV profiles that are unique to each condition will assist in generating predictive models of health. In the current study, we directly compared 12 adults with schizophrenia, 25 adults with autism, and 27 neurotypical controls on their HRV profiles. HRV was measured using an electrocardiogram (ECG) channel as part of a larger electroencephalography (EEG) study. All participants also completed the UCLA Loneliness Questionnaire as a measure of social stress. We found that the adults with schizophrenia exhibited reduced variability in R-R peaks and lower low frequency power in the ECG trace compared to controls. The HRV in adults with autism was slightly suppressed compared to controls but not significantly so. Interestingly, the autism group reported feeling lonelier than the schizophrenia group, and HRV did not correlate with feelings of loneliness for any of the three groups. However, suppressed HRV was related to worse performance on neuropsychological tests of cognition in the schizophrenia group. Together, this suggests that autonomic functioning is more abnormal in schizophrenia than in autism and could be reflecting health factors that are unique to schizophrenia.

Clinical Assessment, Genetics, and Treatment Approaches in Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) consists of a genetically heterogenous group of neurobehavioral disorders characterized by impairment in three behavioral domains including communication, social interaction, and stereotypic repetitive behaviors. ASD affects more than 1% of children in Western societies, with diagnoses on the rise due to improved recognition, screening, clinical assessment, and diagnostic testing. We reviewed the role of genetic and metabolic factors which contribute to the causation of ASD with the use of new genetic technology. Up to 40 percent of individuals with ASD are now diagnosed with genetic syndromes or have chromosomal abnormalities including small DNA deletions or duplications, single gene conditions, or gene variants and metabolic disturbances with mitochondrial dysfunction. Although the heritability estimate for ASD is between 70 and 90%, there is a lower molecular diagnostic yield than anticipated. A likely explanation may relate to multifactorial causation with etiological heterogeneity and hundreds of genes involved with a complex interplay between inheritance and environmental factors influenced by epigenetics and capabilities to identify causative genes and their variants for ASD. Behavioral and psychiatric correlates, diagnosis and genetic evaluation with testing are discussed along with psychiatric treatment approaches and pharmacogenetics for selection of medication to treat challenging behaviors or comorbidities commonly seen in ASD. We emphasize prioritizing treatment based on targeted symptoms for individuals with ASD, as treatment will vary from patient to patient based on diagnosis, comorbidities, causation, and symptom severity.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

White matter structure in schizophrenia and autism: Abnormal diffusion across the brain in schizophrenia

BACKGROUND

Schizophrenia and autism share many behavioral and neurological similarities, including altered white matter tract structure. However, because schizophrenia and autism are rarely compared directly, it is difficult to establish whether white matter abnormalities are disorder-specific or are common across these disorders that share some symptomatology.

METHODS

In the current study, we compared white matter water diffusion using tensor imaging in 25 adults with autism, 15 adults with schizophrenia, all with IQ scores above 88, and 19 neurotypical adults.

RESULTS

Although the three groups evinced no statistically significant differences in measures of fractional anisotropy (FA), the schizophrenia group showed significantly greater mean diffusivity (MD; Cohen's d > 0.77), due to greater radial diffusivity (RD; Cohen's d > 0.92), compared to both the autism and control groups. This effect was evident across the brain rather than specific to a particular tract.

CONCLUSIONS

The greater MD and RD in schizophrenia appears to be diagnosis-specific. The altered diffusion may reflect subtle abnormalities in myelination, which could be a potential mechanism underlying the widespread behavioral deficits associated with schizophrenia.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Evidence for regional hippocampal damage in patients with schizophrenia

PURPOSE

Schizophrenia patients show cognitive and mood impairments, including memory loss and depression, suggesting damage in the brain regions. The hippocampus is a brain structure that is significantly involved in memory and mood function and shows impairment in schizophrenia. In the present study, we examined the regional hippocampal changes in schizophrenia patients using voxel-based morphometry (VBM), Freesurfer, and proton magnetic resonance spectroscopy (¹H MRS) procedures.

METHODS

¹H MRS and high-resolution T1-weighted magnetic resonance imaging were collected in both healthy control subjects (N = 28) and schizophrenia patients (N = 28) using 3-Tesla whole body MRI system. Regional hippocampal volume was analyzed using VBM and Freesufer procedures. The relative ratios of the neurometabolites were calculated using linear combination model (LCModel).

RESULTS

Compared to controls, schizophrenia patients showed significantly decreased gray matter volume in the hippocampus. Schizophrenia patients also showed significantly reduced glutamate (Glu) and myo-inositol (mI) ratios in the hippocampus. Additionally, significant positive correlation between gray matter volume and Glu/tCr was also observed in the hippocampus in schizophrenia.

CONCLUSION

Our findings provide an evidence for a possible association between structural deficits and metabolic alterations in schizophrenia patients.

Load-dependent hyperdeactivation of the default mode network in people with schizophrenia

Schizophrenia is associated with impairment in a range of cognitive functions. Neuroimaging studies have reported lower, but also higher, task-induced activation accompanying impaired performance. Differences in task-load and the ability of people with schizophrenia (PSZ) to stay engaged in the cognitive operations probed appear to underlie such discrepancies. Similarly, task-induced deactivation of the default mode network (DMN) was weaker in PSZ relative to healthy control subjects (HCS) in most studies, but some reported greater deactivation. An inability to stay engaged in the cognitive operations could account for these discrepancies, too, as it would lead to more time off-task and consequently less deactivation of DMN functions. The present study employed a change detection paradigm with small to moderate set sizes (SSs) of 1, 2, and 4 items. Task training prior to fMRI scanning abolished the group difference in no-response trials. Task-positive regions of interest (ROIs) displayed greater activation with increasing SS in both groups. PSZ showed greater activation relative to HCS at SSs 1 and 2. DMN ROIs displayed greater deactivation with increasing SS in PSZ, but not in HCS, and PSZ tended to hyperdeactivate DMN regions at SS 4. No hypodeactivation was observed in PSZ. In conclusion, when minimizing differences in task-engagement, PSZ tend to over-recruit task-positive regions during low-load operations, and hyperdeactivate DMN functions at higher load, perhaps reflecting heightened non-specific vigilance or effort when dealing with cognitive challenges. This speaks against an inability to down-regulate task-independent thought processes as a primary mechanism underlying cognitive impairment in schizophrenia.

Aberrant spontaneous neural activity and correlation with evoked-brain potentials in first-episode, treatment-naïve patients with deficit and non-deficit schizophrenia

The goals of the study were to analyze spontaneous neural activity between deficit and non-deficit schizophrenia (DS, NDS) using resting-state fMRI, and to investigate the correlation of fMRI with clinical features and evoked brain potentials. The amplitude of low frequency fluctuation (ALFF) was measured in 41 DS participants, 42 NDS participants, and 42 healthy controls. ALFF in the bilateral cerebellum posterior lobe was significantly decreased in patients, while ALFF in the right fusiform gyrus and the bilateral putamen was significantly increased. In schizophrenia patients, ALFF in the right putamen positively correlated with excited/activation on Positive and Negative Syndrome Scale (PANSS-EXC/ACT). In DS patients, ALFF in the right insula was significantly increased than in controls and positively correlated with S2-P50 amplitude of sensory gating P50. ALFF in the left cerebellum posterior lobe negatively correlated with negative symptoms and withdrawn on PANSS (PANSS-NS, PANSS-WIT), ALFF in the right putamen positively correlated with PANSS-WIT. In NDS patients, ALFF in the middle temporal gyrus decreased than in controls and negatively correlated with P3b subcomponent of P300 latency. ALFF in the left cerebellum posterior lobe negatively correlated with PANSS-EXC/ACT. The middle temporal gyrus in NDS or the right insula in DS may show spatiotemporal defects.

In vivo microscopic voxel-based morphometry with a brain template to characterize strain-specific structures in the mouse brain

Hundreds of inbred mouse strains are established for use in a broad spectrum of basic research fields, including genetics, neuroscience, immunology, and cancer. Inbred mice exhibit identical intra-strain genetics and divergent inter-strain phenotypes. The cognitive and behavioral divergences must be controlled by the variances of structure and function of their brains; however, the underlying morphological features of strain-to-strain difference remain obscure. Here, in vivo microscopic magnetic resonance imaging was optimized to image the mouse brains by using an isotropic resolution of 80 μm. Next, in vivo templates were created from the data from four major inbred mouse strains (C57Bl/6, BALB/cBy, C3H/He, and DBA/2). A strain-mixed brain template was also created, and the template was then employed to establish automatic voxel-based morphometry (VBM) for the mouse brain. The VBM assessment revealed strain-specific brain morphologies concerning the gray matter volume of the four strains, with a smaller volume in the primary visual cortex for the C3H/He strain, and a smaller volume in the primary auditory cortex and field CA1 of the hippocampus for the DBA/2 strain. These findings would contribute to the basis of for understanding morphological phenotype of the inbred mouse strain and may indicate a relationship between brain morphology and strain-specific cognition and behavior.

Reduction of Interhemispheric Functional Connectivity in Sensorimotor and Visual Information Processing Pathways in Schizophrenia

Background:

Previous studies have demonstrated interhemispheric functional connectivity alterations in schizophrenia. However, the relationship between these alterations and the disease state of schizophrenia is largely unknown. Therefore, we aimed to investigate this relationship using voxel-mirrored homotopic connectivity (VMHC) method.

Methods:

This study enrolled 36 schizophrenia patients with complete remission, 58 schizophrenia patients with incomplete remission and 55 healthy controls. The VMHC was calculated based on resting-state functional magnetic resonance imaging data. Differences in VMHC among three groups were compared using one-way analysis of variance. A brain region with a significant difference in VMHC was defined as a region of interest (ROI), and the mean VMHC value in the ROI was extracted for the post hoc analysis, i.e., pair-wise comparisons across the three groups.

Results:

VMHC in the visual region (inferior occipital and fusiform gyri) and the sensorimotor region (paracentral lobule) showed significant differences among the three groups (P < 0.05, a false discovery rate method corrected). Pair-wise comparisons in the post hoc analysis showed that VMHC of the visual and sensorimotor regions in schizophrenia patients with complete remission and incomplete remission was lower than that in healthy controls (P < 0.05, Bonferroni corrected); however, there was no significant difference between the two patient subgroups.

Conclusions:

Interhemispheric functional connectivity in the sensorimotor and visual processing pathways was reduced in patients with schizophrenia, but this reduction was unrelated to the disease state; thus, this reduction may serve as a trait marker of schizophrenia.

Histological Underpinnings of Grey Matter Changes in Fibromyalgia Investigated Using Multimodal Brain Imaging

Chronic pain patients present with cortical gray matter alterations, observed with anatomical magnetic resonance (MR) imaging. Reduced regional gray matter volumes are often interpreted to reflect neurodegeneration, but studies investigating the cellular origin of gray matter changes are lacking. We used multimodal imaging to compare 26 postmenopausal women with fibromyalgia with 25 healthy controls (age range: 50-75 years) to test whether regional gray matter volume decreases in chronic pain are associated with compromised neuronal integrity. Regional gray matter decreases were largely explained by T1 relaxation times in gray matter, a surrogate measure of water content, and not to any substantial degree by GABA_A receptor concentration, an indirect marker of neuronal integrity measured with [¹⁸F] flumazenil PET. In addition, the MR spectroscopy marker of neuronal viability, N-acetylaspartate, did not differ between patients and controls. These findings suggest that decreased gray matter volumes are not explained by compromised neuronal integrity. Alternatively, a decrease in neuronal matter could be compensated for by an upregulation of GABA_A receptors. The relation between regional gray matter and T1 relaxation times suggests decreased tissue water content underlying regional gray matter decreases. In contrast, regional gray matter increases were explained by GABA_A receptor concentration in addition to T1 relaxation times, indicating perhaps increased neuronal matter or GABA_A receptor upregulation and inflammatory edema. By providing information on the histological origins of cerebral gray matter alterations in fibromyalgia, this study advances the understanding of the neurobiology of chronic widespread pain.

SIGNIFICANCE STATEMENT

Regional gray matter alterations in chronic pain, as detected with voxel-based morphometry of anatomical magnetic resonance images, are commonly interpreted to reflect neurodegeneration, but this assumption has not been tested. We found decreased gray matter in fibromyalgia to be associated with T1 relaxation times, a surrogate marker of water content, but not with GABA_A receptor concentration, a surrogate of neuronal integrity. In contrast, regional gray matter increases were partly explained by GABA_A receptor concentration, indicating some form of neuronal plasticity. The study emphasizes that voxel-based morphometry is an exploratory measure, demonstrating the need to investigate the histological origin of gray matter alterations for every distinct clinical entity, and advances the understanding of the neurobiology of chronic (widespread) pain.

Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part II: Cognition, neuroimaging and genetics

OBJECTIVES

Schizophrenia is a group of severe psychiatric disorders with high heritability but only low odds ratios of risk genes. Despite progress in the identification of pathophysiological processes, valid biomarkers of the disease are still lacking.

METHODS

This comprehensive review summarises recent efforts to identify genetic underpinnings, clinical and cognitive endophenotypes and symptom dimensions of schizophrenia and presents findings from neuroimaging studies with structural, functional and spectroscopy magnetic resonance imaging and positron emission tomography. The potential of findings to be biomarkers of schizophrenia is discussed.

RESULTS

Recent findings have not resulted in clear biomarkers for schizophrenia. However, we identified several biomarkers that are potential candidates for future research. Among them, copy number variations and links between genetic polymorphisms derived from genome-wide analysis studies, clinical or cognitive phenotypes, multimodal neuroimaging findings including positron emission tomography and magnetic resonance imaging, and the application of multivariate pattern analyses are promising.

CONCLUSIONS

Future studies should address the effects of treatment and stage of the disease more precisely and apply combinations of biomarker candidates. Although biomarkers for schizophrenia await validation, knowledge on candidate genomic and neuroimaging biomarkers is growing rapidly and research on this topic has the potential to identify psychiatric endophenotypes and in the future increase insight on individual treatment response in schizophrenia.

White matter volume change and its correlation with symptom severity in patients with schizophrenia: a VBM-DARTEL study

The aim of this study was to evaluate the white matter (WM) volume change and its correlation with symptom severity in patients with schizophrenia using voxel-based morphometry. A total of 20 patients with schizophrenia and 20 age-matched healthy controls participated in this study. MR image data were processed using SPM8 software with diffeomorphic anatomical registration through an exponentiated Lie algebra (DARTEL) algorithm. The patients with schizophrenia showed significant decreases (P=0.042) in the WM volumes of the temporal lobe and superior frontal gyrus compared with the healthy controls. The WM volumes of the middle temporal gyrus were negatively correlated with the scores of both the Positive Subscale (Pearson's ρ=-0.68, P=0.001) and the Negative Subscale (ρ=-0.71, P=0.0005) in the Positive and Negative Syndrome Scale. In addition, the scores of the General Psychopathology Subscale were negatively correlated with the WM volumes of the superior frontal gyrus (ρ=-0.68, P=0.0009). This study evaluated the WM volume of patients with schizophrenia compared with healthy controls using DARTEI-based voxel-based morphometry and also assessed the correlation of the localized WM volume changes with the Positive and Negative Syndrome Scale. These findings will be useful to understand the neuropathology associated with WM abnormality in schizophrenia.

Semi-automated registration-based anatomical labelling, voxel based morphometry and cortical thickness mapping of the mouse brain

BACKGROUND

Morphoanatomical MRI methods have recently begun to be applied in the mouse. However, substantial differences in the anatomical organisation of human and rodent brain prevent a straightforward extension of clinical neuroimaging tools to mouse brain imaging. As a result, the vast majority of the published approaches rely on tailored routines that address single morphoanatomical readouts and typically lack a sufficiently-detailed description of the complex workflow required to process images and quantify structural alterations.

NEW METHOD

Here we provide a detailed description of semi-automated registration-based procedures for voxel based morphometry, cortical thickness estimation and automated anatomical labelling of the mouse brain. The approach relies on the sequential use of advanced image processing tools offered by ANTs, a flexible open source toolkit freely available to the scientific community.

RESULTS

To illustrate our procedures, we described their application to quantify morphological alterations in socially-impaired BTBR mice with respect to normosocial C57BL/6J controls, a comparison recently described by us and other research groups. We show that the approach can reliably detect both focal and large-scale grey matter alterations using complementary readouts.

COMPARISON WITH EXISTING METHODS

No detailed operational workflows for mouse imaging are available for direct comparison with our methods. However, empirical assessment of the mapped inter-strain differences is in good agreement with the findings of other groups using analogous approaches.

CONCLUSION

The detailed operational workflows described here are expected to help the implementation of rodent morphoanatomical methods by non-expert users, and ultimately promote the use of these tools across the preclinical neuroimaging community.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (N_sz = 151) and healthy controls (N_hc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

A novel 3q29 deletion associated with autism, intellectual disability, psychiatric disorders, and obesity

Copy number variation (CNV) has been associated with a variety of neuropsychiatric disorders, including intellectual disability/developmental delay (ID/DD), autism spectrum disorder (ASD), and schizophrenia (SCZ). Often, individuals carrying the same pathogenic CNV display high clinical variability. By array-CGH analysis, we identified a novel familial 3q29 deletion (1.36 Mb), centromeric to the 3q29 deletion region, which manifests with variable expressivity. The deletion was identified in a 3-year-old girl diagnosed with ID/DD and autism and segregated in six family members, all affected by severe psychiatric disorders including schizophrenia, major depression, anxiety disorder, and personality disorder. All individuals carrying the deletion were overweight or obese, and anomalies compatible with optic atrophy were observed in three out of four cases examined. Amongst the 10 genes encompassed by the deletion, the haploinsufficiency of Optic Atrophy 1 (OPA1), associated with autosomal dominant optic atrophy, is likely responsible for the ophthalmological anomalies. We hypothesize that the haploinsufficiency of ATPase type 13A4 (ATP13A4) and/or Hairy/Enhancer of Split Drosophila homolog 1 (HES1) contribute to the neuropsychiatric phenotype, while HES1 deletion might underlie the overweight/obesity. In conclusion, we propose a novel contiguous gene syndrome due to a proximal 3q29 deletion variably associated with autism, ID/DD, psychiatric traits and overweight/obesity.

Effects of Long-Term Mindfulness Meditation on Brain's White Matter Microstructure and its Aging

Although research on the effects of mindfulness meditation (MM) is increasing, still very little has been done to address its influence on the white matter (WM) of the brain. We hypothesized that the practice of MM might affect the WM microstructure adjacent to five brain regions of interest associated with mindfulness. Diffusion tensor imaging was employed on samples of meditators and non-meditators (n = 64) in order to investigate the effects of MM on group difference and aging. Tract-Based Spatial Statistics was used to estimate the fractional anisotrophy of the WM connected to the thalamus, insula, amygdala, hippocampus, and anterior cingulate cortex. The subsequent generalized linear model analysis revealed group differences and a group-by-age interaction in all five selected regions. These data provide preliminary indications that the practice of MM might result in WM connectivity change and might provide evidence on its ability to help diminish age-related WM degeneration in key regions which participate in processes of mindfulness.

Structural insights into aberrant cortical morphometry and network organization in psychogenic erectile dysfunction

Functional neuroimaging studies have revealed abnormal brain dynamics of male sexual arousal (SA) in psychogenic erectile dysfunction (pED). However, the neuroanatomical correlates of pED are still unclear. In this work, we obtained cortical thickness (CTh) measurements from structural magnetic resonance images of 40 pED patients and 39 healthy control subjects. Abnormalities in CTh related to pED were explored using a scale space search based brain morphometric analysis. Organizations of brain structural covariance networks were analyzed as well. Compared with healthy men, pED patients showed significantly decreased CTh in widespread cortical regions, most of which were previously reported to show abnormal dynamics of male SA in pED, such as the medial prefrontal, orbitofrontal, cingulate, inferotemporal, and insular cortices. CTh reductions in these areas were found to be significantly correlated with male sexual functioning degradation. Moreover, pED patients showed decreased interregional CTh correlations from the right lateral orbitofrontal cortex to the right supramarginal gyrus and the left angular cortex, implying disassociations between the cognitive, motivational, and inhibitory networks of male SA in pED. This work provides structural insights on the complex phenomenon of psychogenic sexual dysfunction in men, and suggests a specific vulnerability factor, possibly as an extra "organic" factor, that may play an important role in pED.

Voxel-based morphometry of the marmoset brain: In vivo detection of volume loss in the substantia nigra of the MPTP-treated Parkinson's disease model

Movement dysfunction in Parkinson's disease (PD) is caused by the degeneration of dopaminergic (DA) neurons in the substantia nigra (SN). Here, we established a method for voxel-based morphometry (VBM) and automatic tissue segmentation of the marmoset monkey brain using a 7-T animal scanner and applied the method to assess DA degeneration in a PD model, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated animals, with tyrosine-hydroxylase staining. The most significant decreases of local tissue volume were detected in the bilateral SN of MPTP-treated marmoset brains (-53.0% in right and -46.5% in left) and corresponded with the location of DA neurodegeneration found in histology (-65.4% in right). In addition to the SN, the decreases were also confirmed in the locus coeruleus, and lateral hypothalamus. VBM using 7-T MRI was effective in detecting volume loss in the SN of the PD-model marmoset. This study provides a potential basis for the application of VBM with ultra-high field MRI in the clinical diagnosis of PD. The developed method may also offer value in automatic whole-brain evaluation of structural changes for the marmoset monkey.

The association between autism and schizophrenia spectrum disorders: A review of eight alternate models of co-occurrence

Although now believed to be two distinct disorders, autism spectrum disorders (ASD) and schizophrenia spectrum disorders (SSD) share multiple phenotypic similarities and risk factors, and have been reported to co-occur at elevated rates. In this narrative review, we give a brief overview of the phenomenological, genetic, environmental, and imaging evidence for the overlap between ASD and SSD, highlighting similarities and areas of distinction. We examine eight possible alternate models of explanation for the association and comorbidity between the disorders, and set out a research agenda to test these models. Understanding how and why these disorders co-occur has important implications for diagnosis, treatment, and prognosis, as well as for developing fundamental aetiological models of the disorders.

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Over the past decade, in-vivo MRI studies have provided many invaluable insights into the neural substrates underlying autism spectrum disorder (ASD), which is now known to be associated with neurodevelopmental variations in brain anatomy, functioning, and connectivity. These systems-level features of ASD pathology seem to develop differentially across the human lifespan so that the cortical abnormalities that occur in children with ASD differ from those noted at other stages of life. Thus, investigation of the brain in ASD poses particular methodological challenges, which must be addressed to enable the comparison of results across studies. Novel analytical approaches are also being developed to facilitate the translation of findings from the research to the clinical setting. In the future, the insights provided by human neuroimaging studies could contribute to biomarker development for ASD and other neurodevelopmental disorders, and to new approaches to diagnosis and treatment.

The reduction of volume and fiber bundle connections in the hippocampus of EGR3 transgenic schizophrenia rats

BACKGROUND AND OBJECTIVE

There is a growing consensus that schizophrenia is ultimately caused by abnormal communication between spatially disparate brain structures. White matter fasciculi represent the primary infrastructure for long distance communication in the brain. In this study, we aimed to investigate the white matter connection in schizophrenia susceptible brain regions of early growth response factor 3 (EGR3) expressing rats.

METHODS

A rat model of schizophrenia was created by the transfection of the EGR3 gene into rat hippocampus. All animals were placed in a fixation system using a commercial rat-dedicated coil. Schizophrenia susceptible brain regions were scanned using in vivo diffusion tensor magnetic resonance imaging. The volume, quantity, average length of fiber bundles, fractional anisotropy, apparent diffusion coefficient, the relative heterosexual fraction, and volume ratio were collected in the whole brain and schizophrenia related brain areas (the hippocampus, thalamus, and prefrontal lobe). MedINRIA software was used for data processing of diffusion tensor and fiber bundles tracking. The fibronectin in relevant brain regions was also analyzed.

RESULTS

There was a significant decrease in the volume of the fiber beam through the left hippocampus dentate in the schizophrenia model group in comparison to the control group and the risperidone treatment group (P<0.05). A significant reduction in the volume and number of the fiber bundles was also observed in left prefrontal-left hippocampus, left hippocampus-left thalamus, left prefrontal-left hippocampus-left thalamus areas in the model group (all P<0.05).

CONCLUSION

The volume of hippocampus and the number of fiber bundles were reduced in EGR3 transgenic schizophrenia rats, and are the most sensitive indicators in schizophrenia. The diffusion tensor imaging technique plays an important role in the evaluation of patients with schizophrenia.

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.

The effects of methylphenidate on whole brain intrinsic functional connectivity

Methylphenidate (MPH) is an indirect dopaminergic and noradrenergic agonist that is used to treat attention deficit hyperactivity disorder and that has shown therapeutic potential in neuropsychiatric diseases such as depression, dementia, and Parkinson's disease. While effects of MPH on task-induced brain activation have been investigated, little is known about how MPH influences the resting brain. To investigate the effects of 40 mg of oral MPH on intrinsic functional connectivity, we used resting state fMRI in 54 healthy male subjects in a double-blind, randomized, placebo-controlled study. Functional connectivity analysis employing ICA revealed seven resting state networks (RSN) of interest. Connectivity strength between the dorsal attention network and the thalamus was increased after MPH intake. Other RSN located in association cortex areas, such as the left and right frontoparietal networks and the executive control network, showed MPH-induced connectivity increase to sensory-motor and visual cortex regions and connectivity decrease to cortical and subcortical components of cortico-striato-thalamo-cortical circuits (CST). RSN located in sensory-motor cortex areas showed the opposite pattern with MPH-induced connectivity increase to CST components and connectivity decrease to sensory-motor and visual cortex regions. Our results provide evidence that MPH does not only alter intrinsic connectivity between brain areas involved in sustained attention, but that it also induces significant changes in the cortico-cortical and cortico-subcortical connectivity of many other cognitive and sensory-motor RSN.

Motor function deficits in schizophrenia: an fMRI and VBM study

INTRODUCTION

To investigate whether the motor functional alterations in schizophrenia (SZ) are also associated with structural changes in the related brain areas using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM).

METHODS

A sample of 14 right-handed SZ patients and 14 right-handed healthy control subjects matched for age, sex, and education were examined with structural high-resolution T1-weighted MRI; fMRI images were obtained during right index finger-tapping task in the same session.

RESULTS

fMRI results showed reduced functional activation in the motor areas (contralateral precentral and postcentral gyrus) and ipsilateral cerebellum in SZ subjects as compared to healthy controls (n = 14). VBM analysis also revealed reduced grey matter in motor areas and white matter reduction in cerebellum of SZ subjects as compared to controls.

CONCLUSION

The present study provides an evidence for a possible association between structural alterations in the motor cortex and disturbed functional activation in the motor areas in persons affected with SZ during a simple finger-tapping task.

Assessment of white matter integrity of autistic preschool children with diffusion weighted MR imaging

The purpose was to assess white matter integrity of autistic preschool children with diffusion weighted MR imaging. Prospective study was carried on 19 autistic children (mean age 55.2ms, IQ of 86.5) and 10 sex, age and IQ matched control (mean age 53.2ms, IQ 84.5). The childhood Autism Rating Scale (CARS), social age and language age were calculated. Patients and controls underwent diffusion weighted MR imaging of the brain with b factor of 0, 500 and 1000s/mm(2). The apparent diffusion coefficient (ADC) value at different regions of the white matter were calculated and correlated with CARS, social age and language age. There were significant differences at the ADC value of the white matter between autistic and control children at genu (P=0.043), splenium (P=0.003) of the corpus callosum, frontal white matter (P=0.015) and temporal white matter (P=0.020). There was positive correlation of CARS score with ADC value of the genu (r=0.63, P=0.001), splenium (r=0.59, P=0.005), frontal white matter (r=0.81, P=0.001) and temporal white matter (r=0.74, P=0.001). The social age well correlated with ADC value of the frontal white matter (r=0.81, P=0.001) and language age well correlated with ADC value of the temporal white matter (r=0.78, P=0.001). We concluded that ADC value can be helpful in assessment of integrity of the white matter in autistic preschool children and well correlated with CARS score, social age and language age.

CNTNAP2 polymorphisms and structural brain connectivity: a diffusion-tensor imaging study

CNTNAP2 is a gene on chromosome 7 that has shown associations with autism and schizophrenia, and there is evidence that it plays an important role for neuronal synchronization and brain connectivity. In this study, we assessed the relationship between Diffusion Tensor Imaging (DTI), a putative marker of anatomical brain connectivity, and multiple single nucleotide polymorphisms (SNPs) spread out over this large gene. 81 healthy controls and 44 patients with schizophrenia (all Caucasian) underwent DTI and genotyping of 31 SNPs within CNTNAP2. We employed Tract-based Spatial Statistics (TBSS) for inter-subject brain registration and computed average diffusivity values for six major white matter tracts. Analyses of Covariance (ANCOVAs) were computed to test for possible associations with genotypes. The strongest association, which survived rigorous Bonferroni correction, was between rs2710126 genotype and Fractional Anisotropy (FA) in the uncinate fasciculus (p = .00003). This anatomical location is particularly interesting given the enriched fronto-temporal expression of CNTNAP2 in the developing brain. For this SNP, no phenotype association has been reported before. There were several further genotype-DTI associations that were nominally significant but did not survive Bonferroni correction, including an association between axial diffusivity in the dorsal cingulum bundle and a region in intron 13 (represented by rs2710102, rs759178, rs2538991), which has previously been reported to be associated with anterior-posterior functional connectivity. We present new evidence about the effects of CNTNAP2 on brain connectivity, whose disruption has been hypothesized to be central to schizophrenia pathophysiology.

Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study

Brain tissue changes in autism spectrum disorders seem to be rather subtle and widespread than anatomically distinct. Therefore a multimodal, whole brain imaging technique appears to be an appropriate approach to investigate whether alterations in white and gray matter integrity relate to consistent changes in functional resting state connectivity in individuals with high functioning autism (HFA). We applied diffusion tensor imaging (DTI), voxel-based morphometry (VBM) and resting state functional connectivity magnetic resonance imaging (fcMRI) to assess differences in brain structure and function between 12 individuals with HFA (mean age 35.5, SD 11.4, 9 male) and 12 healthy controls (mean age 33.3, SD 9.0, 8 male). Psychological measures of empathy and emotionality were obtained and correlated with the most significant DTI, VBM and fcMRI findings. We found three regions of convergent structural and functional differences between HFA participants and controls. The right temporo-parietal junction area and the left frontal lobe showed decreased fractional anisotropy (FA) values along with decreased functional connectivity and a trend towards decreased gray matter volume. The bilateral superior temporal gyrus displayed significantly decreased functional connectivity that was accompanied by the strongest trend of gray matter volume decrease in the temporal lobe of HFA individuals. FA decrease in the right temporo-parietal region was correlated with psychological measurements of decreased emotionality. In conclusion, our results indicate common sites of structural and functional alterations in higher order association cortex areas and may therefore provide multimodal imaging support to the long-standing hypothesis of autism as a disorder of impaired higher-order multisensory integration.

ADHD and autism: differential diagnosis or overlapping traits? A selective review

According to DSM-IV TR and ICD-10, a diagnosis of autism or Asperger Syndrome precludes a diagnosis of attention-deficit/hyperactivity disorder (ADHD). However, despite the different conceptualization, population-based twin studies reported symptom overlap, and a recent epidemiologically based study reported a high rate of ADHD in autism and autism spectrum disorders (ASD). In the planned revision of the DSM-IV TR, dsm5 (www.dsm5.org), the diagnoses of autistic disorder and ADHD will not be mutually exclusive any longer. This provides the basis of more differentiated studies on overlap and distinction between both disorders. This review presents data on comorbidity rates and symptom overlap and discusses common and disorder-specific risk factors, including recent proteomic studies. Neuropsychological findings in the areas of attention, reward processing, and social cognition are then compared between both disorders, as these cognitive abilities show overlapping as well as specific impairment for one of both disorders. In addition, selective brain imaging findings are reported. Therapeutic options are summarized, and new approaches are discussed. The review concludes with a prospectus on open questions for research and clinical practice.

Assessment and treatment in autism spectrum disorders: a focus on genetics and psychiatry

Autism spectrum disorders (ASDs) are neurobehavioral disorders characterized by abnormalities in three behavioral domains including social interaction, impaired communication, and repetitive stereotypic behaviors. ASD affects approximately 1% of children and is on the rise with significant genetic mechanisms underlying these disorders. We review the current understanding of the role of genetic and metabolic factors contributing to ASD with the use of new genetic technology. Fifty percent is diagnosed with chromosomal abnormalities, small DNA deletions/duplications, single-gene conditions, or metabolic disturbances. Genetic evaluation is discussed along with psychiatric treatment and approaches for selection of medication to treat associated challenging behaviors or comorbidities seen in ASD. We emphasize the importance of prioritizing treatment based on target symptom clusters and in what order for individuals with ASD, as the treatment may vary from patient to patient.