Hippocampal shape analysis: surface-based representation and classification

Schizoaffective disorder (SA) is distinguished from schizophrenia (SZ) based on the presence of prominent mood symptoms over the illness course. Despite this clinical distinction, SA and SZ patients are often combined in research studies, in part because data supporting a distinct pathophysiological boundary between the disorders are lacking. Indeed, few studies have addressed whether neurobiological abnormalities associated with SZ, such as the widely replicated reduction and delay of the P300 event-related potential (ERP), are also present in SA. Scalp EEG was acquired from patients with DSM-IV SA (n = 15) or SZ (n = 22), as well as healthy controls (HC; n = 22) to assess the P300 elicited by infrequent target (15%) and task-irrelevant distractor (15%) stimuli in separate auditory and visual ”oddball” tasks. P300 amplitude was reduced and delayed in SZ, relative to HC, consistent with prior studies. These SZ abnormalities did not interact with stimulus type (target vs. task-irrelevant distractor) or modality (auditory vs. visual). Across sensory modality and stimulus type, SA patients exhibited normal P300 amplitudes (significantly larger than SZ patients and indistinguishable from HC). However, P300 latency and reaction time were both equivalently delayed in SZ and SA patients, relative to HC. P300 differences between SA and SZ patients could not be accounted for by variation in symptom severity, socio-economic status, education, or illness duration. Although both groups show similar deficits in processing speed, SA patients do not exhibit the P300 amplitude deficits evident in SZ, consistent with an underlying pathophysiological boundary between these disorders.

Neuropsychological correlates of P300 abnormalities in patients with schizophrenia and obsessive–compulsive disorder

20. Movement sequencing abnormalities in schizophrenia: Changes in cortical activity during finger-tapping task

Comparative morphometric study of the septum and cavum septi pellucidi in patients with schizophrenia, mood disorders and control subjects

Chapter 11 - Converging evidence for gamma synchrony deficits in schizophrenia

We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.

Electroencephalographic Cerebral Dysrhythmic Abnormalities in the Trinity of Nonepileptic General Population, Neuropsychiatric, and Neurobehavioral Disorders

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.

Shape modeling is an important area in computer vision and medical imaging. Shape models have proved valuable in the tasks of object representation, recognition, and classification. This thesis focuses on shape models of 3D surfaces extracted from volumetric images and their applications in medical image analysis. This thesis presents a new framework for classification of 3D neuroanatomic structures using shape features, with the goal of deriving a shape-based medical classifier that has clinical value. This framework combines a powerful 3D surface modeling method, the spherical harmonics (SPHARM) description, with a set of effective pattern classification, feature selection, evaluation, and visualization techniques. This approach is shown to be effective using simulated shape data. It is also applied to real hippocampal data, and achieves good cross-validation accuracy (>90%) in detecting hippocampal shape changes in schizophrenia, which is competitive with the best results in previous studies. To help medical diagnosis in practice, a threshold-free receiver operating characteristic approach is employed as a means of evaluating the potential of a classifier. In addition, a new visualization approach is proposed for localizing discriminative patterns. This thesis also considers two extensions of the above work. In one extension, the left and right hippocampi are treated as a single shape configuration, and a new multi-surface alignment algorithm is developed for aligning configurations of multiple surfaces simultaneously. This alignment algorithm is incorporated into our classification for hippocampal data. The best cross-validation accuracy achieved is 92%, supporting the idea that the shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. In the other extension, we focus on spherical parameterization, which is the key step for deriving a SPHARM description for a 3D object. Earlier techniques can be applied only to voxel surfaces. We propose CALD, a new spherical parameterization algorithm which makes our framework applicable to general triangle meshes. This new algorithm can be used for shape representation and manipulation purposes in different areas, including medical image analysis, computer vision, graphics, and multimedia databases.

In any DEM simulation, the chosen particle shape will greatly influence the simulated material behaviour. For a specific material, e.g. railway ballast, it remains an open question how to model the particle shape, such that DEM simulations are computationally efficient and simulation results are in good accordance with measurements. While DEM shape modelling for railway ballast is well addressed in the literature, approaches mainly aim at approximating the stones’ actual shape, resulting in rather complex and thus inefficient particle shapes. In contrast, very simple DEM shapes will be constructed, clumps of three spheres, which aim to approximate shape descriptors of the considered ballast material. In DEM simulations of the packing behaviour, a set of clump shapes is identified, which can pack at porosities observed at track sites, as well as in lab tests. The relation between particle shape (descriptors) and obtained packing (characteristic) is investigated in a correlation analysis. The simulated packing’s porosity is strongly correlated to four shape descriptors, which are also strongly correlated among each other. Thus, to derive simple shape models of a given particle shape, matching one of these shape descriptors, might be a good first step to bring simulated porosities closer to measured ones. The conducted correlation analysis also shows that packing’s coordination number and isotropic fabric are correlated to more shape descriptors, making it more difficult to estimate the effect of particle shape on these quantities.Graphic abstract

At present, little is known about brain functional connectivity and its small-world topologic properties in first-episode schizophrenia (SZ) patients during cool executive function task. In this paper, the Trail Making Test-B (TMT-B) task was used to evaluate the cool executive function of first-episode SZ patients and electroencephalography (EEG) data were recorded from 14 first-episode SZ patients and 14 healthy controls during this cool executive function task. Brain functional connectivity between all pairs of EEG channels was constructed based on mutual information (MI) analysis. The constructed brain functional networks were filtered by three thresholding schemes: absolute threshold, mean degree, and a novel data-driven scheme based on orthogonal minimal spanning trees (OMST), and graph theory was then used to study the topographical characteristics of the filtered brain graphs. Results indicated that the graph theoretical measures of the theta band showed obvious difference between SZ patients and healthy controls. In the theta band, the characteristic path length was significantly longer and the cluster coefficient was significantly smaller in the SZ patients for a wide range of absolute threshold T. However, the cluster coefficient showed no significant changes, and the characteristic path length was still significantly longer in SZ patients when calculated as a function of mean degree K. Interestingly, we also found that only the characteristic path length was significantly longer in SZ patients compared with healthy controls after using the OMST scheme. Pearson correlation analysis showed that the characteristic path length was positively correlated with executive time of TMT-B for the combined SZ patients and healthy controls (r = 0.507, P = 0.006), but not for SZ patients alone (r = 0.072, P = 0.612). The above results suggested a less optimal organization of the brain network and could be useful for understanding the pathophysiologic mechanisms underlying cool executive dysfunction in first-episode SZ patients.

Despite the many neuroimaging studies that suggest gray matter volume reductions in schizophrenia, there is no compelling postmortem evidence to suggest neuronal loss, nor is there a distinctive or specific signature of gray matter abnormalities in schizophrenia. Instead, there appears to be no observed focal lesion that characterizes gray matter pathology in schizophrenia. This state of affairs has led to a movement away from conceptualizing schizophrenia as resulting from a single lesion, to conceptualizing schizophrenia as arising from abnormal communication between brain regions. Abnormal connectivity between brain regions is not a new concept, as Bleuler (1911) postulated such abnormalities. What is new, however, is an appreciation that brain regions that are not spatially proximal may be connected functionally into neural networks and that to understand altered neural connectivity we need to have a better understanding of the connections between brain regions which are made possible by white matter, the main infrastructure in the brain that makes possible long distance communication among neurons. Accordingly, a focus on white matter connections in the brain has become increasingly of interest in schizophrenia, particularly in the areas of imaging studies, postmortem studies, and new animal models of schizophrenia. This special issue focuses on schizophrenia and oligodendrocytes, the latter a class of neuroglia that give rise to myelin. The primary aim of this special issue is to understand and to clarify further white matter pathology in schizophrenia and how it may contribute to disconnectivity among brain regions, which results in the observed cognitive, behavioral, and clinical symptoms in this disorder. The first paper begins with a description of the theory that schizophrenia reflects a disorder of connectivity. The proposition put forth is that schizophrenia is, at least in part, the result of abnormal communication between brain regions that may be spatially distant but nonetheless functionally connected. As white matter is the main infrastructure for brain connectivity, this paper focuses on the use of a magnetic resonance imaging (MRI) technique called diffusion tensor imaging (DTI) to gain insight into the role of white matter abnormalities in schizophrenia. This paper also focuses on the functional implications of white matter abnormalities, particularly with regard to the possible role of myelin in modulating the transmission velocity of neural discharges. The paper ends with a speculative hypothesis about the relationship between gray matter and white matter abnormalities in schizophrenia. The second paper also focuses on altered neuronal connectivity. Here, however, the focus is on altered connectivity and impaired myelination in a postmortem study of schizophrenia and normal controls, where electron microscopy is used to study myelinated fibers and oligodendrocytes. This morphometric study examines myelinated fibers in the prefrontal cortex in both gray and white matter. Six types of abnormal fibers and ultrastructural alterations are described in the schizophrenia sample. The study reveals increased pathological fibers in gray matter in both young and elderly patients that are associated with positive symptoms, while in elderly patients, the frequency of pathological fibers in white matter is increased and these changes are associated with more negative symptoms. The key implication here is that altered myelinated fibers in white matter in schizophrenia likely follow alterations of myelinated fibers in gray matter that occur earlier in the course of illness. The third paper is a postmortem study on age-related changes in the number of oligodendrocytes in prefrontal cortex in schizophrenia, bipolar disorder and major depression. The study reveals an age-related increase in numerical density of oligodendrocytes in layer VI and adjacent white matter of BA9 and 10 that is seen only in normal controls and not in schizophrenia or in mood disorders. The absence of the normal age-related increase in oligodendrocytes in patients suggests that this aspect of normal brain development is dysregulated in schizophrenia and in mood disorders, and confirms postmortem and imaging data, further highlighting the fact that the absence of a normal age-related increase in oligodendrocytes is a key shared feature in these psychiatric populations. The fourth paper reviews white matter abnormalities in schizophrenia, including imaging and postmortem findings on the disconnectivity theory of schizophrenia, a major theme that underlies the three previous papers in this special issue. This paper then reviews some of the white matter diseases that commonly lead to psychotic symptoms and then reviews transgenic and mutant mouse models of schizophrenia. More specifically, genetic mouse models such as Plp1 transgenic mice and mutant mice heterogeneous for either NRG1 or its receptor erbB4, as well as Nogo-A- deficient mice are reviewed. This is followed by a focus on the myelin toxicity model of mice fed cuprizone. In the early stage (the second and third week) of the cuprizone feeding, mice show higher dopamine levels and lower norepinephrine levels in their prefrontal cortex, along with behavioral changes indicative of increased CNS activity. In the late stage (weeks 4–6), when demyelination and oligodendrocyte loss are obvious, mice display cognitive deficits as well as deficits in social interactions that are reminiscent of social withdrawal seen in patients with schizophrenia. Importantly, this cuprizone-feeding mouse may be used as an in vivo platform for psychopharmacological studies testing the effects of antipsychotic drugs on white matter alterations and associated behavioral changes, in addition to providing a new animal model of schizophrenia. The fifth and final paper of this special issue focuses on abnormal behavior and microstructural changes in juvenile mice that are repeatedly exposed to nonneurotoxic levels of amphetamine. These mice are compared with nonamphetamine treated mice. Oligodendrocyte numbers and three proteins expressed in mature oligodendrocytes are investigated in these young male mice at sacrifice. Treated mice showed higher locomotion and impaired spatial working memory, along with lowered Noga-A and GST-pi proteins, and lower MBP proteins, in frontal cortex and hippocampus and fewer mature oligodendrocytes in frontal cortex and corpus callosum, as well as lower MBP staining in frontal cortex, corpus callosum, and hippocampus, than untreated mice. These differences suggest that in wild-type mice late developing white matter is vulnerable to amphetamine and may lead to compromised white matter with increased dopamine in specific brain regions. Not only do these findings explain well the increased locomotion and impaired spatial working memory observed in amphetamine-treated mice, but also may serve as a principle for the amphetamine-treated mouse being used as a novel animal model of schizophrenia. To summarize, compared to the focus on gray matter and neurons, research on white matter and myelin/glial has been sparse in schizophrenia. In this special issue, neuroimaging findings are presented that suggest that myelin abnormalities in schizophrenia may underlie the white matter abnormalities observed using diffusion tensor imaging techniques. Data from postmortem studies also confirm the presence of oligodendrocyte and myelin abnormalities in schizophrenia as well as mood disorders. Oligodendrocytes provide protection and improve communication between brain regions and the oligodendroglia-producing myelin is approaching maturity in the same time frame as the onset of symptoms. Further, normal age-related white matter increases and related gray matter changes that likely predate the white matter changes are dysregulated in schizophrenia and may contribute to the clinical symptomatology of the disease. Finally, the recently developed animal models are not only of help in understanding connectivity abnormalities in schizophrenia but also provide novel platforms to test novel therapeutic approaches for schizophrenic patients. Treatments specifically targeting white matter, for example, may improve on the efficacy of those treatments currently in use. Martha E. Shenton Haiyun Xu Vahram Haroutunian George Bartzokis

BackgroundAlthough magnetoencephalography (MEG) studies show superior temporal gyrus (STG) auditory processing abnormalities in schizophrenia at 50 and 100 ms, EEG and corticography studies suggest involvement of additional brain areas (e.g., frontal areas) during this interval. Study goals were to identify 30 to 130 ms auditory encoding processes in schizophrenia (SZ) and healthy controls (HC) and group differences throughout the cortex.MethodsThe standard paired-click task was administered to 19 SZ and 21 HC subjects during MEG recording. Vector-based Spatial–temporal Analysis using L1-minimum-norm (VESTAL) provided 4D maps of activity from 30 to 130 ms. Within-group t-tests compared post-stimulus 50 ms and 100 ms activity to baseline. Between-group t-tests examined 50 and 100 ms group differences.ResultsBilateral 50 and 100 ms STG activity was observed in both groups. HC had stronger bilateral 50 and 100 ms STG activity than SZ. In addition to the STG group difference, non-STG activity was also observed in both groups. For example, whereas HC had stronger left and right inferior frontal gyrus activity than SZ, SZ had stronger right superior frontal gyrus and left supramarginal gyrus activity than HC.ConclusionsLess STG activity was observed in SZ than HC, indicating encoding problems in SZ. Yet auditory encoding abnormalities are not specific to STG, as group differences were observed in frontal and SMG areas. Thus, present findings indicate that individuals with SZ show abnormalities in multiple nodes of a concurrently activated auditory network.

There is abundant evidence that neurocognitive deficits are present in schizophrenic patients (Aylward et al.  ). A recent meta-analytical review of  neurocognitive test variables concluded that schizophrenia is characterized by a broadly based, moderately severe (d =  .  ) cognitive impairment, with varying degrees of deficit in all domains (Heinrichs & Zakzanis  ). In comparison with learning disability these deficits are mild, with around  % of patients scoring in the deficit range on most tests of cognitive function (Green  ). This review will summarize some of the key findings regarding the relationship of cognitive deficits to the aetiology of schizophrenia.

Background: Neurological soft signs (NSS), i.e. subtle neurological abnormalities, have been frequently found in schizophrenia. Neuroimaging studies in schizophrenia have shown abnormal cortical thickness changes across the cortical mantle. However, few studies have examined relationships between NSS and cortical thickness abnormalities in schizophrenia. Method: A sample of 18 patients with chronic schizophrenia and 20 age-matched healthy controls were included. Cortical thickness was assessed on high-resolution 3-tesla magnetic resonance imaging by using FreeSurfer software and NSS were rated on the Heidelberg Scale. Results: Significant negative correlations between NSS and cortical thickness were found in the prefrontal, inferior temporal, superior parietal, postcentral, and supramarginal cortices in the schizophrenia patients. In the controls, however, this negative correlation was found in the anterior cingulate, pericalcarine and superior/middle temporal regions. Conclusion: Our results not only confirmed the association between NSS and cortical thickness in chronic schizophrenia but also indicated that patients and controls have different anatomical substrates of NSS.