Comparative Neuropsychiatry: White Matter Abnormalities in Children and Adolescents with Schizophrenia, Bipolar Affective Disorder, and Obsessive-Compulsive Disorder

White, Tonya; Langen, Carolyn D.; Schmidt, Marcus; Hough, Morgan; James, Anthony

doi:10.1016/j.eurpsy.2014.10.003

Cited by 13 publications

(14 citation statements)

References 76 publications

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“…In general, demyelinating diseases are classified as neurological diseases but not psychiatric diseases. However, cognitive dysfunction is observed in patients experiencing demyelination (Malkki 2015), and white matter abnormalities are found in many SCZ patients (White et al 2015). In our previous study using demyelinating PLP 4e/À mice, we observed cognitive dysfunction prior to the onset of demyelination (Tanaka et al 2009).…”

Section: Discussionmentioning

confidence: 85%

“…An imbalance of the inhibitory-excitatory activities within the neural network in the CNS can cause psychiatric diseases (Cui et al 2016). White matter abnormalities can also play roles in psychiatric diseases, especially during the juvenile and adolescent periods (White et al 2015). Several studies have demonstrated that a decrease in oligodendrocyte number and altered expression of myelin/oligodendrocyte-related genes in schizophrenia (SCZ) patients could affect white matter morphology and neuronal connectivity, which are characteristic features of SCZ (Martins-de-Souza et al 2009).…”

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confidence: 99%

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Dysregulation of schizophrenia‐related aquaporin 3 through disruption of paranode influences neuronal viability

Kunisawa

Shimizu

Kushima

et al. 2018

Journal of Neurochemistry

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Myelinated axons segregate the axonal membrane into four defined regions: the node of Ranvier, paranode, juxtaparanode, and internode. The paranodal junction consists of specific component proteins, such as neurofascin155 (NF155) on the glial side, and Caspr and Contactin on the axonal side. Although paranodal junctions are thought to play crucial roles in rapid saltatory conduction and nodal assembly, the role of their interaction with neurons is not fully understood. In a previous study, conditional NF155 knockout in oligodendrocytes led to disorganization of the paranodal junctions. To examine if disruption of paranodal junctions affects neuronal gene expression, we prepared total RNA from the retina of NF155 conditional knockout, and performed expression analysis. We found that the expression level of 433 genes changed in response to paranodal junction ablation. Interestingly, expression of aquaporin 3 (AQP3) was significantly reduced in NF155 conditional knockout mice, but not in cerebroside sulfotransferase knockout (CST-KO) mice, whose paranodes are not originally formed during development. Copy number variations have an important role in the etiology of schizophrenia (SCZ). We observed rare duplications of AQP3 in SCZ patients, suggesting a correlation between abnormal AQP3 expression and SCZ. To determine if AQP3 over-expression in NF155 conditional knockout mice influences neuronal function, we performed adeno-associated virus (AAV)-mediated over-expression of AQP3 in the motor cortex of mice and found a significant increase in caspase 3-dependent neuronal apoptosis in AQP3-transduced cells. This study may provide new insights into therapeutic approaches for SCZ by regulating AQP3 expression, which is associated with paranodal disruption.

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Section: Discussionmentioning

confidence: 85%

mentioning

confidence: 99%

Dysregulation of schizophrenia‐related aquaporin 3 through disruption of paranode influences neuronal viability

Kunisawa

Shimizu

Kushima

et al. 2018

Journal of Neurochemistry

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“…In summary, results from the current study suggest that inferences regarding the spatial distribution of SSA across multiple individuals following various z-transform methodologies are not statistically valid unless an independent third group is utilized for the initial z-transform. Although it has been repeatedly demonstrated that the LOO, IDS, EZ-MAP and DisCo-Z methods eliminate group-wise bias when SSA are summed across individuals (Bouix et al 2013; Kim et al 2013; Mayer et al 2014; Mayer et al 2017), recent attempts to quantify the number of individuals who exhibit SSA within the same voxel/region (Bouix et al 2013; Hayes et al 2015; Meier et al 2016; Miller et al 2016; Miller et al 2016; Solmaz et al 2017; Taber et al 2015; White et al 2013; White et al 2015) or compute weighted statistics based on the proportion of individuals with extreme values (Ware et al 2017) may be statistically flawed depending on the transform method utilized. Specifically, any SSA z-transform (e.g., LOO and DisCo-Z methods) that utilizes the first two statistical moments (mean and standard deviation) for both the transform itself as well as for subsequent comparison purposes will produce a bias in the spatial location of extrema.…”

Section: Resultsmentioning

confidence: 99%

“…More recently, a growing number of investigators have examined the spatial overlap of extrema across subjects within individual voxels or regions of interest (ROI) following various z-transform methods (Bouix et al 2013; Hayes et al 2015; Meier et al 2016; Miller et al 2016; Miller et al 2016; Solmaz et al 2017; Taber et al 2015; White et al 2013; White et al 2015). Specifically, these studies have quantified the number of individuals who exhibit SSA within the same voxel or region rather than collectively examining the total number of SSA across all voxels/regions.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution bias in subject-specific abnormalities analyses

Dodd

Ling

Bedrick

et al. 2018

Brain Imaging and Behavior

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The neuroimaging community has seen a renewed interest in algorithms that provide a location-independent summary of subject-specific abnormalities (SSA) to assess individual lesion load. More recently, these methods have been extended to assess whether multiple individuals within the same cohort exhibit extrema in the same spatial location (e.g., voxel or region of interest). However, the statistical validity of this approach has not been rigorously established. The current study evaluated the potential for a spatial bias in the distribution of SSA using several common z-transformation algorithms (leave-one-out [LOO]; independent sample [IDS]; Enhanced Z-Score Microstructural Assessment of Pathology [EZ-MAP]; distribution-corrected z-scores [DisCo-Z]) using both simulated data and DTI data from 50 healthy controls. Results indicated that methods which z-transformed data based on statistical moments from a reference group (LOO, DisCo-Z) led to bias in the spatial location of extrema for the comparison group. In contrast, methods that z-transformed data using an independent third group (EZ-MAP, IDS) resulted in no spatial bias. Importantly, none of the methods exhibited bias when results were summed across all individual elements. The spatial bias is primarily driven by sampling error, in which differences in the mean and standard deviation of the untransformed data have a higher probability of producing extrema in the same spatial location for the comparison but not reference group. In conclusion, evaluating SSA overlap within cohorts should be either be avoided in deference to established group-wise comparisons or performed only when data is available from an independent third group.

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“…White et al (2009) [ 65 ] discovered white matter “potholes”, which are FA hypo-intensities that are spatially unique to each subject. While pothole locations often do not overlap between subjects, the occurrence of potholes tends to be higher in subject groups than in controls [ 65 , 67 , 68 ].…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

et al. 2015

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Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

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Comparative Neuropsychiatry: White Matter Abnormalities in Children and Adolescents with Schizophrenia, Bipolar Affective Disorder, and Obsessive-Compulsive Disorder

Abstract: White matter abnormalities in early-onset schizophrenia and bipolar affective disorder are more global in nature, whereas children and adolescents with obsessive-compulsive disorder do not show widespread differences in FA.

Cited by 13 publications

References 76 publications

Dysregulation of schizophrenia‐related aquaporin 3 through disruption of paranode influences neuronal viability

Dysregulation of schizophrenia‐related aquaporin 3 through disruption of paranode influences neuronal viability

Spatial distribution bias in subject-specific abnormalities analyses

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

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