Latent volumetric structure of the human brain: Exploratory factor analysis and structural equation modeling of gray matter volumes in healthy children and adults

Assessing Patients' Attitudes towards Telepsychotherapy: The Development of the Unified Theory of Acceptance and Use of Technology -Patient Version

This study presents the development of a self-report measure of patients' attitudes towards telepsychotherapy. The measure is based on a well-researched model of attitudes towards using technology, the Unified Theory of Acceptance and Use of Technology (UTAUT) framework (Venkatesh et al., 2003). We examined the psychometric properties of the UTAUT adapted for psychotherapy patients (UTAUT-P) in a sample of 107 psychotherapy patients who received telepsychotherapy via videoconferencing during the COVID-19 pandemic. Exploratory factor analysis resulted in a 14-item UTAUT-P version, with four factors; 1) Therapy Quality Expectancy, 2) Convenience, 3) Ease of Use, and 4) Pressure from Others, and was further corroborated by the results of the confirmatory factor analysis. Our results indicated the four-factor model's adequate fit to the data and demonstrated adequate construct validity and reliability of the UTAUT-P factors. All factors, except for Ease of Use, were significantly and positively associated with intention to use telepsychotherapy technology in the future. This study complements the research on therapists' attitudes towards telepsychotherapy, based on the therapist version of the UTAUT. The developed 14-item UTAUT-P might be a helpful, brief self-report tool in clinical practice, which might give the patient a voice around the potential use of telepsychotherapy technology in their care. This initial application of the UTAUT-P patients during the COVID-19 pandemic offers a building block for future research on patients' attitudes towards telepsychotherapy, outside the context of a forced transition.

Exploratory factor analysis with structured residuals for brain network data

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

Maturation of Brain Microstructure and Metabolism Associates with Increased Capacity for Self-Regulation during the Transition from Childhood to Adolescence

Children ages 9-12 years face increasing social and academic expectations that require mastery of their thoughts, emotions, and behavior. Little is known about the development of neural pathways integral to these improving capacities during the transition from childhood to adolescence. Among 234 healthy, inner-city male and female youth (species Homo sapiens) 9-12 years of age followed by the Columbia Center for Children's Environmental Health, we acquired diffusion tensor imaging, multiplanar chemical shift imaging, and cognitive measures requiring self-regulation. We found that increasing age was associated with increased fractional anisotropy and decreased apparent diffusion coefficient, most prominently in the frontal and cingulate cortices, striatum, thalamus, deep white matter, and cerebellum. Additionally, we found increasing age was associated with increased N-acetyl-l-aspartate (NAA) in the anterior cingulate and insular cortices, and decreased NAA in posterior cingulate and parietal cortices. Age-associated changes in microstructure and neurometabolite concentrations partially mediated age-related improvements in performance on executive function tests. Together, these findings suggest that maturation of key regions within cortico-striatal-thalamo-cortical circuits subserve the emergence of improved self-regulatory capacities during the transition from childhood to adolescence.SIGNIFICANCE STATEMENT Few imaging studies of normal brain development have focused on a population of inner-city, racial/ethnic minority youth during the transition from childhood to adolescence, a period when self-regulatory capacities rapidly improve. We used DTI and MPCSI to provide unique windows into brain maturation during this developmental epoch, assessing its mediating influences on age-related improvement in performance on self-regulatory tasks. Our findings suggest that rapid maturation of cortico-striato-thalamo-cortical circuits, represented as progressive white-matter maturation (increasing FA and increasing NAA, Ch, Cr concentrations accompanying advancing age) in frontal regions and related subcortical projections and synaptic pruning (decreasing NAA, Ch, Cr, Glx) in posterior regions, support age-related improvements in executive functioning and self-regulatory capacities in youth 9-12 years of age.

Impact of sex and reproductive status on memory circuitry structure and function in early midlife using structural covariance analysis

Research on age-related memory alterations traditionally targets individuals aged ≥65 years. However, recent studies emphasize the importance of early aging processes. We therefore aimed to characterize variation in brain gray matter structure in early midlife as a function of sex and menopausal status. Subjects included 94 women (33 premenopausal, 29 perimenopausal, and 32 postmenopausal) and 99 demographically comparable men from the New England Family Study. Subjects were scanned with a high-resolution T1 sequence on a 3 T whole body scanner. Sex and reproductive-dependent structural differences were evaluated using Box's M test and analysis of covariances (ANCOVAs) for gray matter volumes. Brain regions of interest included dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (iPAR), anterior cingulate cortex (ACC), hippocampus (HIPP), and parahippocampus. While we observed expected significant sex differences in volume of hippocampus with women of all groups having higher volumes than men relative to cerebrum size, we also found significant differences in the covariance matrices of perimenopausal women compared with postmenopausal women. Associations between ACC and HIPP/iPAR/DLPFC were higher in postmenopausal women and correlated with better memory performance. Findings in this study underscore the importance of sex and reproductive status in early midlife for understanding memory function with aging.

Neurobehavioral Mechanisms of Resilience Against Emotional Distress: An Integrative Brain-Personality-Symptom Approach Using Structural Equation Modeling

Clarifying individual differences that predict resilience or vulnerability to emotional distress is crucial for identifying etiological factors contributing to affective disturbances, and to promoting emotional well-being. Despite recent progress identifying specific brain regions and personality traits, it remains unclear whether there are common factors underlying the structural aspects of the brain and the personality traits that, in turn, protect against symptoms of emotional distress. In the present study, an integrative structural equation model was developed to examine the associations among (1) a latent construct of Control, representing the volumes of a system of prefrontal cortical (PFC) regions including middle, inferior, and orbital frontal cortices; (2) a latent construct of Resilience personality traits including cognitive reappraisal, positive affectivity, and optimism; and (3) Anxiety and Depression symptoms, in a sample of 85 healthy young adults. Results showed that the latent construct of PFC volumes positively predicted the latent construct of Resilience, which in turn negatively predicted Anxiety. Mediation analysis confirmed that greater latent PFC volume is indirectly associated with lower Anxiety symptoms through greater latent trait Resilience. The model did not show a significant mediation for Depression. These results support the idea that there are common volumetric and personality factors that help protect against symptoms of emotional distress. These findings provide strong evidence that such brain-personality-symptom approaches can provide novel insights with valuable implications for understanding the interaction of these factors in healthy and clinically diagnosed individuals.

Age sensitive associations of adolescent substance use with amygdalar, ventral striatum, and frontal volumes in young adulthood

INTRODUCTION

This study evaluated an age sensitive model of substance use across adolescence to determine if substance use was associated with smaller volumes for an earlier developing brain region, the amygdala, a later developing region, the inferior frontal gyrus, and the ventral striatum.

METHOD

Participants (N = 110) were African American young adults who were members of a longitudinal cohort across childhood and adolescence. Measures of substance use were collected across early (ages 12-15 yrs.), middle (ages 16-18 yrs.), and later (ages 19-21 yrs.) adolescence; then, at age 25, a representative subset of the sample completed magnetic resonance imaging (MRI) that assessed regional brain volumes.

RESULTS

Higher levels of substance use during early adolescence, but not middle or later adolescence, were significantly associated with smaller amygdalar volume in young adulthood. Higher levels of substance use during middle adolescence, but not early or later adolescence, were significantly associated with smaller pars opercularis volume. Substance use was not associated with the pars triangularis or ventral striatum.

CONCLUSION

These findings support age sensitive associations between substance use and smaller gray matter volumes at age 25 and are consistent with literature supporting the differential nature of substance use and brain maturation across adolescence and into young adulthood.

How neuroscience can inform the study of individual differences in cognitive abilities

Theories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems and whether most of the variability between the operations of different brains can be ascribed to a single factor is questionable. Research in neuroscience suggests that psychological processes such as perception, attention, decision, and executive control are emergent properties of interacting distributed networks. The modules that make up these networks use similar computational processes that involve multiple forms of neural plasticity, each having different time constants. Accordingly, these networks might best be characterized in terms of the information they process rather than in terms of abstract psychological processes such as working memory and executive control.

A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory

Increasing age is tightly linked to decreased thickness of the human neocortex. The biological mechanisms that mediate this effect are hitherto unknown. The DNA methylome, as part of the epigenome, contributes significantly to age-related phenotypic changes. Here, we identify an epigenetic signature that is associated with cortical thickness (P=3.86 × 10⁻⁸) and memory performance in 533 healthy young adults. The epigenetic effect on cortical thickness was replicated in a sample comprising 596 participants with major depressive disorder and healthy controls. The epigenetic signature mediates partially the effect of age on cortical thickness (P<0.001). A multilocus genetic score reflecting genetic variability of this signature is associated with memory performance (P=0.0003) in 3,346 young and elderly healthy adults. The genomic location of the contributing methylation sites points to the involvement of specific immune system genes. The decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related traits.

Cortical thickness has high heritability estimates and is known to be influenced by genetic factors. Here, Freytag and colleagues show that DNA methylation patterns of peripheral blood monocytes are also correlated with cortical thickness and memory performance in human.

Being in a Romantic Relationship Is Associated with Reduced Gray Matter Density in Striatum and Increased Subjective Happiness

Romantic relationship, a widespread feature of human society, is one of the most influential factors in daily life. Although stimuli related to romantic love or being in a romantic relationship commonly result in enhancement of activation or functional connectivity of the reward system, including the striatum, the structure underlying romantic relationship-related regions remain unclear. Because individual experiences can alter gray matter within the adult human brain, we hypothesized that romantic relationship is associated with structural differences in the striatum related to the positive subjective experience of being in a romantic relationship. Because intimate romantic relationships contribute to perceived subjective happiness, this subjective enhancement of happiness might be accompanied by the experience of positive events related to being in a romantic relationship. To test this hypothesis and elucidate the structure involved, we compared subjective happiness, an indirect measure of the existence of positive experiences caused by being in a romantic relationship, of participants with or without romantic partners (N = 68). Furthermore, we also conducted a voxel-based morphometry study of the effects of being in a romantic relationship (N = 113). Being in a romantic relationship was associated with greater subjective happiness and reduced gray matter density within the right dorsal striatum. These results suggest that being in a romantic relationship enhances perceived subjective happiness via positive experiences. Furthermore, the observed reduction in gray matter density in the right dorsal striatum may reflect an increase in saliency of social reward within a romantic relationship. Thus, being in a romantic relationship is associated with positive experiences and a reduction of gray matter density in the right dorsal striatum, representing a modulation of social reward.

Brain circuitry of compulsivity

Compulsivity is associated with alterations in the structure and the function of parallel and interacting brain circuits involved in emotional processing (involving both the reward and the fear circuits), cognitive control, and motor functioning. These brain circuits develop during the pre-natal period and early childhood under strong genetic and environmental influences. In this review we bring together literature on cognitive, emotional, and behavioral processes in compulsivity, based mainly on studies in patients with obsessive-compulsive disorder and addiction. Disease symptoms normally change over time. Goal-directed behaviors, in response to reward or anxiety, often become more habitual over time. During the course of compulsive disorders the mental processes and repetitive behaviors themselves contribute to the neuroplastic changes in the involved circuits, mainly in case of chronicity. On the other hand, successful treatment is able to normalize altered circuit functioning or to induce compensatory mechanisms. We conclude that insight in the neurobiological characteristics of the individual symptom profile and disease course, including the potential targets for neuroplasticity is an unmet need to advance the field.

Brain structure-function associations identified in large-scale neuroimaging data

The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure–function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.

Correlations between brain structure and symptom dimensions of psychosis in schizophrenia, schizoaffective, and psychotic bipolar I disorders

INTRODUCTION

Structural alterations may correlate with symptom severity in psychotic disorders, but the existing literature on this issue is heterogeneous. In addition, it is not known how cortical thickness and cortical surface area correlate with symptom dimensions of psychosis.

METHODS

Subjects included 455 individuals with schizophrenia, schizoaffective, or bipolar I disorders. Data were obtained as part of the Bipolar Schizophrenia Network for Intermediate Phenotypes study. Diagnosis was made through the Structured Clinical Interview for DSM-IV. Positive and negative symptom subscales were assessed using the Positive and Negative Syndrome Scale. Structural brain measurements were extracted from T1-weight structural MRIs using FreeSurfer v5.1 and were correlated with symptom subscales using partial correlations. Exploratory factor analysis was also used to identify factors among those regions correlating with symptom subscales.

RESULTS

The positive symptom subscale correlated inversely with gray matter volume (GMV) and cortical thickness in frontal and temporal regions, whereas the negative symptom subscale correlated inversely with right frontal cortical surface area. Among regions correlating with the positive subscale, factor analysis identified four factors, including a temporal cortical thickness factor and frontal GMV factor. Among regions correlating with the negative subscale, factor analysis identified a frontal GMV-cortical surface area factor. There was no significant diagnosis by structure interactions with symptom severity.

CONCLUSIONS

Structural measures correlate with positive and negative symptom severity in psychotic disorders. Cortical thickness demonstrated more associations with psychopathology than cortical surface area.

Genetic architecture of subcortical brain regions: common and region-specific genetic contributions

Understanding the aetiology of patterns of variation within and covariation across brain regions is key to advancing our understanding of the functional, anatomical and developmental networks of the brain. Here we applied multivariate twin modelling and principal component analysis (PCA) to investigate the genetic architecture of the size of seven subcortical regions (caudate nucleus, thalamus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens) in a genetically informative sample of adolescents and young adults (N = 1038; mean age = 21.6 ± 3.2 years; including 148 monozygotic and 202 dizygotic twin pairs) from the Queensland Twin IMaging (QTIM) study. Our multivariate twin modelling identified a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In addition, we also found substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52). This provides further insight into the extent and organization of subcortical genetic architecture, which includes developmental and general growth pathways, as well as the functional specialization and maturation trajectories that influence each subcortical region.

Disrupted thalamo-cortical connectivity in schizophrenia: a morphometric correlation analysis

Increasing studies have implicated the thalamus in schizophrenia, supporting the view that this structure has an important role in this disorder. Given that extensive reciprocal connections exist between the thalamus and the cerebral cortex, it is believed that disruptions of the thalamo-cortical connections may underlie the multiplicity of schizophrenic symptoms. Therefore, assessing the relationship between the thalamus and the neocortex may provide new insights into the pathophysiology of schizophrenia. We analyzed magnetic resonance images from a sample of 101 schizophrenic patients and 101 healthy controls. By assessing the correlation between the thalamic volume and cortical thickness at each vertex on the cortical surface, a thalamo-cortical network was obtained for each group. We compared the patterns of thalamo-cortical connectivity between the two groups. Compared with healthy controls, less distributed cortical regions were identified in the thalamo-cortical network in patients with schizophrenia. Vertex-wise comparison revealed decreased thalamo-cortical connectivity in bilateral inferior frontal gyrus, the left superior temporal gyrus and the right parieto-occipital region in schizophrenia. The observed disruptions in thalamo-cortical connectivity might be the substrate underlying the wide range of schizophrenic symptoms and provide further evidence to support the notion of schizophrenia as a disorder of brain dysconnectivity.

Imaging structural co-variance between human brain regions

Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

Models of the aging brain structure and individual decline

The aging brain's structural development constitutes a spatiotemporal process that is accessible by MR-based computational morphometry. Here we introduce basic concepts and analytical approaches to quantify age-related differences and changes in neuroanatomical images of the human brain. The presented models first address the estimation of age trajectories, then we consider inter-individual variations of structural decline, using a repeated measures design. We concentrate our overview on preprocessed neuroanatomical images of the human brain to facilitate practical applications to diverse voxel- and surface-based structural markers. Together these methods afford analysis of aging brain structure in relation to behavioral, health, or cognitive parameters.

Genetic patterns of correlation among subcortical volumes in humans: results from a magnetic resonance imaging twin study

Little is known about genetic influences on the volume of subcortical brain structures in adult humans, particularly whether there is regional specificity of genetic effects. Understanding patterns of genetic covariation among volumes of subcortical structures may provide insight into the development of individual differences that have consequences for cognitive and emotional behavior and neuropsychiatric disease liability. We measured the volume of 19 subcortical structures (including brain and ventricular regions) in 404 twins (110 monozygotic and 92 dizygotic pairs) from the Vietnam Era Twin Study of Aging and calculated the degree of genetic correlation among these volumes. We then examined the patterns of genetic correlation through hierarchical cluster analysis and by principal components analysis. We found that a model with four genetic factors best fit the data: a Basal Ganglia/Thalamus factor; a Ventricular factor; a Limbic factor; and a Nucleus Accumbens factor. Homologous regions from each hemisphere loaded on the same factors. The observed patterns of genetic correlation suggest the influence of multiple genetic influences. There is a genetic organization among structures which distinguishes between brain and cerebrospinal fluid spaces and between different subcortical regions. Further study is needed to understand this genetic patterning and whether it reflects influences on early development, functionally dependent patterns of growth or pruning, or regionally specific losses due to genes involved in aging, stress response, or disease.

Exploratory factor analysis for small samples

Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.

Differential development of human brain white matter tracts

Neuroscience is increasingly focusing on developmental factors related to human structural and functional connectivity. Unfortunately, to date, diffusion-based imaging approaches have only contributed modestly to these broad objectives, despite the promise of diffusion-based tractography. Here, we report a novel data-driven approach to detect similarities and differences among white matter tracts with respect to their developmental trajectories, using 64-direction diffusion tensor imaging. Specifically, using a cross-sectional sample comprising 144 healthy individuals (7 to 48 years old), we applied k-means cluster analysis to separate white matter voxels based on their age-related trajectories of fractional anisotropy. Optimal solutions included 5-, 9- and 14-clusters. Our results recapitulate well-established tracts (e.g., internal and external capsule, optic radiations, corpus callosum, cingulum bundle, cerebral peduncles) and subdivisions within tracts (e.g., corpus callosum, internal capsule). For all but one tract identified, age-related trajectories were curvilinear (i.e., inverted 'U-shape'), with age-related increases during childhood and adolescence followed by decreases in middle adulthood. Identification of peaks in the trajectories suggests that age-related losses in fractional anisotropy occur as early as 23 years of age, with mean onset at 30 years of age. Our findings demonstrate that data-driven analytic techniques may be fruitfully applied to extant diffusion tensor imaging datasets in normative and neuropsychiatric samples.

Similarity-based extraction of individual networks from gray matter MRI scans

The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were "small world." The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.

Thalamic-insular dysconnectivity in schizophrenia: evidence from structural equation modeling

Structural and functional studies have shown that schizophrenia is often associated with frontolimbic abnormalities in the prefrontal and mediotemporal regions. It is still unclear, however, if such dysfunctional interaction extends as well to relay regions such as the thalamus and the anterior insula. Here, we measured gray matter volumes of five right-hemisphere regions in 68 patients with schizophrenia and 77 matched healthy subjects. The regions were amygdala, thalamus, and entorhinal cortex (identified as anomalous by prior studies on the same population) and dorsolateral prefrontal cortex and anterior insula (isolated by voxel-based morphometry analysis). We used structural equation modeling and found altered path coefficients connecting the thalamus to the anterior insula, the amygdala to the DLPFC, and the entorhinal cortex to the DLPFC. In particular, patients exhibited a stronger thalamus-insular connection than healthy controls. Instead, controls showed positive entorhinal-DLPFC and negative amygdalar-DLPFC connections, both of which were absent in the clinical population. Our data provide evidence that schizophrenia is characterized by an impaired right-hemisphere network, in which intrahemispheric communication involving relay structures may play a major role in sustaining the pathophysiology of the disease.

Covariance modeling of MRI brain volumes in memory circuitry in schizophrenia: Sex differences are critical

Women have consistently demonstrated better verbal memory on tests that evaluate immediate and delayed free recall. In patients with schizophrenia, these verbal memory processes are relatively more preserved in women than men. However an understanding of the brain anatomy of the female advantage for verbal memory is still unclear. 29 females and 59 males with schizophrenia made comparable to 21 female and 27 male healthy volunteers were scanned using structural magnetic resonance imaging (sMRI) in order to assess volumes of regions across the entire brain. Sex differences within and between groups in the covariance structure of memory circuitry regions were evaluated using a novel approach to covariance analysis (the Box M Test). Brain areas of interest included the prefrontal cortex (PFC), inferior parietal lobule (iPAR), anterior cingulate gyrus (ACG), parahippocampus, and hippocampus (HIPP). Results showed significant differences in the covariance matrices of females and males with schizophrenia compared with their healthy counterparts, in particular the relationships between iPAR-PFC, iPAR-ACG, and HIPP-PFC. Sex differences in the iPAR-PFC relationship were significantly associated with sex differences in verbal memory performance. In control women, but not in men ACG volume correlated strongly with memory performance. In schizophrenia, ACG volume was reduced in females, but not in men, relative to controls. Findings suggest that the relationship between iPAR and PFC is particularly important for understanding the relative preservation of verbal memory processing in females with schizophrenia and may compensate for ACG volume reductions. These results illustrate the utility of a unique covariance structure modeling approach that yields important new knowledge for understanding the nature of schizophrenia.

Structural equation modeling and principal component analysis of gray matter volumes in major depressive and bipolar disorders: differences in latent volumetric structure

Abnormalities of the cortico-striatal-thalamic-cortical (CSTC) and the limbic-cortico-striatal-thalamic-cortical (LCSTC) circuits have been hypothesized in mood disorders. We performed principal component analysis (PCA) to identify latent volumetric systems on regional brain volumes and correlated these patterns with clinical characteristics; further, we performed exploratory structural equation modeling (SEM) to test a priori hypotheses about the organization among the structures comprising the CSTC and LCSTC circuits, and to investigate differences among subjects with bipolar disorder (BD), major depressive disorder (MDD), and healthy controls (HC). Participants included 45 BD and 31 MDD patients, and 72 HC. Regional MR brain volumes were used to calculate patterns of volumetric covariance. The identified latent volumetric systems were related to the depression severity and the duration of illness. BD differed from HC on the estimated parameters describing the paths of cortico-striatal, thalamo-striatal and intrastriatal loops of the CSTC circuit, and the paths between anterior and posterior cingulate cortex (PCC), and hippocampus to amygdala of the LCSTC circuit. MDD differed from HC on the paths between putamen and thalamus, and PCC to hippocampus. This study provides evidence to suggest different organizational patterns among structures within the CSTC and LCSTC circuits for BD, MDD, and HC, which may point to structural abnormalities underlying mood disorders.

Diffusion tensor imaging of hemispheric asymmetries in the developing brain

Diffusion tensor imaging (DTI) was performed in 39 right-handed children to examine structural hemispheric differences and the impact of age, socioeconomic status, and sex on these differences. Apparent diffusion coefficient (ADC) values were smaller in the left than in the right temporal, prefrontal, anterior internal capsular and the thalamic regions, and fractional anisotropy (FA) values were larger in the left than in the right internal capsule, thalamus, and cingulate. Significant region-by-sex interactions disclosed that the relation of DTI asymmetries to performance depended on sex including the relation of temporal lobes to reading comprehension and the relation of frontal lobes to solving applied mathematical problems.