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Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task‐functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task‐fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data‐driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion‐MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree‐based classifiers produced significant above‐chance predictions both during cross‐validation (NN: AUC–ROC[CI] = 0.68[0.64–0.73], accuracy[CI] = 68.34%[63–73.2%]; RF: AUC–ROC[CI] = 0.7[0.66–0.73], accuracy[CI] = 64.81%[60.9–68.5]) and testing (NN: AUC–ROC[CI] = 0.69[0.53–0.84], accuracy[CI] = 68.09[53.2–80.9]; RF: AUC–ROC[CI] = 0.68[0.53–0.84], accuracy[CI] = 68.09[55.3–80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above‐chance predictions of functional language lateralization in the ATL are possible based on diffusion‐MRI connectomes alone. Increased degree within the right pMTG as a right‐sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward‐lateralized semantic processing. [ABSTRACT FROM AUTHOR]

In addition to its traditional role in motor control, the cerebellum has been implicated in various cognitive and linguistic functions. Lesion, anatomic, and functional imaging studies indicate a link between left frontal language regions and the right cerebellum. To probe the specificity of this circuit, we examined the association between language-related lateralized activation of the frontal cortex with lateralized activation of the cerebellum. Functional magnetic resonance imaging (fMRI) was carried out during letter-cued word generation in 14 healthy subjects: 7 subjects displayed typical left-hemisphere and 7 subjects displayed atypical right-hemisphere language dominance. We found activation of the cerebellar hemisphere contralateral to the language-dominant cerebral hemisphere in each subject. The cerebellar activation was confined to the lateral posterior cerebellar hemisphere (lobule VI, VII B, Cr I, Cr II). This study demonstrates that crossed cerebral and cerebellar language dominance is a typical characteristic of brain organization. The functional significance of the reported activations can now be tested in patients with lesions of the lateral posterior cerebellum. Hum. Brain Mapp 24:165-172, 2005. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]