GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures.

BACKGROUND: We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. NEW METHOD: GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. RESULTS: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. COMPARISON WITH EXISTING METHODS: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. CONCLUSIONS: GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

[Big data approaches in psychiatry: examples in depression research]. / Big-Data-Ansätze in der Psychiatrie: Beispiele aus der Depressionsforschung.

BACKGROUND: The exploration and therapy of depression is aggravated by heterogeneous etiological mechanisms and various comorbidities. With the growing trend towards big data in psychiatry, research and therapy can increasingly target the individual patient. This novel objective requires special methods of analysis. OBJECTIVE: The possibilities and challenges of the application of big data approaches in depression are examined in closer detail. MATERIAL AND METHODS: Examples are given to illustrate the possibilities of big data approaches in depression research. Modern machine learning methods are compared to traditional statistical methods in terms of their potential in applications to depression. RESULTS: Big data approaches are particularly suited to the analysis of detailed observational data, the prediction of single data points or several clinical variables and the identification of endophenotypes. A current challenge lies in the transfer of results into the clinical treatment of patients with depression. CONCLUSION: Big data approaches enable biological subtypes in depression to be identified and predictions in individual patients to be made. They have enormous potential for prevention, early diagnosis, treatment choice and prognosis of depression as well as for treatment development.

Co-activation based parcellation of the human frontal pole.

Historically, the human frontal pole (FP) has been considered as a single architectonic area. Brodmann's area 10 is located in the frontal lobe with known contributions in the execution of various higher order cognitive processes. However, recent cytoarchitectural studies of the FP in humans have shown that this portion of cortex contains two distinct cytoarchitectonic regions. Since architectonic differences are accompanied by differential connectivity and functions, the frontal pole qualifies as a candidate region for exploratory parcellation into functionally discrete sub-regions. We investigated whether this functional heterogeneity is reflected in distinct segregations within cytoarchitectonically defined FP-areas using meta-analytic co-activation based parcellation (CBP). The CBP method examined the co-activation patterns of all voxels within the FP as reported in functional neuroimaging studies archived in the BrainMap database. Voxels within the FP were subsequently clustered into sub-regions based on the similarity of their respective meta-analytically derived co-activation maps. Performing this CBP analysis on the FP via k-means clustering produced a distinct 3-cluster parcellation for each hemisphere corresponding to previously identified cytoarchitectural differences. Post-hoc functional characterization of clusters via BrainMap metadata revealed that lateral regions of the FP mapped to memory and emotion domains, while the dorso- and ventromedial clusters were associated broadly with emotion and social cognition processes. Furthermore, the dorsomedial regions contain an emphasis on theory of mind and affective related paradigms whereas ventromedial regions couple with reward tasks. Results from this study support previous segregations of the FP and provide meta-analytic contributions to the ongoing discussion of elucidating functional architecture within human FP.

The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis.

The right temporoparietal junction (rTPJ) is frequently associated with different capacities that to shift attention to unexpected stimuli (reorienting of attention) and to understand others' (false) mental state [theory of mind (ToM), typically represented by false belief tasks]. Competing hypotheses either suggest the rTPJ representing a unitary region involved in separate cognitive functions or consisting of subregions subserving distinct processes. We conducted activation likelihood estimation (ALE) meta-analyses to test these hypotheses. A conjunction analysis across ALE meta-analyses delineating regions consistently recruited by reorienting of attention and false belief studies revealed the anterior rTPJ, suggesting an overarching role of this specific region. Moreover, the anatomical difference analysis unravelled the posterior rTPJ as higher converging in false belief compared with reorienting of attention tasks. This supports the concept of an exclusive role of the posterior rTPJ in the social domain. These results were complemented by meta-analytic connectivity mapping (MACM) and resting-state functional connectivity (RSFC) analysis to investigate whole-brain connectivity patterns in task-constrained and task-free brain states. This allowed for detailing the functional separation of the anterior and posterior rTPJ. The combination of MACM and RSFC mapping showed that the posterior rTPJ has connectivity patterns with typical ToM regions, whereas the anterior part of rTPJ co-activates with the attentional network. Taken together, our data suggest that rTPJ contains two functionally fractionated subregions: while posterior rTPJ seems exclusively involved in the social domain, anterior rTPJ is involved in both, attention and ToM, conceivably indicating an attentional shifting role of this region.

ALE meta-analysis on facial judgments of trustworthiness and attractiveness.

Faces convey a multitude of information in social interaction, among which are trustworthiness and attractiveness. Humans process and evaluate these two dimensions very quickly due to their great adaptive importance. Trustworthiness evaluation is crucial for modulating behavior toward strangers; attractiveness evaluation is a crucial factor for mate selection, possibly providing cues for reproductive success. As both dimensions rapidly guide social behavior, this study tests the hypothesis that both judgments may be subserved by overlapping brain networks. To this end, we conducted an activation likelihood estimation meta-analysis on 16 functional magnetic resonance imaging studies pertaining to facial judgments of trustworthiness and attractiveness. Throughout combined, individual, and conjunction analyses on those two facial judgments, we observed consistent maxima in the amygdala which corroborates our initial hypothesis. This finding supports the contemporary paradigm shift extending the amygdala's role from dominantly processing negative emotional stimuli to processing socially relevant ones. We speculate that the amygdala filters sensory information with evolutionarily conserved relevance. Our data suggest that such a role includes not only "fight-or-flight" decisions but also social behaviors with longer term pay-off schedules, e.g., trustworthiness and attractiveness evaluation.