Establishing online mentorship for early career researchers: Lessons from the Organization for Human Brain Mapping International Mentoring Programme.

Mentorship facilitates personal growth through pairing trainees with mentors who can share their expertise. In times of global integration, geographical proximity between mentors and mentees is relevant to a lesser degree. This has led to popularization of online mentoring programs. In this editorial, we introduce the history and architecture of the International Online Mentoring Programme organized by the Student and Postdoc Special Interest Group of the Organization for Human Brain Mapping.

Thresholding functional connectomes by means of mixture modeling.

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

Writing an effective and supportive recommendation letter.

Writing recommendation letters on behalf of students and other early-career researchers is an important mentoring task within academia. An effective recommendation letter describes key candidate qualities such as academic achievements, extracurricular activities, outstanding personality traits, participation in and dedication to a particular discipline, and the mentor's confidence in the candidate's abilities. In this Words of Advice, we provide guidance to researchers on composing constructive and supportive recommendation letters, including tips for structuring and providing specific and effective examples, while maintaining a balance in language and avoiding potential biases.

Building and sustaining mentor interactions as a mentee.

Mentorship is experience and/or knowledge-based guidance. Mentors support, sponsor and advocate for mentees. Having one or more mentors when you seek advice can significantly influence and improve your research endeavours, well-being and career development. Positive mentee-mentor relationships are vital for maintaining work-life balance and success in careers. Early-career researchers (ECRs), in particular, can benefit from mentorship to navigate challenges in academic and nonacademic life and careers. Yet, strategies for selecting mentors and maintaining interactions with them are often underdiscussed within research environments. In this Words of Advice, we provide recommendations for ECRs to seek and manage mentorship interactions. Our article draws from our experiences as ECRs and published work, to provide suggestions for mentees to proactively promote beneficial mentorship interactions. The recommended practices highlight the importance of identifying mentorship needs, planning and selecting multiple and diverse mentors, setting goals, and maintaining constructive, and mutually beneficial working relationships with mentors.

Cortical control of aggression: GABA signalling in the anterior cingulate cortex.

Reduced top-down control by cortical areas is assumed to underlie pathological forms of aggression. While the precise underlying molecular mechanisms are still elusive, it seems that balancing the excitatory and inhibitory tones of cortical brain areas has a role in aggression control. The molecular mechanisms underpinning aggression control were examined in the BALB/cJ mouse model. First, these mice were extensively phenotyped for aggression and anxiety in comparison to BALB/cByJ controls. Microarray data was then used to construct a molecular landscape, based on the mRNAs that were differentially expressed in the brains of BALB/cJ mice. Subsequently, we provided corroborating evidence for the key findings from the landscape through 1H-magnetic resonance imaging and quantitative polymerase chain reactions, specifically in the anterior cingulate cortex (ACC). The molecular landscape predicted that altered GABA signalling may underlie the observed increased aggression and anxiety in BALB/cJ mice. This was supported by a 40% reduction of 1H-MRS GABA levels and a 20-fold increase of the GABA-degrading enzyme Abat in the ventral ACC. As a possible compensation, Kcc2, a potassium-chloride channel involved in GABA-A receptor signalling, was found increased. Moreover, we observed aggressive behaviour that could be linked to altered expression of neuroligin-2, a membrane-bound cell adhesion protein that mediates synaptogenesis of mainly inhibitory synapses. In conclusion, Abat and Kcc2 seem to be involved in modulating aggressive and anxious behaviours observed in BALB/cJ mice through affecting GABA signalling in the ACC.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI.

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

Effective Self-Management for Early Career Researchers in the Natural and Life Sciences.

Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management.

Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions.

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.

Time-delay model of perceptual decision making in cortical networks.

It is known that cortical networks operate on the edge of instability, in which oscillations can appear. However, the influence of this dynamic regime on performance in decision making, is not well understood. In this work, we propose a population model of decision making based on a winner-take-all mechanism. Using this model, we demonstrate that local slow inhibition within the competing neuronal populations can lead to Hopf bifurcation. At the edge of instability, the system exhibits ambiguity in the decision making, which can account for the perceptual switches observed in human experiments. We further validate this model with fMRI datasets from an experiment on semantic priming in perception of ambivalent (male versus female) faces. We demonstrate that the model can correctly predict the drop in the variance of the BOLD within the Superior Parietal Area and Inferior Parietal Area while watching ambiguous visual stimuli.

Quantifying free behaviour in an open field using k-motif approach.

Quantification and parametrisation of movement are widely used in animal behavioural paradigms. In particular, free movement in controlled conditions (e.g., open field paradigm) is used as a "proxy for indices of baseline and drug-induced behavioural changes. However, the analysis of this is often time- and labour-intensive and existing algorithms do not always classify the behaviour correctly. Here, we propose a new approach to quantify behaviour in an unconstrained environment: searching for frequent patterns (k-motifs) in the time series representing the position of the subject over time. Validation of this method was performed using subchronic quinpirole-induced changes in open field experiment behaviours in rodents. Analysis of this data was performed using k-motifs as features to better classify subjects into experimental groups on the basis of behaviour in the open field. Our classifier using k-motifs gives as high as 94% accuracy in classifying repetitive behaviour versus controls which is a substantial improvement compared to currently available methods including using standard feature definitions (depending on the choice of feature set and classification strategy, accuracy up to 88%). Furthermore, visualisation of the movement/time patterns is highly predictive of these behaviours. By using machine learning, this can be applied to behavioural analysis across experimental paradigms.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches.

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI.

PURPOSE: Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI. MATERIALS AND METHODS: In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies. RESULTS: First, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state-of-the-art datasets does not influence the performance of the lagged methods. CONCLUSIONS: Factors such as background scale-free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder.

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of "constructs" as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD.