Cognitive-related and Connectome-based Biomarkers for Depression: The Application of State-of-the-Art Techniques and Models to Uncover

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Background

Depression (Major Depressive Disorder, MDD) is defined as a combination of symptoms such as sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness, and poor concentration. It has long been acknowledged that depression can impact an individual’s social functioning (e.g., at work or school) as well as their daily life. Epidemiological prevalence studies suggest that depression affected 4.7% of the global population in 2015. Furthermore, depression contributes to almost 800, 000 deaths by suicide per year and is ranked by the World Health Organization (WHO) as the top factor for global disability at a rate of 6.1 %. Depression also causes heavy burdens of disease worldwide, particularly in middle- and low-income countries. To reveal why people may suffer from this condition, many efforts have been made to examine the neurobiological markers of depression, with results so far finding more depressive symptoms for hyperactivity in default mode network, fronto-limbic, and corco-striatal circuits. Despite the proliferation of investigations into neural substrates, connectome-based neural patterns for depression have not yet been elucidated fully.

Connectome theory – a key theory in network neuroscience – has advanced our understanding of neural mechanisms from isolated brain localizers to intrinsic large-scale networks. Brain connectome refers to the information system among brain regions, which represents the communication efficacy (i.e., brain connectivity) in the brain and is also associated with mental health problems, particularly depression. However, one flaw worth noting is that existing studies have been grounded largely in brain–behavior configuration or in plain functional connectivity. Although the preliminary evidence has probed into neural markers for diagnosis, prognosis, or treatment of depression, lacking connectome-based biomarkers still poses barriers to clinical practices, especially in precision medicine. In addition, state-of-the-art models and techniques for examining connectome-based biomarkers could advance our understanding of depression to map out the neural landscape for this disease; for example, through a hidden Markov model (HMM) and gradient estimate model.

The aim of this Research Topic, therefore, is to reveal novel connectome-based biomarkers for depression through the application of state-of-the-art models or techniques, to offer better translational potentials for predicting the diagnosis, prognosis, or treatment of this condition. We welcome high-quality original research, reviews, and meta-analyses, in addition to commentaries or perspectives concerning, but are not confined to, the following subtopics:
- neural (multi-scale) connectome models (e.g., fMRI, fNIRS, ERP, EEG) to reveal biomarkers for depression.
- cognitive-related biomarkers of depression, such as negative cognition bias trait, anxiety trait, and working memory.
- novel neurobiological markers based on the application of advanced or state-of-the-art models and techniques.
- neural changes evoked by neuromodulation (e.g., TMS, tDCS, tACS), meditation and psychological therapy in brain connectome and corresponding cognitive mechanisms.
- high-quality reproducible studies for validating biomarkers of depression.

Keywords: Depression, Neural connectome, Brain network, Cognitive processing biases, Machine learning, Major Depressive Disorder

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