Recent prevalence rates indicate that, at any one time, 2.7% of adolescents are experiencing depression, with up to 20% of adolescents experiencing at least one depressive episode before entering adulthood. The diagnosis of depression in adolescents relies on identifying the presence of specific core and additional symptoms. However, symptom expression may vary with developmental stage, and some children and adolescents may have difficulty identifying and describing internal mood states. Children often have difficulty in expressing or recalling information regarding their disorder; therefore, multiple informants (usually parents) must often be used to obtain the information. In addition, comorbid diagnoses are common in early onset depression, making diagnosis more difficult. It is still unclear to what extent the neurophysiological and neuro-functional underpinnings of depression differ significantly in children and adults. This contributes more challenges to the development of appropriate diagnostic tools and treatments.
The goal of this Research Topic is to review assessment research of depressive disorder in children and adolescents with a focus on the diagnostic issue more specifically using the recent advances in neuroimaging (for example machine learning methodologies). There are changing trends in neuroimaging studies but despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop “bridges” to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. We may integrate more recent advances (for example, multimodal imaging, AI, and genetics) for the diagnosis in pediatric mood disorders.
• The benefits and costs of involving clinical judgment in the diagnostic process;
• The validity of parent, teacher, and youth self-report of mood symptoms;
• How much cross-situational consistency is typically shown in mood and behaviour in different cultural spectrum;
• How different measures compare in terms of detecting mood disorder;
• The challenges in comparing the performance of measures across research groups;
• The understanding of the neural underpinnings and neuro-functional deficits involved in depression in developmental population
• The implications of newer biological and technological advances in the assessment of pediatric mood disorders (for example, machine learning etc.);
• Research to increase the understanding of phenomenology of mood disorder from a developmental framework;
• Neuroimaging methodology and its use for diagnosis including functional MRI and connectivity studies and machine learning;
• Cytokines and other neuroinflammatory biomarkers of depressive disorders;
• Comorbidity and differential diagnosis (from bipolar disorder etc.).
Recent prevalence rates indicate that, at any one time, 2.7% of adolescents are experiencing depression, with up to 20% of adolescents experiencing at least one depressive episode before entering adulthood. The diagnosis of depression in adolescents relies on identifying the presence of specific core and additional symptoms. However, symptom expression may vary with developmental stage, and some children and adolescents may have difficulty identifying and describing internal mood states. Children often have difficulty in expressing or recalling information regarding their disorder; therefore, multiple informants (usually parents) must often be used to obtain the information. In addition, comorbid diagnoses are common in early onset depression, making diagnosis more difficult. It is still unclear to what extent the neurophysiological and neuro-functional underpinnings of depression differ significantly in children and adults. This contributes more challenges to the development of appropriate diagnostic tools and treatments.
The goal of this Research Topic is to review assessment research of depressive disorder in children and adolescents with a focus on the diagnostic issue more specifically using the recent advances in neuroimaging (for example machine learning methodologies). There are changing trends in neuroimaging studies but despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop “bridges” to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. We may integrate more recent advances (for example, multimodal imaging, AI, and genetics) for the diagnosis in pediatric mood disorders.
• The benefits and costs of involving clinical judgment in the diagnostic process;
• The validity of parent, teacher, and youth self-report of mood symptoms;
• How much cross-situational consistency is typically shown in mood and behaviour in different cultural spectrum;
• How different measures compare in terms of detecting mood disorder;
• The challenges in comparing the performance of measures across research groups;
• The understanding of the neural underpinnings and neuro-functional deficits involved in depression in developmental population
• The implications of newer biological and technological advances in the assessment of pediatric mood disorders (for example, machine learning etc.);
• Research to increase the understanding of phenomenology of mood disorder from a developmental framework;
• Neuroimaging methodology and its use for diagnosis including functional MRI and connectivity studies and machine learning;
• Cytokines and other neuroinflammatory biomarkers of depressive disorders;
• Comorbidity and differential diagnosis (from bipolar disorder etc.).
