AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Acceptability of home-based transcranial direct current stimulation (tDCS) in major depression:a qualitative analysis of individual experiences

Purpose The purpose of this paper was to gain a qualitative view of the participant experience of using home-based transcranial direct current stimulation (tDCS). Acceptability impacts patient preference, treatment adherence and outcomes. However, acceptability is usually assessed by rates of attrition, while multifaceted constructs are not reflected or given meaningful interpretations. tDCS is a novel non-invasive brain stimulation that is a potential treatment for major depressive disorder (MDD). Most studies have provided tDCS in a research centre. As tDCS is portable, the authors developed a home-based treatment protocol that was associated with clinical improvements that were maintained in the long term. Design/methodology/approach The authors examined the acceptability of home-based tDCS treatment in MDD through questionnaires and individual interviews at three timepoints: baseline, at a six-week course of treatment, and at six-month follow-up. Twenty-six participants (19 women) with MDD in a current depressive episode of at least moderate severity were enrolled. tDCS was provided in a bifrontal montage with real-time remote supervision by video conference at each session. A thematic analysis was conducted of the individual interviews. Findings Thematic analysis revealed four main themes: effectiveness, side effects, time commitment and support, feeling held and contained. The themes reflected the high acceptability of tDCS treatment, whereas the theme of feeling contained might be specific to this protocol. Originality/value Qualitative analysis methods and individual interviews generated novel insights into the acceptability of tDCS as a potential treatment for MDD. Feelings of containment might be specific to the present protocol, which consisted of real-time supervision at each session. Meaningful interpretation can provide context to a complex construct, which will aid in understanding and clinical applications

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.</p

Delaying surgery for patients with a previous SARS-CoV-2 infection

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