Integrating Neurobiological Markers of Depression

Hahn, Tim; Marquand, André F.; Ehlis, Ann‐Christine; Dresler, Thomas; Kittel‐Schneider, Sarah; Jarczok, Tomasz A.; Lesch, Klaus‐Peter; Jakob, Peter M.; Mourão-Miranda, Janaı́na; Brammer, Michael; Fallgatter, Andreas J.

doi:10.1001/archgenpsychiatry.2010.178

Cited by 135 publications

(125 citation statements)

References 42 publications

(62 reference statements)

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“…According to recent neuroimaging studies, depression is considered as an outcome of the abnormality of various interactions and systems rather than the abnormality of a single area of the brain. [35] The cortico-limbic circuit is currently gaining attention due to its ability of mediating stress response and playing an important role in the regulation of emotions. In early neuroimaging studies, which investigated the role of the limbic area in depression, the structural abnormalities of the limbic area, such as the atrophy or bilateral loss of the amygdala [36,37] and the volume reduction of the hippocampus [38,39] and caudate nucleus [40], were consistently reported in depressive patients.…”

Section: Neuroimaging Study On Depressive Disordermentioning

confidence: 99%

The Effects of Psychotherapy on Brain Function — Major Depressive Disorder

Jeon¹,

Kim²

2015

Major Depressive Disorder - Cognitive and Neurobiological Mechanisms

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Section: Neuroimaging Study On Depressive Disordermentioning

confidence: 99%

The Effects of Psychotherapy on Brain Function — Major Depressive Disorder

Jeon¹,

Kim²

2015

Major Depressive Disorder - Cognitive and Neurobiological Mechanisms

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“…Additional sensitivity though may be generated by combining functional imaging tasks of emotional and reward processing. 39 Structural abnormalities are evident in depression, in particular in the hippocampus, which is present in the first episode 40 ; however, these provided statistically significant but clinically limited diagnostic potential (sensitivity 65%, specificity 70%) for patients with a moderate severity of depression. 40 The diagnostic accuracy of structural neuroimaging data, though, may be greater in patients who experience a more severe form of the illness.…”

Section: Figure 2 Training and Testing Of Classification Modelsmentioning

confidence: 99%

“…74 In depression, the findings that functional abnormalities have diagnostic potential 36 and the predictive potential of grey matter abnormalities 40 has been confirmed in independent samples. 39,61 Validation studies are essential and should include subjects representative of the population, compared with the so-called clean, comorbidity-free, clearcut patients who are normally used in proof-of-principle studies. 73 Multicentre designs are particularly convincing, because they can test whether that the prediction is robust to differences in prevalence, recruitment, and clinical management.…”

Section: Next Stepsmentioning

confidence: 99%

Neuroimaging-Based Biomarkers in Psychiatry: Clinical Opportunities of a Paradigm Shift

Costafreda

2013

Can J Psychiatry

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Neuroimaging research has substantiated the functional and structural abnormalities underlying psychiatric disorders but has, thus far, failed to have a significant impact on clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation. This review introduces the key concepts of this approach, including how the multivariate integration of patterns of brain abnormalities is a crucial component. We survey recent findings that have potential application for diagnosis, in particular early and differential diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response to treatment in depression. We discuss the specific clinical opportunities and the challenges for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We propose that longitudinal outcomes, such as early diagnosis and prediction of treatment response, offer definite opportunities for progress. We propose that efforts should be directed toward clinically challenging predictions in which neuroimaging may have added value, compared with the existing standard assessment. We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis. W W W Biomarqueurs de neuroimagerie en psychiatrie : possibilités cliniques d'un changement de paradigmeLa recherche en neuroimagerie a fourni la preuve des anomalies fonctionnelles et structurelles sous-jacentes des troubles psychiatriques, mais jusqu'ici, elle n'a pas réussi à avoir un impact significatif sur la pratique clinique. Récemment, les diagnostics basés sur la neuroimagerie et les prédictions cliniques tirées d'une analyse d'apprentissage automatique ont démontré un potentiel significatif de traduction clinique. Cette revue présente les concepts clés de cette approche, notamment à quel point l'intégration multivariée des modèles d'anomalies cérébrales est un composant essentiel. Nous passons en revue les résultats récents qui ont des applications potentielles au diagnostic, en particulier, pour les diagnostics précoces et différentiels de la maladie d'Alzheimer et de la schizophrénie, et la prédiction de la réponse clinique au traitement de la dépression. Nous discutons des possibilités cliniques spécifiques et des défis de développement de biomarqueurs pour la psychiatrie en l'absence de standard de référence diagnostique. Nous proposons que les résultats longitudinaux, comme le diagnostic précoce et la prédiction de la réponse au traitement, offrent des possibilités définitives de progrès. Nous proposons que des efforts soient dirigés vers des prédictions cliniquement difficiles dans lesquelles la neuroimagerie peut avoir une valeur ajoutée, comparée à l'évaluation standard existante. Nous concluons que les biomarqueurs diagnostiques et pronostiques seront développés par l'application conjointe du savoir psychiatrique expert et ...

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“…MVPA is also a promising tool for psychiatric research. Most psychiatric applications have, so far, aimed to classify individuals into diagnostic groups by their patterns of brain activation or structure [9,10], or to predict treatment response or prognosis [11,12] In the affective domain, a number of studies have attempted to classify the processing of emotion-related information in the human brain, including near-threshold fear [13], pleasantness of thermal stimuli [14], emotional prosody [15], imagery [16], and cross-modal integration of emotional cues from faces and body signals [17]. These studies show that multivariate statistics can predict the valence of sensory stimuli and even internally generated affective states [16] Another open question addressed in the present paper is whether such classification will work across subjects, which will be important for any diagnostic application of normative data.…”

Section: Introductionmentioning

confidence: 99%

Pattern classification predicts individuals’ responses to affective stimuli

Yuen¹,

Johnston

Martino

et al. 2012

Translational Neuroscience

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Since the successful demonstration of "brain reading" of fMRI BOLD signals using multivoxel pattern classification (MVPA) techniques, the neuroimaging community has made vigorous attempts to exploit the technique in order to identify the signature patterns of brain activities associated with different cognitive processes or mental states. In the current study, we tested whether the valence and arousal dimensions of the affective information could be used to successfully predict individual's active affective states. Using a whole-brain MVPA approach, together with feature elimination procedures, we are able to discriminate between brain activation patterns associated with the processing of positive or negative valence and cross validate the discriminant function with an independent data set. Arousal information, on the other hand, failed to provide such discriminating power. With an independent sample, we test further whether the MVPA identified brain network could be used for inter-individual classification. Although the inter-subject classification success was only marginal, we found correlations with individual differences in affective processing. We discuss the implications of our findings for future attempts to classify patients based on their responses to affective stimuli.

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Integrating Neurobiological Markers of Depression

Cited by 135 publications

References 42 publications

The Effects of Psychotherapy on Brain Function — Major Depressive Disorder

The Effects of Psychotherapy on Brain Function — Major Depressive Disorder

Neuroimaging-Based Biomarkers in Psychiatry: Clinical Opportunities of a Paradigm Shift

Pattern classification predicts individuals’ responses to affective stimuli

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