Prognostic prediction of therapeutic response in depression using high-field MR imaging

Gong, Qiyong; Wu, Qisong; Scarpazza, Cristina; Lui, Su; Jia, Zhiyun; Marquand, André F.; Huang, Xiaoqi; McGuire, Philip; Mechelli, Andrea

doi:10.1016/j.neuroimage.2010.11.079

Cited by 176 publications

(151 citation statements)

References 27 publications

(40 reference statements)

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“…28 The support vector machine recursive feature elimination method can predict group membership at an individual subject level, and the results obtained by using this method may be clinically useful 29 because the results can include unique information that may be overlooked by univariate voxel-based morphometry approaches. 30,31 As indicated in Fig 1, maximal classification accuracy (generalization rate ϭ 95.2%, area under the receiver operating characteristic curve ϭ 100%) was obtained by using only 23 features.…”

Section: Identification Of Fcsmentioning

confidence: 99%

Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

et al. 2015

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Although most studies on epilepsy have focused on the epileptogenic zone, epilepsy is a system-level disease characterized by aberrant neuronal synchronization among groups of neurons. Increasingly, studies have indicated that mesial temporal lobe epilepsy may be a network-level disease; however, few investigations have examined resting-state functional connectivity of the entire brain, particularly in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. This study primarily investigated whole-brain resting-state functional connectivity abnormality in patients with mesial temporal lobe epilepsy and right hippocampal sclerosis during the interictal period.

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Section: Identification Of Fcsmentioning

confidence: 99%

Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

et al. 2015

AJNR Am J Neuroradiol

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“…[29,30] It was reported that depressive patients with hypometabolic orbitofrontal cortex responded better to pharmacotherapy, whereas those with hypermetabolic orbitofrontal cortex responded better to psychotherapy. Accordingly, studies on the prediction of treatment outcome by neuroimaging prior to therapy had been continuously conducted [31,32] in order to investigate how new treatments of depression, such as mindfulness-based cognitive therapy and acceptance and commitment therapy, change the brain process [33,34] by which the neuroimaging study will be closer to the actual clinical practice.…”

Section: Prediction Of the Prognosis Of Depressionmentioning

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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“…60 As well, we have observed distinct predictors of clinical response to pharmacotherapy, compared with psychological treatment. The structural neuroanatomy of depression was highly predictive treatment response to ADs, 40,61 while it was the pattern of functional neural responses to emotional processing that predicted clinical response to cognitive-behavioural therapy. 62 Functional neural correlates of emotional processing or verbal memory showed statistically significant but clinically limited predictive potential for ADs.…”

Section: Predicting Clinical Response To Treatmentmentioning

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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Prognostic prediction of therapeutic response in depression using high-field MR imaging

Cited by 176 publications

References 27 publications

Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy

The Effects of Psychotherapy on Brain Function — Major Depressive Disorder

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

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