Diagnostic and therapeutic utility of neuroimaging in depression: an overview

Wise, Toby; Cleare, Anthony J.; Herane, A.; Young, Allan H.; Arnone, Danilo

doi:10.2147/ndt.s50156

Cited by 48 publications

(35 citation statements)

References 167 publications

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“…73 Compared with the findings in these modalities, the abnormalities reported from DTI voxel-wise analyses seem disproportionally small in distribution. 74 The present results suggest that DKI is capable of demonstrating abnormalities that are not fully depicted in DTI.…”

Section: Discussionmentioning

confidence: 51%

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder

et al. 2018

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Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that results from disruption of multiple neural circuits involved in emotional regulation. Although previous studies using diffusion tensor imaging (DTI) found smaller values of fractional anisotropy (FA) in the white matter, predominantly in the frontal lobe, of patients with MDD, studies using diffusion kurtosis imaging (DKI) are scarce. Here, we used DKI whole‐brain analysis with tract‐based spatial statistics (TBSS) to investigate the brain microstructural abnormalities in MDD. Twenty‐six patients with MDD and 42 age‐ and sex‐matched control subjects were enrolled. To investigate the microstructural pathology underlying the observations in DKI, a compartment model analysis was conducted focusing on the corpus callosum. In TBSS, the patients with MDD showed significantly smaller values of FA in the genu and frontal portion of the body of the corpus callosum. The patients also had smaller values of mean kurtosis (MK) and radial kurtosis (RK), but MK and RK abnormalities were distributed more widely compared with FA, predominantly in the frontal lobe but also in the parietal, occipital, and temporal lobes. Within the callosum, the regions with smaller MK and RK were located more posteriorly than the region with smaller FA. Model analysis suggested significantly smaller values of intra‐neurite signal fraction in the body of the callosum and greater fiber dispersion in the genu, which were compatible with the existing literature of white matter pathology in MDD. Our results show that DKI is capable of demonstrating microstructural alterations in the brains of patients with MDD that cannot be fully depicted by conventional DTI. Though the issues of model validation and parameter estimation still remain, it is suggested that diffusion MRI combined with a biophysical model is a promising approach for investigation of the pathophysiology of MDD.

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Section: Discussionmentioning

confidence: 51%

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder

et al. 2018

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“…Neuroimaging (eg, Wise et al195) and genetic markers (Tamatam et al196) are reviewed extensively elsewhere.…”

Section: Figurementioning

confidence: 99%

Biomarkers for depression: recent insights, current challenges and future prospects

Strawbridge¹,

Young²,

Cleare³

2017

NDT

306

156

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A plethora of research has implicated hundreds of putative biomarkers for depression, but has not yet fully elucidated their roles in depressive illness or established what is abnormal in which patients and how biologic information can be used to enhance diagnosis, treatment and prognosis. This lack of progress is partially due to the nature and heterogeneity of depression, in conjunction with methodological heterogeneity within the research literature and the large array of biomarkers with potential, the expression of which often varies according to many factors. We review the available literature, which indicates that markers involved in inflammatory, neurotrophic and metabolic processes, as well as neurotransmitter and neuroendocrine system components, represent highly promising candidates. These may be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomic and neuroimaging assessments. The use of novel approaches and systematic research programs is now required to determine whether, and which, biomarkers can be used to predict response to treatment, stratify patients to specific treatments and develop targets for new interventions. We conclude that there is much promise for reducing the burden of depression through further developing and expanding these research avenues.

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“…Some examples of network variation include failure to deactivate the DMN during tasks in both autism ( Spencer et al, 2012 ) and depression ( Grimm et al, 2008 ; Sheline et al, 2009 ); decreased DMN activity and connectivity in AD ( Sorg et al, 2007 ; Sheline and Raichle, 2013 ); antagonistic activity during attention-demanding tasks in schizophrenia ( Whitfield-Gabrieli et al, 2009 ); differences in functional connectivity in both anorexia ( Cowdrey et al, 2014 ) and autism ( Assaf et al, 2010 ) and network inhomogeneity in ADHD ( Uddin et al, 2008 ) and bipolar disorders ( Liu et al, 2013 ). In some disorders such as AD ( Sperling, 2011 ; Koch et al, 2012 ; Balthazar et al, 2014 ), depression ( Li et al, 2013 ; Wise et al, 2014 ), and schizophrenia ( Shen et al, 2014 ), these abnormalities of the DMN are consistent enough to be evaluated for use as diagnostic biomarkers.…”

Section: The Dmn As a Diagnostic Toolmentioning

confidence: 99%

The default mode network as a biomarker for monitoring the therapeutic effects of meditation

Simon

Engström

2015

Front. Psychol.

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The default mode network (DMN) is a group of anatomically separate regions in the brain found to have synchronized patterns of activation in functional magnetic resonance imaging (fMRI). Mentation associated with the DMN includes processes such as mind wandering, autobiographical memory, self-reflective thought, envisioning the future, and considering the perspective of others. Abnormalities in the DMN have been linked to symptom severity in a variety of mental disorders indicating that the DMN could be used as a biomarker for diagnosis. These correlations have also led to the use of DMN modulation as a biomarker for assessing pharmacological treatments. Concurrent research investigating the neural correlates of meditation, have associated DMN modulation with practice. Furthermore, meditative practice is increasingly understood to have a beneficial role in the treatment of mental disorders. Therefore we propose the use of DMN measures as a biomarker for monitoring the therapeutic effects of meditation practices in mental disorders. Recent findings support this perspective, and indicate the utility of DMN monitoring in understanding and developing meditative treatments for these debilitating conditions.

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Diagnostic and therapeutic utility of neuroimaging in depression: an overview

Cited by 48 publications

References 167 publications

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder

Biomarkers for depression: recent insights, current challenges and future prospects

The default mode network as a biomarker for monitoring the therapeutic effects of meditation

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