Neural response to emotional prosody in schizophrenia and in bipolar affective disorder

Mitchell, Rachel L.C.; Elliott, Richard M.; Barry, Martin; Cruttenden, Alan; Woodruff, Peter

doi:10.1192/bjp.184.3.223

Cited by 119 publications

(88 citation statements)

References 27 publications

(37 reference statements)

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“…The results of the current study are in line with Mitchell et al (2004), who found atypical low activation in the right hemisphere (i.e., inferior frontal and superior temporal gyri, and amygdala) of BD patients, as compared to healthy controls, while listening to emotional prosody. This finding was interpreted by the authors as a reduced capacity to process emotional prosody in BD patients.…”

Section: Discussionsupporting

confidence: 92%

“…This finding was interpreted by the authors as a reduced capacity to process emotional prosody in BD patients. However, given that Mitchell et al (2004) did not control for mood state, it was unclear whether atypical functional brain organization, and atypical right hemispheric functioning in particular, only occurs during manic/depressive mood states or also applies to euthymic states in BD patients.…”

Section: Discussionmentioning

confidence: 99%

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Atypical right hemispheric functioning in the euthymic state of bipolar affective disorder

Najt

Hausmann

2014

Psychiatry Research

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Atypical right hemispheric functioning in the euthymic state of bipolar affective disorder

Najt

Hausmann

2014

Psychiatry Research

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“…One preliminary study by Mitchell and colleagues 78 compared the neural responses of controls and patients with schizophrenia and bipolar disorder to recorded scenarios presented in happy, sad and neutral intonations. Patients were scanned while passively listening to affective prosody stimuli and while actively attending to the emotional intonation of each phrase.…”

Section: Neuroimaging Studies Of Affective Prosody Recognition In Patmentioning

confidence: 99%

Systematic review of the neural basis of social cognition in patients with mood disorders

Cusi

Nazarov

Holshausen

et al. 2012

J Psychiatry Neurosci

255

114

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“…Several of the regions in this subnetwork deserve further consideration. For example, prior functional neuroimaging studies in BD have found evidence of superior temporal gyrus dysfunction during emotional processing [223,258,259] as well as during rest [237,260]. Medial temporal structures in particular (such as the amygdala and hippocampus, which constitute components of our sub-network) have been implicated in the structural pathology of BD patients and unaffected relatives, though findings have been inconsistent [261].…”

Section: Discussionmentioning

confidence: 99%

“…Bipolar disorder (BD) is a relatively common and highly disabling condition [30,223], the etiological and pathophysiological underpinnings of which are poorly understood. The characteristic profile of depressive and manic episodes in BD likely reflect impaired functioning of the neural substrates of emotion regulation and cognitive control.…”

Section: Introductionmentioning

confidence: 99%

Biometric markers for affective disorders

Lord¹

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Effective treatment of affective disorders is often challenged by incorrect or delayed diagnosis. Patients with bipolar disorder (BD) typically receive between 1 and 4 prior diagnoses of other mental health disorders preceding correct diagnosis, commonly with a delay from illness onset of 10 years, highlighting the difficulty of accurate diagnosis in a clinical setting. Most commonly, bipolar disorder is misdiagnosed as unipolar major depressive disorder (MDD), with between 25 and 50% of people diagnosed with depression being later re-classified as bipolar. The current problem of misdiagnosis is compounded by the lack of laboratory based quantitative testing for affective disorders available in clinical situations.The overarching objectives of this thesis are to demonstrate the utility of machine learning algorithms to neuroimaging data in order to provide a computational platform for classification, diagnosis and prediction of mood disorders. The thesis hence aims to unify three main themes -namely the clinical psychiatry of affective (mood) disorders, analytic principles of neuroimaging data (such as networkbased platforms) and machine learning.I first combine network theory and machine learning to address the issue of diagnostic classification using resting state functional magnetic resonance imaging (fMRI). I find that key metrics describing functional organization of the brain can be harnessed to yield accurate diagnostic boundaries in the data set I examine. In particular, the organization of ongoing brain activity into communities provides key information which distinguishes MDD from healthy controls.I next investigate a core group of brain regions which modulate network efficiency at a global scale, known as the rich club. Although this has previously been examined in structural brain networks, I apply it to resting state functional images. Furthermore, I investigate the robustness of the functional rich club following the correction of systematic artifacts in fMRI scans. Here I conclude a densely connected rich club is present in resting state fMRI across using all processing styles. However, the composition of members changes depending on key choices in the preprocessing of data. Further validation of the rich club should assess the impact of using different scanners or brain atlases.From chapter 4 onward, I shift the focus from depression to bipolar disorder. Here I start by assessing the functional connectivity of the inferior frontal gyrus, a region which is down regulated during an ii emotional recognition task in first degree relatives of bipolar subjects. Using a technique capable of identifying subtle alterations in functional networks I identified a community of connected brain regions with substantially reduced functional connectivity in bipolar subjects. I used machine learning to distinguish people with bipolar from first degree relatives and a healthy cohort. This was most effective between bipolar subjects and control subjects, while identifying the first degree relatives from eith...

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Neural response to emotional prosody in schizophrenia and in bipolar affective disorder

Abstract: Patients with schizophrenia and bipolar disorder may display some left-lateralisation of the normal right-lateralised temporal lobe response to emotional prosody.

Cited by 119 publications

References 27 publications

Atypical right hemispheric functioning in the euthymic state of bipolar affective disorder

Atypical right hemispheric functioning in the euthymic state of bipolar affective disorder

Systematic review of the neural basis of social cognition in patients with mood disorders

Biometric markers for affective disorders

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