Brain Structural Signature of Familial Predisposition for Bipolar Disorder: Replicable Evidence For Involvement of the Right Inferior Frontal Gyrus

Hájek, Tomáš; Cullis, Jeffrey; Novák, Tomáš; Kopeček, Miloslav; Blagdon, Ryan; Propper, Lukáš; Stopková, Pavla; Duffy, Anne; Höschl, Cyril; Uher, Rudolf; Paus, Tomáš; Young, L. Trevor; Alda, Martin

doi:10.1016/j.biopsych.2012.06.015

Cited by 124 publications

(134 citation statements)

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“…[15][16][17] They may be underrepresented in convenience samples, which include patients with long chronic illness, polypharmacy and medical and psychiatric comorbid conditions. 18 In such samples, neuroimaging may primarily detect the consequences of the illness, medication exposure or signatures of comorbid conditions, which may mask or override the diagnostically relevant biomarkers. 8,12,16,18,19 Consequently, the identification of brain changes with diagnostic potential requires research designs that attempt to minimize or completely eliminate the secondary brain changes and thus increase sensitivity for detection of the primary alterations/ biological risk factors.…”

Section: Introductionmentioning

confidence: 99%

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Hájek

Cooke

Kopeček

et al. 2015

jpn

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IntroductionThe diagnostic system in psychiatry continues to be based on behavioural symptoms rather than biomarkers. This complicates clinical work and research as it introduces marked hetero geneity. Neuroimaging has the unique ability to noninvasively investigate brain structure and function. Yet, the diag nostic promise of neuroimaging in psychiatry has not been fully realized. Brain imaging studies have shown replicated evidence for neuroanatomical changes in groups of participants with psychiatric disorders relative to controls. However, these statistical group differences have low specificity and sensitivity and thus are of limited diagnostic use on the level of individual participants. 1-3The problem of low sensitivity and specificity may be overcome by novel methods of neuroimaging analyses, such as machine learning (ML).1,2 Traditional methods of MRI data analysis focus on relatively large, localized and spatially segregated patterns of between-group differences. 4 In contrast, the multivariate ML techniques target patterns of relatively minor alterations distributed throughout the whole brain, 1 which may better characterize the abnormalities found in individuals with psychiatric disorders.5 These techniques bring neuroimaging analyses to the level of individual participants and potentially allow for their diagnostic use.The use of neuroimaging for diagnostic purposes in psychiatry is further complicated by clinical heterogeneity.6,7 Not all neuroimaging findings in psychiatric patients are of diagnostic use. For example, brain changes in patients with bipolar disorders (BD) may represent biological markers of BD, but also the consequences of illness episodes, 8,9 exposure to medications 10-12 or comorbid conditions. 13,14 The changes Background: Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of individual participants. Studying unaffected offspring of parents with bipolar disorders (BD) decreases clinical heterogeneity and thus increases sensitivity for detection of biomarkers. The present study used ML to identify individuals at genetic high risk (HR) for BD based on brain structure. Methods:We studied unaffected and affected relatives of BD probands recruited from 2 sites (Halifax, Canada, and Prague, Czech Republic). Each participant was individually matched by age and sex to controls without personal or family history of psychiatric disorders. We applied support vector machines (SVM) and Gaussian process classifiers (GPC) to structural MRI. Results: We included 45 unaffected and 36 affected relatives of BD probands matched by age and sex on an individual basis to healthy controls. The SVM of white matter distinguished unaffected HR from control participants (accuracy = 68.9%, p = 0.001), with similar accuracy for the GPC (65.6%, p = 0.002) or when analyzing data from each site separately. Differentiation of the mo...

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Section: Introductionmentioning

confidence: 99%

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Hájek

Cooke

Kopeček

et al. 2015

jpn

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“…[1][2][3] These studies, among others, described early stages of the illness and their comorbidities 4 as well as neuropsychological function ing 5 and structural brain findings. 6 Based on these studies as well as on observations of other authors, we be lieve that the point of heterogeneity of bipolar disorder and the need to sepa rate it from the concept of uniformly progressing illness are equally applica ble in the early stages as in the latter ones. The prodromes may appear non specific and uninformative with re spect to future illness trajectories when viewed through the prism of current diagnostic classification.…”

Section: Author Responsementioning

confidence: 81%

Concepts and misconceptions regarding clinical staging models

Alda

Kapczinski

2016

jpn

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“…49,[52][53][54][55][56][57][58] The specific involvement of the IFG adds to a converging body of evidence from structural and functional studies of BD. [59][60][61][62][63][64] Likewise, the involvement of the insula adds to a growing number of reports of structural and functional differences in HR cohorts. 23,[65][66][67] The involvement of a left-sided network is unique to the at-risk group (relative to CNs) and is not present in the bipolar group.…”

Section: Discussionmentioning

confidence: 99%

784. Structural Dysconnectivity of Key Cognitive and Emotional Hubs in Young People at High Genetic Risk for Bipolar Disorder

Mitchell

Breakspear

Roberts

et al. 2017

Biological Psychiatry

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Brain Structural Signature of Familial Predisposition for Bipolar Disorder: Replicable Evidence For Involvement of the Right Inferior Frontal Gyrus

Cited by 124 publications

References 82 publications

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Concepts and misconceptions regarding clinical staging models

784. Structural Dysconnectivity of Key Cognitive and Emotional Hubs in Young People at High Genetic Risk for Bipolar Disorder

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