Hippocampal volumes in bipolar disorders: opposing effects of illness burden and lithium treatment

Hájek, Tomáš; Cullis, Jeffrey; Novák, Tomáš; Kopeček, Miloslav; Höschl, Cyril; Blagdon, Ryan; O′Donovan, Claire; Bauer, Michael; Young, L. Trevor; MacQueen, Glenda; Alda, Martin

doi:10.1111/j.1399-5618.2012.01013.x

Cited by 104 publications

(58 citation statements)

References 48 publications

(72 reference statements)

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“…There is replicated evidence suggesting that repeated episodes of illness, 8,9 comorbid conditions, 13,14 or exposure to medications [10][11][12] introduce heterogeneity, which may mask/overcome the primary changes indicative of the risk for BD. 16,18,19 The findings emphasize the need to control for clinical heterogeneity, for example by recruiting unaffected participants at high genetic risk for the illness.…”

Section: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 98%

“…Clinical heterogeneity with regards to medication exposure, duration of illness and comorbid conditions influences brain structure in individuals with BD, sometimes in opposing directions. 8,18 The fact that the unaffected groups included only medication-naive, unaffected participants without any comorbid conditions makes the interpretation of findings much easier. Although the HR design decreases the clinical heterogeneity, it may introduce other sources of hetero geneity vis-à-vis resilience/protective factors.…”

Section: Limitationsmentioning

confidence: 99%

“…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][11][12] or comorbid conditions. 13,14 The changes secondary to illness burden, medication exposure or comorbid conditions have limited diagnostic potential as they occur only later in the course of BD.…”

Section: Introductionmentioning

confidence: 99%

“…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. This can be achieved by studying the unaffected offspring of parents with BD, who are at high genetic risk for the illness but do not show any consequences of the illness -a so-called genetic high risk (HR) design.…”

Section: Introductionmentioning

confidence: 99%

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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: J Psychiatry Neurosci 2015;40(5)mentioning

confidence: 98%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

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“…16 Recent imaging studies have shown that long-term lithium treatment in these patients is neuroprotective. [17][18][19] Reflecting the broadening of the diagnostic criteria (the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised, and beyond) and the preference to diagnose BD instead of psychotic disorders, 20 there has been increased debate about progressively shortened cycle lengths and recasting BD as typically a progressive deteriorating illness. 21 For example, using current diagnostic criteria, it has been estimated that the classical episodic lithium responsive subtype accounts for only 30% of patients in a typical Canadian subspecialty outpatient program.…”

Section: Natural History and Heterogeneity Of Bipolar Disordermentioning

confidence: 99%

Toward a Comprehensive Clinical Staging Model for Bipolar Disorder: Integrating the Evidence

Duffy

2014

Can J Psychiatry

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Objectives: To describe key findings relating to the natural history and heterogeneity of bipolar disorder (BD) relevant to the development of a unitary clinical staging model. Currently proposed staging models are briefly discussed, highlighting complementary findings, and a comprehensive staging model of BD is proposed integrating the relevant evidence.Method: A selective review of key published findings addressing the natural history, heterogeneity, and clinical staging models of BD are discussed. Results:The concept of BD has broadened, resulting in an increased spectrum of disorders subsumed under this diagnostic category. Different staging models for BD have been proposed based on the early psychosis literature, studies of patients with established BD, and prospective studies of the offspring of parents with BD. The overarching finding is that there are identifiable sequential clinical phases in the development of BD that differ in important ways between classical episodic and psychotic spectrum subtypes. In addition, in the context of familial risk, early risk syndromes add important predictive value and inform the staging model for BD. Conclusions:A comprehensive clinical staging model of BD can be derived from the available evidence and should consider the natural history of BD and the heterogeneity of subtypes. This model will advance both early intervention efforts and neurobiological research. W W WVers un modèle complet de staification clinique du trouble bipolaire : intégrant l'évidence Objectifs : Décrire les principaux résultats concernant l'histoire naturelle et l'hétérogénéité du trouble bipolaire (TB) qui se rapportent à l'élaboration d'un modèle unitaire de stades cliniques. Les modèles de staification présentement proposés sont brièvement présentés, de même que les résultats complémentaires, et un modèle complet de staification du TB est proposé qui intègre les données probantes pertinentes.Méthode : Est présentée une revue sélective des principaux résultats publiés sur l'histoire naturelle, l'hétérogénéité, et les modèles de staification clinique du TB.Résultats : Le concept du TB s'est élargi, ce qui a donné un spectre accru de troubles classés dans cette catégorie diagnostique. Différents modèles de staification du TB ont été proposés d'après la littérature sur la psychose précoce, les études de patients souffrant d'un TB établi, et les études prospectives des enfants de parents souffrant de TB. Le résultat déterminant est qu'il y a des phases cliniques séquentielles identifiables dans le développement du TB qui diffèrent de manière importante entre les sous-types épisodiques classiques et ceux du spectre psychotique. En outre, dans le contexte du risque familial, les syndromes de risque précoce ajoutent une valeur prédictive importante et éclairent le modèle de stadification du TB.Conclusions : Un modèle complet de stadification clinique du TB peut être déduit des données probantes disponibles et devrait tenir compte de l'histoire naturelle du TB et de l'hétérogénéité des sous-types. Ce ...

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Immunohistochemical evidence for adult human neurogenesis in health and disease

Gault

Szele

2021

WIREs Mechanisms of Disease

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Postnatal and adult neurogenesis in the subventricular zone and subgranular zone of animals such as rodents and non-human primates has been observed with many different technical approaches. Since most techniques used in animals cannot be used in humans, the majority of human neurogenesis studies rely on postmortem immunohistochemistry. This technique is difficult in human tissue, due to poor and variable preservation of antigens and samples.Nevertheless, a survey of the literature reveals that most published studies provide evidence for childhood and adult neurogenesis in the human brain stem cell niches. There are some conflicting results even when assessing the same markers and when using the same antibodies. Focusing on immunohistochemical studies on post-mortem human sections, we discuss the relative robustness of the literature on adult neurogenesis. We also discuss the response of the subventricular and subgranular zones to human disease, showing that the two niches can respond differently and that the stage of disease impacts neurogenesis levels. Thus, we highlight strong evidence for adult human neurogenesis, discuss other work that did not find it, describe obstacles in analysis, and offer other approaches to evaluate the neurogenic potential of the subventricular and subgranular zones of Homo sapiens.

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Hippocampal volumes in bipolar disorders: opposing effects of illness burden and lithium treatment

Cited by 104 publications

References 48 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

Toward a Comprehensive Clinical Staging Model for Bipolar Disorder: Integrating the Evidence

Immunohistochemical evidence for adult human neurogenesis in health and disease

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