Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Caspi, Yaron; Brouwer, Rachel M.; Schnack, Hugo G.; Nieuwenhuijzen, Marieke E. van de; Cahn, Wiepke; Kahn, René S.; Niessen, Wiro J.; Lugt, Aad van der; Pol, Hilleke E. Hulshoff

doi:10.1016/j.neuroimage.2020.116842

Cited by 35 publications

(32 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To show the effects of de-identification while preventing participant identification, we created average brains (Caspi et al, 2020;Peper et al, 2009). In short, the individual scans were registered to Talairach space and corrected for nonuniformity followed by a series of linear and nonlinear warpings of the scans (Collins, Holmes, Peters, & Evans, 1995).…”

Section: Visualization Of De-identificationmentioning

confidence: 99%

De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

Buimer

Schnack

Caspi

et al. 2021

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy-sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1-weighted MRI scans of children, young adults and older adults. For the young adults, test-retest data were included with a 1-week interval. The effects of the de-identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de-identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de-identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de-identification, depending on the studied subsample, de-identification method, and brain metric. In young adults, test-retest intraclass correlation coefficients (ICCs) were comparable for original scans and de-identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness.We conclude that apparent visual differences between de-identification methods Part of the data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu

show abstract

Section: Visualization Of De-identificationmentioning

confidence: 99%

De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

Buimer

Schnack

Caspi

et al. 2021

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…Global and regional gray matter volumes are heritable in late childhood (Peper et al, 2009). Subcortical gray matter volume increases for the thalamus, hippocampus, amygdala and pallidum, and decreases in the caudate and nucleus accumbens, and are highly heritable at similar levels as during adulthood (Swagerman et al, 2014;Brouwer et al, 2020). The change rates of subcortical volume are also heritable with several genetic loci recently identified (Brouwer et al, 2017;Brouwer et al, 2020).…”

Section: Structural Brain Developmentmentioning

confidence: 99%

“…Similarly, although sex effects have been reported for cortical thickness (Lenroot and Giedd, 2010), this effect is not always observed (van Soelen Chapter 1 et al, 2012). Sex effects of volumetric measures are largely attributed to intracranial volume that is positively associated with stature and where both measures share a common genetic background (Caspi et al, 2020;. Sex effects are also determined by pubertal hormones that influence brain structure (Koolschijn et al, 2014); a review of the effect of pubertal hormones on brain structure suggesting that changes in sex hormones might trigger a reorganization of the structural brain .…”

Section: Structural Brain Developmentmentioning

confidence: 99%

“…Genetic influences may vary with age and also drive changes in the brain (i.e. brain plasticity; Pfefferbaum et al, 2004;Brans et al, 2008;Lessov-Schlaggar et al, 2012;Bootsman et al, 2015;Hedman et al, 2016;Brouwer et al, 2017;Brouwer et al, 2020). The expression of genes is highly regulated, especially during the early developmental stages (Kang et al, 2011;.…”

Section: General Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genetic influences on structural and functional brain maturation

Teeuw¹

View full text Add to dashboard Cite

General introduction 1 controls that was used to study accelerated aging of the brain in relation to accelerated epigenetic aging and polygenic risk for schizophrenia (Schnack et al., 2016;Ori et al., 2019). Finally, the Human Connectome Project Young Adult cohort that was used to evaluate the effectiveness of reliability modeling of functional connectivity of the brain in relation to various traits (van Essen et al., 2013). The BrainSCALE cohortThe BrainSCALE cohort is a longitudinal study of adolescent twins born between 1995-1996 whose parents registered them with the Netherlands Twin Register (NTR; Ligthart et al., 2019). Twin pairs who had an older sibling who was also willing to take part in the study were recruited through the NTR. The cohort was designed to investigate cognitive, behavioral, physical and physiological transitions during childhood and adolescence in relation to brain development and genetics. It is a collaborative effort between the University Medical Center Utrecht (UMCU) and Vrije Universiteit Amsterdam (VU Amsterdam). The subjects were selected to represent a cross-section of the Dutch population with an equal proportion of the sexes (52% female; including 20 dizygotic twin pairs of opposite sex), a mixture of ethnical background and culture, normal cognitive functioning (intelligence quotient 101.9 ± 14.75 at baseline), and no prior history of physical or mental health problems. They were assessed when the twins were 9, 12, and 17 years of age; average ages were 9.3 ± 0.28, 12.2 ± 0.35 and 17.2 ± 0.37 years, with their older siblings on average 2.7 ± 1.1 years older. Extensive phenotypic information was collected about the children, including cognitive, behavioral, and neuropsychological tests, physical assessment, hormone levels, and magnetic resonance imaging scans of the brain Koenis et al., 2013). The BrainSCALE study was approved by the Central Committee on Research Involving Human Subjects of The Netherlands (CCMO). CHAPTER 2 LONGITUDINAL DEVELOPMENT OF CEREBRAL CORTICAL THICKNESSOriginal title: Genetic influences on the development of cerebral cortical thickness during childhood and adolescence in a Dutch longitudinal twin sample: the BrainSCALE study

show abstract

“…At the time of the MRI scan, the average age was 63 years old. A recent longitudinal study has shown that ICV does not stay constant during adulthood but instead shows small increases during young adulthood and decreases from the fourth decade of life (Caspi et al, 2019). Therefore, we have to be careful with drawing conclusions on the differential effect between the phenotype ICV and ICV-PGS.…”

mentioning

confidence: 98%

It's all relative

Zwarte¹,

Cornelia²

View full text Add to dashboard Cite

Chapter 1 General introductionChapter 2 Running in the family? Structural brain abnormalities and IQ in offspring, siblings, parents, and co-twins of patients with schizophrenia Chapter 3The association between familial risk and brain abnormalities is disease specific: an ENIGMA-Relatives study of schizophrenia and bipolar disorder Press release Biological Psychiatry Chapter 4 Intelligence, educational attainment and brain structure in those at familial high-risk for schizophrenia or bipolar disorder Chapter 5 The genetic relationship between schizophrenia, bipolar disorder and intracranial volume through polygenic scoring Chapter 6 Summary and general discussion Chapter 7 References Appendices Nederlandse samenvatting 170 Dankwoord 175 List of publications 178 Curriculum Vitae 181 8Almost everyone, either directly or indirectly, will be confronted with mental illness at some point in their lives. According to the World Health Organization, around 23 million people worldwide have a diagnosis of schizophrenia, bipolar disorder affects around 60 million people, and an estimated 300 million people are diagnosed with severe depression (World Health Organization, 2018). This means that roughly 1 in every 20 individuals is affected by a severe mental illness and the impact on the lives of these individuals, but also on their family, friends, and society as a whole, can be tremendous. Yet we still know little about the etiology (i.e. what causes disease or leads to becoming ill) of these disorders, how to effectively treat patients, and, even more importantly, how to prevent people from becoming ill. SCHIZOPHRENIA vs. BIPOLAR DISORDERPsychiatric diseases are characterized by affective, behavioral, and cognitive abnormalities. The Diagnostic and Statistical Manual of Mental Disorders (DSM), published by the American Psychiatric Association (5th edition, 2013), lists 18 distinct disorders, including neurodevelopmental disorders, schizophrenia spectrum and psychotic disorders, bipolar and related disorders, depressive disorders and anxiety disorders.Schizophrenia and bipolar disorder are two severe psychiatric disorders listed in the DSM. Schizophrenia is characterized by delusions, hallucinations, disorganized speech and behavior, while bipolar disorder is primarily known for the (alternating) manic and depressive episodes. This distinction between these two illnesses dates back to the early 20 th century, when Kraepelin divided them into two categories: dementia praecox and manic depressive insanity (Kraepelin, 1910). Although the clinical presentation is often different, schizophrenia and bipolar disorder also share symptoms, which can make it difficult to distinguish between the two illnesses. For instance, approximately two-thirds of patients diagnosed with bipolar disorder have a life-time history of at least one psychotic episode (Goodwin & Jamison, 2007). Similarly, it is estimated that comorbid depression occurs in 50% of the patients diagnosed with schizophrenia (Buckley et al., 2009). The overlapping elements l...

show abstract

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Cited by 35 publications

References 77 publications

De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

Genetic influences on structural and functional brain maturation

It's all relative

Contact Info

Product

Resources

About