Hypothalamus and pituitary volume in schizophrenia: A structural MRI study

The International Journal of Neuropsychopharmacology (Impact Factor: 4.01). 02/2012; 15(02):281 - 288. DOI: 10.1017/S1461145711000794


Volumetric differences of the hypothalamus and/or the pituitary gland tend to support involvement of the HPA axis in psychotic disorders. These structures were manually outlined in 154 schizophrenia patients and 156 matched healthy comparison subjects by MRI brain images. Linear regression analyses were performed to investigate differences in volume between groups. Moreover, the effects of illness duration and type of medication were investigated. No significant differences were found between patients and healthy controls in volumes of the hypothalamus and pituitary gland. In addition, there were no differences in volumes between patients with short and long illness duration. There was a trend towards patients receiving typical antipsychotic medication at the time of scanning having larger pituitary volumes than patients receiving atypical medication. These findings indicate that volume decreases in brain structures important for the normal functioning of the HPA axis are not present, either in recent-onset or chronically ill patients.

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Available from: P Cédric MP Koolschijn
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    • "Typically, cross-sectional studies have observed larger pituitary volumes among patients with first-episode psychosis relative to healthy controls (Pariante et al. 2004, 2005; Büschlen et al. 2011; Takahashi et al. 2011) while smaller volumes relative to controls have been reported among those with established schizophrenia (Pariante et al. 2004; Upadhyaya et al. 2007). Not all studies, however, have shown differences in pituitary volume between psychosis patients (either those with first-episode or chronic illness) and healthy controls (Nicolo et al. 2010; Klomp et al. 2012). These abnormalities may reflect changes in hypothalamic–pituitary–adrenal (HPA) axis activity, the primary system involved in coordinating the physiological response to stressors. "
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    ABSTRACT: Pituitary volume enlargements have been observed among individuals with first-episode psychosis. These abnormalities are suggestive of hypothalamic-pituitary-adrenal (HPA) axis hyperactivity, which may contribute to the development of psychosis. However, the extent to which these abnormalities characterize individuals at elevated risk for schizophrenia prior to illness onset is currently unclear, as volume increases, decreases and no volume differences have all been reported relative to controls. The current study aimed to determine whether antipsychotic-naive, putatively at-risk children who present multiple antecedents of schizophrenia (ASz) or a family history of illness (FHx) show pituitary volume abnormalities relative to typically developing (TD) children. An additional aim was to explore the association between pituitary volume and experiences of psychosocial stress. ASz (n = 30), FHx (n = 22) and TD (n = 32) children were identified at age 9-12 years using a novel community-screening procedure or as relatives of individuals with schizophrenia. Measures of pituitary volume and psychosocial stress were obtained at age 11-14 years. Neither ASz nor FHx children showed differences in pituitary volume relative to TD children. Among FHx children only, pituitary volume was negatively associated with current distress relating to negative life events and exposure to physical punishment. The lack of pituitary volume abnormalities among ASz and FHx children is consistent with our previous work demonstrating that these children are not characterized by elevated diurnal cortisol levels. The findings imply that these biological markers of HPA axis hyperactivity, observed in some older samples of high-risk individuals, may emerge later, more proximally to disease onset.
    Full-text · Article · Jul 2015 · Psychological Medicine
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    • "ed manual segmentation of the hypothalamus to calculate hypothalamic volume and we performed VBM analysis to determine voxel - wise differences in volume between groups according to Holle et al . ( 2011 ) . Manual segmentation of the hypothalamus was done based on morphological anatomical landmarks as described earlier ( Goldstein et al . , 2007 ; Klomp et al . , 2012 ; Terlevic et al . , 2013 ) . A coronal view of an example segmentation of the hypothalamus is shown in Figure 2 . Differences in hypothalamic volume between patients and controls were tested using an independent samples t - test . FIGURE 1 | Segmentation and subdivision of the hypothalamus into four ROIs . For analysis of the hypothalam"
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    ABSTRACT: Inconsistent findings about the neurobiology of Anorexia Nervosa (AN) hinder the development of effective treatments for this severe mental disorder. Therefore, the need arises for elucidation of neurobiological factors involved in the pathophysiology of AN. The hypothalamus plays a key role in the neurobiological processes that govern food intake and energy homeostasis, processes that are disturbed in anorexia nervosa (AN). The present study will assess the hypothalamic response to energy intake and the hypothalamic structure in patients with AN and healthy controls. Ten women aged 18-30 years diagnosed with AN and 11 healthy, lean (BMI < 23 kg/m(2)) women in the same age range were recruited. We used functional magnetic resonance imaging (MRI) to determine function of the hypothalamus in response to glucose. Structural MRI was used to determine differences in hypothalamic volume and local gray matter volume using manual segmentation and voxel-based morphometry. No differences were found in hypothalamic volume and neuronal activity in response to a glucose load between the patients and controls. Whole brain structural analysis showed a significant decrease in gray matter volume in the cingulate cortex in the AN patients, bilaterally. We argue that in spite of various known changes in the hypothalamus the direct hypothalamic response to glucose intake is similar in AN patients and healthy controls.
    Full-text · Article · May 2015 · Frontiers in Neuroscience
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    • "We observed that majority of the SDG interactions were present in hypothalamus. Earlier studies have also reported the association of hypothalamus with schizophrenia, though its specific role in disease etiology is still not clear (Hans-Gert et al., 2010; Klomp et al., 2012; Tognin et al., 2012). In view of this, we suggest that the hypothalamus plays a crucial role in understanding the tissue specificity of the disease. "
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    ABSTRACT: Schizophrenia, a complex neurological disorder, is comprised of interactions between multiple genetic and environmental factors wherein each of the factors individually exhibits a small effect. In this regard a network-based strategy is best suited to capture the combined effect of multiple genes with their definite pattern of interactions. Given that schizophrenia affects multiple regions of the brain, we postulated that instead of any single specific tissue, a mutual set of interactions occurs between different regions of brain in a well-defined pattern responsible for the disease phenotype. To validate, we constructed and compared tissue specific co-expression networks of schizophrenia candidate genes in twenty diverse brain tissues. As predicted, we observed a common interaction network of certain genes in all the studied brain tissues. We examined fundamental network topologies of the common network to sequester essential common candidates for schizophrenia. We also performed a gene set analysis to identify the essential biological pathways enriched by the common candidates in the network. Finally, the candidate drug targets were prioritized and scored against known available schizophrenic drugs by molecular docking studies. We distinctively identified protein kinases as the top candidates in the network that can serve as probable drug targets for the disease. Conclusively, we propose that a comprehensive study of the connectivity amongst the disease genes themselves may turn out to be more informative to understand schizophrenia disease etiology and the underlying complexity.
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