Dehydration can affect brain structure which has important implications for human health. In this study, we measured regional changes in brain structure following acute dehydration. Healthy volunteers received a structural MRI scan before and after an intensive 90-min thermal-exercise dehydration protocol. We used two techniques to determine changes in brain structure: a manual point counting technique using MEASURE, and a fully automated voxelwise analysis using SIENA. After the exercise regime, participants lost (2.2% 6 0.5%) of their body mass. Using SIENA, we detected expansion of the ventricular system with the largest change occurring in the left lateral ventricle (P 5 0.001 corrected for multiple comparisons) but no change in total brain volume (P 5 0.13). Using manual point counting, we could not detect any change in ventricular or brain volume, but there was a significant correlation between loss in body mass and third ventricular volume increase (r 5 0.79, P 5 0.03). These results show ventricular expansion occurs following acute dehydration, and suggest that automated longitudinal voxelwise analysis methods such as SIENA are more sensitive to regional changes in brain volume over time compared with a manual point counting technique. Hum Brain Mapp 30: [291][292][293][294][295][296][297][298] 2009. V V C 2007 Wiley-Liss, Inc.

Although a wide range of approaches have been developed to automatically assess the volume of brain regions from MRI, the reproducibility of these algorithms across different scanners and pulse sequences, their accuracy in different clinical populations and sensitivity to real changes in brain volume has not always been comprehensively examined. Firstly we present a comprehensive testing protocol which comprises 312 freely available MR images to assess the accuracy, reproducibility and sensitivity of automated brain segmentation techniques. Accuracy is assessed in infants, young adults and patients with Alzheimer’s disease in comparison to gold standard measures by expert observers using a manual technique based on Cavalieri’s principle. The protocol determines the reliability of segmentation between scanning sessions, different MRI pulse sequences and 1.5T and 3T field strengths and examines their sensitivity to small changes in volume using a large longitudinal dataset. Secondly we apply this testing protocol to a novel algorithm for segmenting the lateral ventricles and compare its performance to the widely used FSL FIRST and FreeSurfer methods. The testing protocol produced quantitative measures of accuracy, reliability and sensitivity of lateral ventricle volume estimates for each segmentation method. The novel algorithm showed high accuracy in all populations (intraclass correlation coefficient, ICC>0.95), good reproducibility between MRI pulse sequences (ICC>0.99) and was sensitive to age related changes in longitudinal data. FreeSurfer demonstrated high accuracy (ICC>0.95), good reproducibility (ICC>0.99) and sensitivity whilst FSL FIRST showed good accuracy in young adults and infants (ICC>0.90) and good reproducibility (ICC=0.98), but was unable to segment ventricular volume in patients with Alzheimer’s disease or healthy subjects with large ventricles. Using the same computer system, the novel algorithm and FSL FIRST processed a single MRI image in less than 10 minutes while FreeSurfer took approximately 7 hours. The testing protocol presented enables the accuracy, reproducibility and sensitivity of different algorithms to be compared. We also demonstrate that the novel segmentation algorithm and FreeSurfer are both effective in determining lateral ventricular volume and are well suited for multicentre and longitudinal MRI studies.

In a large-scale meta-analysis, it has been recently shown that the transcription factor 4 (TCF4) gene is among the most prominent susceptibility genes for schizophrenia. Moreover, transgenic mice overexpressing TCF4 in the brain display a reduction of sensorimotor gating measured by prepulse inhibition (PPI) of the acoustic startle response (ASR). PPI is heritable and has been established as an important translational endophenotype of schizophrenia. We therefore investigated the impact of the schizophrenia susceptibility gene TCF4 (rs9960767) on sensorimotor gating of the ASR in healthy humans and in patients with a schizophrenia spectrum disorder. We assessed PPI, startle reactivity, and habituation of the ASR in two independent samples. The first sample consisted of 107 healthy volunteers from London, UK. The second sample was a schizophrenia spectrum group (n ϭ 113) of 73 schizophrenia patients and 40 individuals at high risk for schizophrenia from Bonn, Germany (total sample n ϭ 220). In both samples, PPI was strongly decreased in carriers of the schizophrenia risk allele C of the TCF4 gene (meta-analysis across both samples: p ϭ 0.00002), whereas startle reactivity and habituation were unaffected by TCF4 genotype. Sensorimotor gating is modulated by TCF4 genotype, indicating an influential role of TCF4 gene variations in the development of early information-processing deficits in schizophrenia.

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