MR Imaging of Hippocampal Asymmetry at 3T in a Multiethnic, Population-Based Sample: Results from the Dallas Heart Study

BACKGROUND AND PURPOSE: Asymmetry of the hippocampus is regarded as an important clinical finding, but limited data on hippocampal asymmetry are available for the general population. Here we present hippocampal asymmetry data from the Dallas Heart Study determined by automated methods and its relationship to age, sex, and ethnicity. MATERIALS AND METHODS: 3D magnetization-prepared rapid acquisition of gradient echo MR imaging was performed in 2082 DHS-2 participants. The MR images were analyzed by using 2 standard automated brain-segmentation programs, FSL-FIRST and FreeSurfer. Individuals with imaging errors, self-reported stroke, or major structural abnormalities were excluded. Statistical analyses were performed to determine the significance of the findings across age, sex, and ethnicity. RESULTS: At the 90th percentile, FSL-FIRST demonstrated hippocampal asymmetry of 9.8% (95% CI, 9.3%–10.5%). The 90th percentile of hippocampal asymmetry, measured by the difference in right and left hippocampi volume and the larger hippocampus, was 17.9% (95% CI, 17.0%–19.1%). Hippocampal asymmetry increases with age (P = .0216), men have greater asymmetry than women as shown by FSL-FIRST (P = .0036), but ethnicity is not significantly correlated with asymmetry. To confirm these findings, we used FreeSurfer. FreeSurfer showed asymmetry of 4.4% (95% CI, 4.3%–4.7%) normalized to total volume and 8.5% (95% CI, 8.3%–9.0%) normalized by difference/larger hippocampus. FreeSurfer also showed that hippocampal asymmetry increases with age (P = .0024) and that men had greater asymmetry than women (P = .03). CONCLUSIONS: There is a significant degree of hippocampal asymmetry in the population. The data provided will aid in the research, diagnosis, and treatment of temporal lobe epilepsy and other neurologic disease.

[1]  C R Jack,et al.  Volumetric magnetic resonance imaging. Clinical applications and contributions to the understanding of temporal lobe epilepsy. , 1997, Archives of neurology.

[2]  C R Jack,et al.  Predictors of outcome of anterior temporal lobectomy for intractable epilepsy , 1998, Neurology.

[3]  D. Bowers,et al.  Asymmetry of the hippocampus and amygdala in MRI volumetric measurements of normal adults , 2004, Journal of the International Neuropsychological Society.

[4]  M. Essig,et al.  Hippocampal volume in first episode and recurrent depression , 2009, Psychiatry Research: Neuroimaging.

[5]  Martin Styner,et al.  A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes , 2009, NeuroImage.

[6]  Yong Chen,et al.  Robust principal component analysis and outlier detection with ecological data , 2004 .

[7]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[8]  J K McLaughlin,et al.  Selection of controls in case-control studies. I. Principles. , 1992, American journal of epidemiology.

[9]  E. Beghi,et al.  The epidemiology of epilepsy in Europe – a systematic review , 2005, European journal of neurology.

[10]  Clemens Reimann,et al.  Multivariate outlier detection in exploration geochemistry , 2005, Comput. Geosci..

[11]  Chunshui Yu,et al.  Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta‐analyses of MRI studies , 2009, Hippocampus.

[12]  E. Lesaffre,et al.  A New Nonparametric Approach for Baseline Covariate Adjustment for Two‐Group Comparative Studies , 2008, Biometrics.

[13]  Martin Styner,et al.  Asymmetric bias in user guided segmentations of brain structures , 2012, NeuroImage.

[14]  C. Leonard,et al.  Quantified Volumes of Temporal Lobe Structures in Patients with Epilepsy , 1996, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[15]  Michael I. Miller,et al.  Neuroanatomical asymmetry patterns in individuals with schizophrenia and their non-psychotic siblings , 2009, NeuroImage.

[16]  Christian Vollmar,et al.  Clinical MRI in children and adults with focal epilepsy: A critical review , 2009, Epilepsy & Behavior.

[17]  G. Pell,et al.  Hippocampal volume assessment in temporal lobe epilepsy: How good is automated segmentation? , 2009, Epilepsia.

[18]  L L Kupper,et al.  Selection bias in epidemiologic studies. , 1981, American journal of epidemiology.

[19]  L. Xia,et al.  Volumetric MRI analysis of the amygdala and hippocampus in subjects with major depression , 2004, Journal of Huazhong University of Science and Technology [Medical Sciences].

[20]  A. Dale,et al.  Regional and progressive thinning of the cortical ribbon in Huntington’s disease , 2002, Neurology.

[21]  Debra T. Silverman,et al.  Selection of controls in case-control studies. II. Types of controls. , 1992, American journal of epidemiology.

[22]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[23]  Luis A. Escobar,et al.  Statistical Intervals: A Guide for Practitioners , 1991 .

[24]  J K McLaughlin,et al.  Selection of controls in case-control studies. III. Design options. , 1992, American journal of epidemiology.

[25]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[26]  Ronald M Peshock,et al.  The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. , 2004, The American journal of cardiology.

[27]  R. Worth,et al.  Longitudinal Follow‐Up in 145 Patients with Medically Refractory Temporal Lobe Epilepsy Treated Surgically Between 1984 and 1995 , 1999, Epilepsia.

[28]  S. Eisenschenk,et al.  Hippocampal volumetrics differentiate patients with temporal lobe epilepsy and extratemporal lobe epilepsy. , 1995, Archives of neurology.

[29]  I. Blümcke Neuropathology of focal epilepsies: A critical review , 2009, Epilepsy & Behavior.