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Overdiagnosis in the Era of Neuropsychiatric Imaging

New guidelines proposed by the National Institute of Mental Health are intended to transform the management of patients with psychiatric disorders. It is anticipated that neuroimaging and other biomarkers will play a more prominent role in diagnosis and prognosis, especially in the prodromal phase of illness. Earlier treatment of psychiatric disorders has the potential to improve outcomes significantly. However, diagnosis in the absence of symptoms can lead to overdiagnosis. Overdiagnosis is a problem in many fields of medicine but could pose additional problems in psychiatry because of the stigmatization that often accompanies a diagnosis of mental illness. This review discusses the magnetic resonance imaging methods that hold the most promise for evaluating neuropsychiatric disorders, the likelihood that they could lead to overdiagnosis, and opportunities to minimize the impact of overdiagnosis in psychiatric disorders.

New paradigms in neuropsychiatry

In 2013, the American Psychiatric Association published the fifth edition of the Diagnostic and Statistical Manual (DSM-5) used for psychiatric diagnosis. It was generally regarded as an incremental change rather than a radical departure from the previous edition, published nearly twenty years earlier . Shortly thereafter, the National Institute of Mental Health (NIMH) rejected the DSM-5 and embraced a new framework for the study of neuropsychiatric disorders. This framework was based on the premise that genetic, cellular, and behavioral systems are responsible for mental health, and failure of these mechanisms constitutes mental illness . In doing so, the NIMH explicitly acknowledged that neuropsychiatric pathology may precede psychiatric symptoms. In contrast to the traditional approach taken by psychiatrists, the NIMH suggested that neuropsychiatric pathology may be treatable even before a diagnosis could be established with the DSM. Indeed, one of the primary motivations behind these changes was to encourage intervention in the prodromal phase of psychiatric disorders, with the aim of preventing progression . Support for this paradigm came partly from encouraging studies of early intervention in youth at risk of schizophrenia and other neuropsychiatric disorders . To identify patients who might benefit from intervention, the NIMH called for research into physiological biomarkers for psychiatric disease, particularly into neuroimaging biomarkers.

The evolution of neuropsychiatric therapy has mirrored the evolution of our understanding of neuropsychiatric disorders. Although pharmaceutical and behavioral interventions remain a mainstay of treatment, in some cases, clinicians have begun turning to direct stimulation of specific regions of the brain using implanted deep brain electrodes, as well as noninvasive transcranial techniques. Transcranial magnetic stimulation of dorsolateral prefrontal cortex has recently been approved by the Food and Drug Administration for use in patients with treatment-resistant major depressive disorder, with an average response rate of 29% . Long-term response rates of 64%–92% have been reported in depression after deep brain stimulation of subcortical targets . In obsessive–compulsive disorder (OCD), deep brain stimulation of the anterior limb of the internal capsule and/or striatum has been associated with symptom improvement in 60%–70% of treatment-resistant patients . Transcranial magnetic stimulation of dorsolateral prefrontal cortex, orbitofrontal cortex, and the supplemental motor area may also be effective in OCD . These and other regions of the brain are also under investigation as targets for transcranial stimulation in the treatment of neuropathic pain, anxiety, schizophrenia, addiction, and other diseases . As neuroscientists continue to advance their understanding of the relationship between brain structure and function, new targets are likely to emerge.

The era of neuropsychiatric imaging

Neuroimaging stands at the intersection of the evolution of neuropsychiatric diagnosis and the evolution of treatment. Meanwhile, neuroimaging itself is in the midst of a renaissance driven by technological advancements in multiple complementary modalities. Currently, the most popular methods used to investigate neuropsychiatric disorders are functional magnetic resonance imaging, diffusion tensor imaging, and structural morphometry. Functional magnetic resonance imaging is used to localize neural activity on a time scale of seconds, usually by analyzing signal changes in blood related to the oxygenation state of hemoglobin . Diffusion tensor imaging is used to assess the integrity of white matter and anatomy of white matter tracts by tracking the movement of water during diffusion, which has a tendency to occur parallel to axons rather than across them . Structural morphometry is used to measure focal changes in brain volume, even when they are too subtle to detect visually, through the application of sophisticated registration techniques . These advances have produced a wealth of data regarding the imaging correlates of psychiatric disorders.

Schizophrenia, one of the first neuropsychiatric disorders to be studied with modern neuroimaging , has been associated with decreased volumes throughout the brain, particularly in the frontal and temporal lobes . Functional imaging studies of patients with schizophrenia have reported consistent hypoactivation of frontal circuits , some of which may be the result of genetic predisposition . Widespread white matter abnormalities have been reported as well . Neuroimaging studies have also explored the relationship between brain structure and individual disease course in schizophrenia. Hallucinations and other positive symptoms have been associated with distinct neuroimaging features, whereas different findings are associated with negative symptoms .

These same techniques have been applied to patients with other neuropsychiatric disorders. For example, autism has consistently been associated with increased brain volume in children . It has also been associated with abnormal microstructure in the corpus callosum and other regions of the white matter , as well as abnormal frontal and subcortical activity during tasks such as face processing . Functional connectivity, which is a measure of temporal synchronization of neural activity across different regions of the brain, may also be abnormal in autism . Posttraumatic stress disorder has been associated with abnormal activity in the amygdala as well as abnormal hippocampal and amygdala volumes . Depression has been associated with multiple structural and functional abnormalities . In keeping with the conceptual framework proposed by the NIMH, neuroimaging findings in these disorders have been shown to sometimes precede overt psychiatric symptoms .

The challenges of overdiagnosis

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Do all patients need a diagnosis?

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References

  • 1. Berk M.: The DSM-5: hyperbole, hope or hypothesis?. BMC Med 2013; 11: pp. 128.

  • 2. Möller H.-J.: The consequences of DSM-5 for psychiatric diagnosis and psychopharmacotherapy. Int J Psychiatry Clin Pract 2014; 18: pp. 78-85.

  • 3. Insel T.R.: The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am J Psychiatry 2014; 171: pp. 395-397.

  • 4. Insel T.R.: Rethinking schizophrenia. Nature 2010; 468: pp. 187-193.

  • 5. Howlin P., Magiati I., Charman T.: Systematic review of early intensive behavioral interventions for children with autism. Am J Intellect Dev Disabil 2009; 114: pp. 23-41.

  • 6. Mokhtari M., Rajarethinam R.: Early intervention and the treatment of prodrome in schizophrenia: a review of recent developments. J Psychiatr Pract 2013; 19: pp. 375-385.

  • 7. Addington J., Heinssen R.: Prediction and prevention of psychosis in youth at clinical high risk. Annu Rev Clin Psychol 2012; 8: pp. 269-289.

  • 8. Miklowitz D.J., O’Brien M.P., Schlosser D.A., et. al.: Family-focused treatment for adolescents and young adults at high risk for psychosis: results of a randomized trial. J Am Acad Child Adolesc Psychiatry 2014; 53: pp. 848-858.

  • 9. McFarlane W.R., Levin B., Travis L., et. al.: Clinical and functional outcomes after 2 years in the early detection and intervention for the prevention of psychosis multisite effectiveness trial. Schizophr Bull 2015; 41: pp. 30-43.

  • 10. Lefaucheur J.-P., André-Obadia N., Antal A., et. al.: Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Clin Neurophysiol 2014; 125: pp. 2150-2206.

  • 11. Williams N.R., Okun M.S.: Deep brain stimulation (DBS) at the interface of neurology and psychiatry. J Clin Invest 2013; 123: pp. 4546-4556.

  • 12. Bais M., Figee M., Denys D.: Neuromodulation in obsessive-compulsive disorder. Psychiatr Clin North Am 2014; 37: pp. 393-413.

  • 13. Berlim M.T., Neufeld N.H., Van den Eynde F.: Repetitive transcranial magnetic stimulation (rTMS) for obsessive-compulsive disorder (OCD): an exploratory meta-analysis of randomized and sham-controlled trials. J Psychiatr Res 2013; 47: pp. 999-1006.

  • 14. Ogawa S.: Finding the BOLD effect in brain images. NeuroImage 2012; 62: pp. 608-609.

  • 15. Le Bihan D., Johansen-Berg H.: Diffusion MRI at 25: exploring brain tissue structure and function. NeuroImage 2012; 61: pp. 324-341.

  • 16. Perlini C., Bellani M., Brambilla P.: Structural imaging techniques in schizophrenia. Acta Psychiatr Scand 2012; 126: pp. 235-242.

  • 17. Johnstone E.C., Crow T.J., Frith C.D., et. al.: Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet 1976; 2: pp. 924-926.

  • 18. Levitt J.J., Bobrow L., Lucia D., et. al.: A selective review of volumetric and morphometric imaging in schizophrenia. Curr Top Behav Neurosci 2010; 4: pp. 243-281.

  • 19. Honea R., Crow T.J., Passingham D., et. al.: Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 2005; 162: pp. 2233-2245.

  • 20. Shepherd A.M., Laurens K.R., Matheson S.L., et. al.: Systematic meta-review and quality assessment of the structural brain alterations in schizophrenia. Neurosci Biobehav Rev 2012; 36: pp. 1342-1356.

  • 21. Haijma S.V., Van Haren N., Cahn W., et. al.: Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull 2013; 39: pp. 1129-1138.

  • 22. Minzenberg M.J., Laird A.R., Thelen S., et. al.: Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 2009; 66: pp. 811-822.

  • 23. MacDonald A.W., Thermenos H.W., Barch D.M., et. al.: Imaging genetic liability to schizophrenia: systematic review of FMRI studies of patients’ nonpsychotic relatives. Schizophr Bull 2009; 35: pp. 1142-1162.

  • 24. Fitzsimmons J., Kubicki M., Shenton M.E.: Review of functional and anatomical brain connectivity findings in schizophrenia. Curr Opin Psychiatry 2013; 26: pp. 172-187.

  • 25. Zhang T., Koutsouleris N., Meisenzahl E., et. al.: Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis. Schizophr Bull 2014; 41: pp. 74-84.

  • 26. Schroder J., Buchsbaum M.S., Siegel B.V., et. al.: Cerebral metabolic activity correlates of subsyndromes in chronic schizophrenia. Schizophr Res 1996; 19: pp. 41-53.

  • 27. Padmanabhan J.L., Tandon N., Haller C.S., et. al.: Correlations between brain structure and symptom dimensions of psychosis in schizophrenia, schizoaffective, and psychotic bipolar I disorders. Schizophr Bull 2014; 41: pp. 154-162.

  • 28. Stigler K.A., McDonald B.C., Anand A., et. al.: Structural and functional magnetic resonance imaging of autism spectrum disorders. Brain Res 2011; 1380: pp. 146-161.

  • 29. Wolff J.J., Gu H., Gerig G., et. al.: Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 2012; 169: pp. 589-600.

  • 30. Kleinhans N.M., Richards T., Johnson L.C., et. al.: fMRI evidence of neural abnormalities in the subcortical face processing system in ASD. NeuroImage 2011; 54: pp. 697-704.

  • 31. Aoki Y., Cortese S., Tansella M.: Neural bases of atypical emotional face processing in autism: a meta-analysis of fMRI studies. World J Biol Psychiatry 2014; pp. 1-10.

  • 32. Just M.A., Cherkassky V.L., Keller T.A., et. al.: Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain J Neurol 2004; 127: pp. 1811-1821.

  • 33. Villalobos M.E., Mizuno A., Dahl B.C., et. al.: Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. NeuroImage 2005; 25: pp. 916-925.

  • 34. Welchew D.E., Ashwin C., Berkouk K., et. al.: Functional disconnectivity of the medial temporal lobe in Asperger’s syndrome. Biol Psychiatry 2005; 57: pp. 991-998.

  • 35. Admon R., Milad M.R., Hendler T.: A causal model of post-traumatic stress disorder: disentangling predisposed from acquired neural abnormalities. Trends Cogn Sci 2013; 17: pp. 337-347.

  • 36. Ruocco A.C., Amirthavasagam S., Zakzanis K.K.: Amygdala and hippocampal volume reductions as candidate endophenotypes for borderline personality disorder: a meta-analysis of magnetic resonance imaging studies. Psychiatry Res 2012; 201: pp. 245-252.

  • 37. Bremner J.D.: Neuroimaging in posttraumatic stress disorder and other stress-related disorders. Neuroimaging Clin N Am 2007; 17: pp. 523-538. ix

  • 38. Zhang W.-N., Chang S.-H., Guo L.-Y., et. al.: The neural correlates of reward-related processing in major depressive disorder: a meta-analysis of functional magnetic resonance imaging studies. J Affect Disord 2013; 151: pp. 531-539.

  • 39. Whalley H.C., Sussmann J.E., Romaniuk L., et. al.: Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging. PloS One 2013; 8: pp. e57357.

  • 40. McIntosh A.M., Owens D.C., Moorhead W.J., et. al.: Longitudinal volume reductions in people at high genetic risk of schizophrenia as they develop psychosis. Biol Psychiatry 2011; 69: pp. 953-958.

  • 41. Job D.E., Whalley H.C., Johnstone E.C., et. al.: Grey matter changes over time in high risk subjects developing schizophrenia. NeuroImage 2005; 25: pp. 1023-1030.

  • 42. Bolton D.: Overdiagnosis problems in the DSM-IV and the new DSM-5: can they be resolved by the distress-impairment criterion?. Can J Psychiatry 2013; 58: pp. 612-617.

  • 43. Welch H.G., Schwartz L., Woloshin S.: Overdiagnosed: Making People Sick in the Pursuit of Health.1st ed.2012.Beacon PressBoston, Mass

  • 44. Conner K.O., Copeland V.C., Grote N.K., et. al.: Mental health treatment seeking among older adults with depression: the impact of stigma and race. Am J Geriatr Psychiatry 2010; 18: pp. 531-543.

  • 45. Poldrack R.A.: Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron 2011; 72: pp. 692-697.

  • 46. Piras F., Piras F., Chiapponi C., et. al.: Widespread structural brain changes in OCD: a systematic review of voxel-based morphometry studies. Cortex 2015; 62: pp. 89-108.

  • 47. Salgado-Pineda P., Landin-Romero R., Fakra E., et. al.: Structural abnormalities in schizophrenia: further evidence on the key role of the anterior cingulate cortex. Neuropsychobiology 2014; 69: pp. 52-58.

  • 48. Kühn S., Gallinat J.: Gray matter correlates of posttraumatic stress disorder: a quantitative meta-analysis. Biol Psychiatry 2013; 73: pp. 70-74.

  • 49. Lai C.-H.: Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies. Psychiatry Res 2013; 211: pp. 37-46.

  • 50. Loh K.K., Kanai R.: Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex. PloS One 2014; 9: pp. e106698.

  • 51. Freund W., Faust S., Gaser C., et. al.: Regionally accentuated reversible brain grey matter reduction in ultra marathon runners detected by voxel-based morphometry. BMC Sports Sci Med Rehabil 2014; 6: pp. 4.

  • 52. Hutzler F.: Reverse inference is not a fallacy per se: cognitive processes can be inferred from functional imaging data. NeuroImage 2014; 84: pp. 1061-1069.

  • 53. Phelps J., Ghaemi S.N.: The mistaken claim of bipolar “overdiagnosis”: solving the false positives problem for DSM-5/ICD-11. Acta Psychiatr Scand 2012; 126: pp. 395-401.

  • 54. Sabuncu M.R., Konukoglu E., for the Alzheimer’s Disease Neuroimaging Initiative: Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 2015; 13: pp. 31-46.

  • 55. Zarogianni E., Moorhead T.W.J., Lawrie S.M.: Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. NeuroImage Clin 2013; 3: pp. 279-289.

  • 56. Nieuwenhuis M., van Haren N.E., Hulshoff Pol H.E., et. al.: Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage 2012; 61: pp. 606-612.

  • 57. Iwabuchi S.J., Liddle P.F., Palaniyappan L.: Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging. Front Psychiatry 2013; 4: pp. 95.

  • 58. Zanetti M.V., Schaufelberger M.S., Doshi J., et. al.: Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2013; 43: pp. 116-125.

  • 59. Bansal R., Staib L.H., Laine A.F., et. al.: Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses. PloS One 2012; 7: pp. e50698.

  • 60. Sato J.R., de Araujo Filho G.M., de Araujo T.B., et. al.: Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning. J Psychiatr Res 2012; 46: pp. 1126-1132.

  • 61. Hart H., Marquand A.F., Smith A., et. al.: Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing. J Am Acad Child Adolesc Psychiatry 2014; 53: pp. 569-578.e1.

  • 62. Wang X., Jiao Y., Tang T., et. al.: Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder. Eur J Radiol 2013; 82: pp. 1552-1557.

  • 63. Dey S., Rao A.R., Shah M.: Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects. Front Neural Circuits 2014; 8: pp. 64.

  • 64. Nielsen J.A., Zielinski B.A., Fletcher P.T., et. al.: Multisite functional connectivity MRI classification of autism: ABIDE results. Front Hum Neurosci 2013; 7: pp. 599.

  • 65. Anderson J.S., Nielsen J.A., Froehlich A.L., et. al.: Functional connectivity magnetic resonance imaging classification of autism. Brain J Neurol 2011; 134: pp. 3742-3754.

  • 66. Iidaka T.: Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 2014; 63C: pp. 55-67.

  • 67. Uddin L.Q., Supekar K., Lynch C.J., et. al.: Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 2013; 70: pp. 869-879.

  • 68. Deshpande G., Libero L.E., Sreenivasan K.R., et. al.: Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci 2013; 7: pp. 670.

  • 69. Lange N., Dubray M.B., Lee J.E., et. al.: Atypical diffusion tensor hemispheric asymmetry in autism. Autism Res 2010; 3: pp. 350-358.

  • 70. Fang P., Zeng L.-L., Shen H., et. al.: Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PloS One 2012; 7: pp. e45972.

  • 71. Karageorgiou E., Schulz S.C., Gollub R.L., et. al.: Neuropsychological testing and structural magnetic resonance imaging as diagnostic biomarkers early in the course of schizophrenia and related psychoses. Neuroinformatics 2011; 9: pp. 321-333.

  • 72. Korgaonkar M.S., Williams L.M., Song Y.J., et. al.: Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder. Br J Psychiatry J Ment Sci 2014; 205: pp. 321-328.

  • 73. Undurraga J., Baldessarini R.J.: Randomized, placebo-controlled trials of antidepressants for acute major depression: thirty-year meta-analytic review. Neuropsychopharmacology 2012; 37: pp. 851-864.

  • 74. Koen N., Stein D.J.: Pharmacotherapy of anxiety disorders: a critical review. Dialogues Clin Neurosci 2011; 13: pp. 423-437.

  • 75. van Waarde J.A., Scholte H.S., van Oudheusden L.J.B., et. al.: A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 2014 Aug 5;

  • 76. Reis Marques T., Taylor H., Chaddock C., et. al.: White matter integrity as a predictor of response to treatment in first episode psychosis. Brain J Neurol 2014; 137: pp. 172-182.

  • 77. Mitelman S.A., Newmark R.E., Torosjan Y., et. al.: White matter fractional anisotropy and outcome in schizophrenia. Schizophr Res 2006; 87: pp. 138-159.

  • 78. Mitelman S.A., Canfield E.L., Newmark R.E., et. al.: Longitudinal assessment of gray and white matter in chronic schizophrenia: a combined diffusion-tensor and structural magnetic resonance imaging study. Open Neuroimaging J 2009; 3: pp. 31-47.

  • 79. Luck D., Buchy L., Czechowska Y., et. al.: Fronto-temporal disconnectivity and clinical short-term outcome in first episode psychosis: a DTI-tractography study. J Psychiatr Res 2011; 45: pp. 369-377.

  • 80. Fung G., Cheung C., Chen E., et. al.: MRI predicts remission at 1 year in first-episode schizophrenia in females with larger striato-thalamic volumes. Neuropsychobiology 2014; 69: pp. 243-248.

  • 81. Szeszko P.R., Narr K.L., Phillips O.R., et. al.: Magnetic resonance imaging predictors of treatment response in first-episode schizophrenia. Schizophr Bull 2012; 38: pp. 569-578.

  • 82. Ardekani B.A., Tabesh A., Sevy S., et. al.: Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers. Hum Brain Mapp 2011; 32: pp. 1-9.

  • 83. Bryant R.A., Felmingham K., Whitford T.J., et. al.: Rostral anterior cingulate volume predicts treatment response to cognitive-behavioural therapy for posttraumatic stress disorder. J Psychiatry Neurosci 2008; 33: pp. 142-146.

  • 84. Bryant R.A., Felmingham K., Kemp A., et. al.: Amygdala and ventral anterior cingulate activation predicts treatment response to cognitive behaviour therapy for post-traumatic stress disorder. Psychol Med 2008; 38: pp. 555-561.

  • 85. Collin J.: Universal cures for idiosyncratic illnesses: a genealogy of therapeutic reasoning in the mental health field. Health (London) 2014 Aug 18; pii: 1363459314545695.

  • 86. Bruchmüller K., Margraf J., Schneider S.: Is ADHD diagnosed in accord with diagnostic criteria? Overdiagnosis and influence of client gender on diagnosis. J Consult Clin Psychol 2012; 80: pp. 128-138.

  • 87. Ghouse A.A., Sanches M., Zunta-Soares G., et. al.: Overdiagnosis of bipolar disorder: a critical analysis of the literature. ScientificWorldJournal 2013; 2013: pp. 297087.

  • 88. Phillips M.L., Kupfer D.J.: Bipolar disorder diagnosis: challenges and future directions. Lancet 2013; 381: pp. 1663-1671.

  • 89. Green R.C., Roberts J.S., Cupples L.A., et. al.: Disclosure of APOE genotype for risk of Alzheimer’s disease. N Engl J Med 2009; 361: pp. 245-254.

  • 90. Ashida S., Koehly L.M., Roberts J.S., et. al.: The role of disease perceptions and results sharing in psychological adaptation after genetic susceptibility testing: the REVEAL study. Eur J Hum Genet 2010; 18: pp. 1296-1301.

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