title: Neuroanatomy of holoprosencephaly as predictor of function: beyond the face predicting the brain.
abstract: BACKGROUND
  Despite advances in neuroimaging and molecular genetics of holoprosencephaly (HPE), the clinical spectrum of HPE has remained inadequately described.
  
  
  OBJECTIVE
  To better characterize the clinical features of HPE and identify specific neuroanatomic abnormalities that may be useful predictors of neurodevelopmental function.
  
  
  METHODS
  The authors evaluated 68 children with HPE in a multicenter, prospective study. Neuroimaging studies were assessed for the grade of HPE (lobar, semilobar, and alobar), the degree of nonseparation of the deep gray nuclei, and presence of dorsal cyst or cortical malformation.
  
  
  RESULTS
  In general, the severity of clinical problems and neurologic dysfunctions correlated with the degree of hemispheric nonseparation (grade of HPE). Nearly three-quarters of the patients had endocrinopathies, with all having at least diabetes insipidus. The severity of endocrine abnormalities correlated with the degree of hypothalamic nonseparation (p = 0.029). Seizures occurred in approximately half of the children with HPE. The presence of cortical malformations was associated with difficult-to-control seizures. The presence and degree of dystonia correlated with the degree of nonseparation of the caudate and lentiform nuclei and the grade of HPE (p < 0.05). Hypotonia correlated with the grade of HPE (p < 0.05). Mobility, upper extremity function, and language correlated with the degree of nonseparation of the caudate, lentiform and thalamic nuclei, and grade of HPE (p < 0.01).
  
  
  CONCLUSIONS
  Patients with HPE manifest a wide spectrum of clinical problems and neurologic dysfunction. The nature and severity of many of these problems can be predicted by specific neuroanatomic abnormalities found in HPE.
authors.affiliation: Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA.
authors.forename: L L
authors.initials: LL
authors.lastname: Plawner
authors.forename: M R
authors.initials: MR
authors.lastname: Delgado
authors.forename: V S
authors.initials: VS
authors.lastname: Miller
authors.forename: E B
authors.initials: EB
authors.lastname: Levey
authors.forename: S L
authors.initials: SL
authors.lastname: Kinsman
authors.forename: A J
authors.initials: AJ
authors.lastname: Barkovich
authors.forename: E M
authors.initials: EM
authors.lastname: Simon
authors.forename: N J
authors.initials: NJ
authors.lastname: Clegg
authors.forename: V T
authors.initials: VT
authors.lastname: Sweet
authors.forename: E E
authors.initials: EE
authors.lastname: Stashinko
authors.forename: J S
authors.initials: JS
authors.lastname: Hahn
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000473:pathology*; D016009:Chi-Square Distribution; D002648:Child; D002675:Child, Preschool; D005145:Face / Q000473:pathology*; D005260:Female; D005544:Forecasting; D016142:Holoprosencephaly / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D007223:Infant; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D011446:Prospective Studies; D012640:Seizures / Q000473:pathology / Q000503:physiopathology; D018709:Statistics, Nonparametric; D014057:Tomography, X-Ray Computed