Morphometric correlates of dysarthric deficit in amyotrophic lateral sclerosis

Matteo De Marco, Antonio Merico, Giulia Berta, Nicoletta Segato, Valentina Citton, Alessandro Baglione, Annalena Venneri

Research output: Contribution to journalArticlepeer-review


Our objective was to investigate the volumetric correlates of speech in amyotrophic lateral sclerosis (ALS). Twenty-three ALS patients had a structural 3D MRI scan, neuropsychological, linguistic and speech assessments. Twenty-three healthy adults of comparable age, education, white-matter hyperintensity load and intracranial volumes were also recruited. Between-group differences in grey matter and white matter (WM) were examined to characterize ALS patients accurately. The association between residual speech and volumetric maps was studied in these patients. Results demonstrated that ALS patients showed a pattern of WM reduction, which was located in peri-cortical motor/premotor fibres bilaterally, and in a large volume extending from the pons/midbrain to the cerebellum. A speech composite score was computed, and this was positively associated with premotor/supplementary-motor WM bilaterally, and right cerebellar WM. Since premotor associations were found in volumes where ALS patients showed WM reduction, this region is believed to be directly involved in speech execution in this group. Since cerebellar associations were instead found in volumes free from shrinkage, this region is interpreted as playing a modulatory role, compensating for the impact of ALS pathology.

Original languageEnglish
Pages (from-to)464-472
Number of pages9
JournalAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Issue number7-8
Publication statusPublished - Nov 27 2015


  • dysarthria
  • Imaging
  • motor neuron disease
  • MRI
  • speech
  • voxel based morphometry

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology


Dive into the research topics of 'Morphometric correlates of dysarthric deficit in amyotrophic lateral sclerosis'. Together they form a unique fingerprint.

Cite this