Primary progressive aphasia: a model for neurodegenerative disease

Abstract

Purpose of review: Knowledge on primary progressive aphasia (PPA) has expanded rapidly in the past few decades. Clinical characteristics, neuroimaging correlates, and neuropathological features of PPA are better delineated. This facilitates scientific studies on the disease pathophysiology and allows speech and language therapy to be more precisely targeted. This review article begins with a summary of the current understanding of PPA and discusses how PPA can serve as a model to promote scientific discovery in neurodegenerative diseases.

Recent findings: Studies on the different variants of PPA have demonstrated the high compatibility between clinical presentations and neuroimaging features, and in turn, enhances the understanding of speech and language neuroanatomy. In addition to the traditional approach of lesion-based or voxel-based mapping, scientists have also adopted functional connectivity and network topology approaches that permits a more multidimensional understanding of neuroanatomy. As a result, pharmacological and cognitive therapeutic strategies can now be better targeted towards specific pathological/molecular and cognitive subtypes.

Summary: Recent scientific advancement in PPA potentiates it to be an optimal model for studying brain network vulnerability, neurodevelopment influences and the effects of nonpharmacological intervention in neurodegenerative diseases.

FIGURE 1.

Grey matter atrophy patterns in patients with three main primary progressive aphasia variants versus controls. Presented here are statistical parametric maps that depict the grey matter atrophy patterns in semantic variant PPA (svPPA, n = 58), nonfluent/agrammatic variant PPA (nfvPPA, n = 40), and logopenic variant PPA (lvPPA, n = 24) compared with control groups that are matched for age, sex, scanner and sample size. Voxel-based morphometry results thresholded are set at a family-wise error rate of P < 0.001. FWE, familywise error rate; PPA, primary progressive aphasia. Reproduced with permission from [88].

FIGURE 2.

White matter damage in the three main primary progressive aphasia variants versus controls. (a) The average mean diffusivity values for left superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UNC) in all three PPA variants when compared with healthy controls on a standard MNI (Montreal Neurological Institute of McGill University Health Centre) brain template. The asterisk symbol (m) indicates statistical difference from normal controls with P value less than 0.05. The colour bar represents the average mean diffusivity values, ranging from low (violet-blue) to high values (yellow-red). Mean diffusivity is measured in 10⁻³ mm² s⁻¹. (b) The average mean diffusivity values for arcuate fasciculus (AF), frontoangular SLF (SLF-II), frontosupramarginal SLF (SLF-III), and temporoparietal SLF (SLF-tp) in all three PPA variants when compared with healthy controls on a standard MNI (Montreal Neurological Institute of McGill University Health Centre) brain template. The asterisk (m) indicates statistical difference from normal controls with P value less than 0.05. The colour bar represents the average mean diffusivity values, ranging from low (violet-blue) to high values (yellow-red). Mean diffusivity is measured in 10⁻³ mm² s⁻¹. Reproduced with permission from [21].

FIGURE 3.

The Aslant tract within the frontal speech production network. Depicted here is the white matter tract reconstruction of Aslant tract within the frontal speech production network (SPN) in healthy controls using MNI brain template. White matter tracts traveling between pre-SMA and SMA to BA44 is shown in blue and to the ventral premotor cortex (BA6) is highlighted in green. Reproduced with permission from [46].