Whitwell, Jennifer L.

Background: TAR DNA binding protein 43 (TDP-43) has been shown to be associated with whole hippocampal atrophy in primary age-related tauopathy (PART). It is currently unknown which subregions of the hippocampus are contributing to TDP-43 associated whole hippocampal atrophy in PART. Objective: To identify which specific hippocampal subfield regions are contributing to TDP-43-associated whole hippocampal atrophy in PART. Methods: A total of 115 autopsied cases from the Mayo Clinic Alzheimer Disease Research Center, Neurodegenerative Research Group, and the Mayo Clinic Study of Aging were analyzed. All cases underwent antemortem brain volumetric MRI, neuropathological assessment of the distribution of Aβ (Thal phase), and neurofibrillary tangle (Braak stage) to diagnose PART, as well as assessment of TDP-43 presence/absence in the amygdala, hippocampus and beyond. Hippocampal subfield segmentation was performed using FreeSurfer version 7.4.1. Statistical analyses using logistic regression were performed to assess for associations between TDP-43 and hippocampal subfield volumes, accounting for potential confounders. Results: TDP-43 positive patients ( n = 37, 32%), of which 15/15 were type-α, had significantly smaller whole hippocampal volumes, and smaller volumes of the body and tail of the hippocampus compared to TDP-43 negative patients. Subfield analyses revealed an association between TDP-43 and the molecular layer of hippocampal body and the body of cornu ammonis 1 (CA1), subiculum, and presubiculum regions. There was no association between TDP-43 stage and subfield volumes. Conclusions: Whole hippocampal volume loss linked to TDP-43 in PART is mainly due to volume loss occurring in the molecular layer, CA1, subiculum and presubiculum of the hippocampal body.

AbstractBackground and purposePrimary lateral sclerosis (PLS) is a neurodegenerative disorder that primarily affects the central motor system. In rare cases, clinical features of PLS may overlap with those of progressive supranuclear palsy (PSP). We investigate neuroimaging features that can help distinguish PLS with overlapping features of PSP (PLS‐PSP) from PSP.MethodsSix patients with PLS‐PSP were enrolled between 2019 and 2023. We compared their clinical and neuroimaging characteristics with 18 PSP–Richardson syndrome (PSP‐RS) patients and 20 healthy controls. Magnetic resonance imaging, 18F‐flortaucipir positron emission tomography (PET), quantitative susceptibility mapping, and diffusion tensor imaging tractography (DTI) were performed to evaluate eight brain regions of interest. Area under the receiver operating characteristic curve (AUROC) was calculated.ResultsFive of the six PLS‐PSP patients (83.3%) were male. Median age at symptom onset was 61.5 (52.5–63) years, and all had mixed features of PLS and PSP. Volumes of the pallidum, caudate, midbrain, and cerebellar dentate were smaller in PSP‐RS than PLS‐PSP, providing good discrimination (AUROC = 0.75 for all). The susceptibilities in pallidum, midbrain, and cerebellar dentate were greater in PSP‐RS compared to PLS‐PSP, providing excellent discrimination (AUROC ≥ 0.90 for all). On DTI, fractional anisotropy (FA) in the posterior limb of the internal capsule from the corticospinal tract was lower in PLS‐PSP compared to PSP‐RS (AUROC = 0.86), but FA in the superior cerebellar peduncle was lower in PSP‐RS (AUROC = 0.95). Pallidum flortaucipir PET uptake was greater in PSP‐RS compared to PLS‐PSP (AUROC = 0.74).ConclusionsRegional brain volume, tractography, and magnetic susceptibility, but not tau‐PET, are useful in distinguishing PLS‐PSP from PSP.

Abstract Background Progressive apraxia of speech (PAOS) is characterized by difficulties with motor speech programming and planning. PAOS targets gray matter (GM) and white matter (WM) microstructure that can be assessed using diffusion tensor imaging (DTI) and multishell applications, such as neurite orientation dispersion and density imaging (NODDI). In this study, we aimed to apply DTI and NODDI to add further insight into PAOS tissue microstructure. Methods Twenty‐two PAOS patients and 26 age‐ and sex‐matched controls, recruited by the Neurodegenerative Research Group (NRG) at Mayo Clinic, underwent diffusion MRI on 3T MRI. Brain maps of fractional anisotropy (FA) and mean diffusivity (MD) from DTI and intracellular volume fraction (ICVF) and isotropic volume fraction (IsoVF) from NODDI were generated. Global WM and GM, and specific WM tracts were identified using tractography and lobar GM regions. Results Global WM differences between PAOS and controls were greatest for ICVF, and global GM differences were greatest for MD and IsoVF. Abnormalities in key WM tracts involved in PAOS, including the body of the corpus callosum and frontal aslant tract, were identified with FA, MD, and ICVF, with excellent differentiation of PAOS from controls (area under the receiver operating characteristic curves >.90). MD and ICVF identified abnormalities in arcuate fasciculus, thalamic radiations, and corticostriatal tracts. Significant correlations were identified between an index of articulatory errors and DTI and NODDI metrics from the arcuate fasciculus, frontal aslant tract, and inferior longitudinal fasciculus. Conclusions DTI and NODDI represent different aspects of brain tissue microstructure, increasing the number of potential biomarkers for PAOS.

Background: Microbleeds in the brain have been shown to occur in Alzheimer’s disease (AD), affecting approximately a third of subjects that present with typical dementia of the Alzheimer’s type (DAT). However, little is known about the frequency or distribution of microbleeds in subjects with AD that present with atypical clinical presentations. Objective: To determine whether the frequency and regional distribution of microbleeds in atypical AD differs from that observed in subjects with DAT, and to determine whether microbleeds in atypical AD are associated with age, demographics, or cognitive impairment. Methods: Fifty-five subjects with amyloid-β deposition on Pittsburgh compound B (PiB) PET who presented with predominant language (n = 37) or visuospatial/perceptual (n = 18) deficits underwent T2*weighted MRI. These subjects were compared to 41 PiB-positive subjects with DAT. Microbleeds were identified and assigned a lobar location. Results: The proportion of subjects with microbleeds did not differ between atypical AD (42%) and DAT (32%). However, atypical AD had larger numbers of microbleeds than DAT. In addition, the topographic distribution of microbleeds differed between atypical AD and DAT, with atypical AD showing the highest density of microbleeds in the frontal lobes. Among atypical AD, number of microbleeds was associated with age, but not gender or cognition. Microbleeds were more common in subjects with language (51%) versus visuospatial/perceptual deficits (22%). Conclusions: Microbleeds are relatively common in both DAT and atypical AD, although atypical AD subjects appear to be at particular risk for developing large numbers of microbleeds and for developing microbleeds in the frontal lobe.