Search for: All records

Pick’s disease (PiD) is a subtype of the tauopathy form of frontotemporal lobar degeneration (FTLD-tau) characterized by intraneuronal 3R-tau inclusions. PiD can underly various dementia syndromes, including primary progressive aphasia (PPA), characterized by an isolated and progressive impairment of language and left-predominant atrophy, and behavioral variant frontotemporal dementia (bvFTD), characterized by progressive dysfunction in personality and bilateral frontotemporal atrophy. In this study, we investigated the neocortical and hippocampal distributions of Pick bodies in bvFTD and PPA to establish clinicopathologic concordance between PiD and the salience of the aphasicversusbehavioral phenotype. Eighteen right-handed cases with PiD as the primary pathologic diagnosis were identified from the Northwestern University Alzheimer’s Disease Research Center brain bank (bvFTD, N = 9; PPA, N = 9). Paraffin-embedded sections were stained immunohistochemically with AT8 to visualize Pick bodies, and unbiased stereological analysis was performed in up to six regions bilaterally [middle frontal gyrus (MFG), superior temporal gyrus (STG), inferior parietal lobule (IPL), anterior temporal lobe (ATL), dentate gyrus (DG) and CA1 of the hippocampus], and unilateral occipital cortex (OCC). In bvFTD, peak neocortical densities of Pick bodies were in the MFG, while the ATL was the most affected in PPA. Both the IPL and STG had greater leftward pathology in PPA, with the latter reaching significance (p < 0.01). In bvFTD, Pick body densities were significantly right-asymmetric in the STG (p < 0.05). Hippocampal burden was not clinicopathologically concordant, as both bvFTD and PPA cases demonstrated significant hippocampal pathology compared to neocortical densities (p < 0.0001). Inclusion-to-neuron analyses in a subset of PPA cases confirmed that neurons in the DG are disproportionately burdened with inclusions compared to neocortical areas. Overall, stereological quantitation suggests that the distribution of neocortical Pick body pathology is concordant with salient clinical features unique to PPA vs. bvFTD while raising intriguing questions about the selective vulnerability of the hippocampus to 3R-tauopathies.

 

The dentate gyrus (DG), a key hippocampal subregion in memory processing, generally resists phosphorylated tau accumulation in the amnestic dementia of the Alzheimer’s type due to Alzheimer’s disease (DAT-AD), but less is known about the susceptibility of the DG to other tauopathies. Here, we report stereologic densities of total DG neurons and tau inclusions in thirty-two brains of human participants with autopsy-confirmed tauopathies with distinct isoform profiles—3R Pick’s disease (PiD, N = 8), 4R corticobasal degeneration (CBD, N = 8), 4R progressive supranuclear palsy (PSP, N = 8), and 3/4R AD (N = 8). All participants were diagnosed during life with primary progressive aphasia (PPA), an aphasic clinical dementia syndrome characterized by progressive deterioration of language abilities with spared non-language cognitive abilities in early stages, except for five patients with DAT-AD as a comparison group. 51% of total participants were female. All specimens were stained immunohistochemically with AT8 to visualize tau pathology, and PPA cases were stained for Nissl substance to visualize neurons. Unbiased stereological analysis was performed in granule and hilar DG cells, and inclusion-to-neuron ratios were calculated. In the PPA group, PiD had highest mean total (granule + hilar) densities of DG tau pathology (p < 0.001), followed by CBD, AD, then PSP. PPA-AD cases showed more inclusions in hilar cells compared to granule cells, while the opposite was true in PiD and CBD. Inclusion-to-neuron ratios revealed, on average, 33% of all DG neurons in PiD cases contained a tau inclusion, compared to ~ 7% in CBD, 2% in AD, and 0.4% in PSP. There was no significant difference between DAT-AD and PPA-AD pathologic tau burden, suggesting that differences in DG burden are not specific to clinical phenotype. We conclude that the DG is differentially vulnerable to pathologic tau accumulation, raising intriguing questions about the structural integrity and functional significance of hippocampal circuits in neurodegenerative dementias.

 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.