SuperAging functional connectomics from resting-state functional MRI.

Understanding the relationship between functional connectivity (FC) of higher-order neurocognitive networks and age-related cognitive decline is a complex and evolving field of research. Decreases in FC have been associated with cognitive decline in persons with Alzheimer's disease and related dementias (ADRD). However, the contributions of FC have been less straightforward in typical cognitive aging. Some investigations suggest relatively robust FC within neurocognitive networks differentiates unusually successful cognitive aging from average aging, while others do not. Methodologic limitations in data processing and varying definitions of 'successful aging' may have contributed to the inconsistent results to date. The current study seeks to address previous limitations by optimized MRI methods to examine FC in the well-established SuperAging phenotype, defined by age and cognitive performance as individuals 80 and older with episodic memory performance equal to or better than 50-to-60-year-olds. Within- and between-network FC of large-scale neurocognitive networks were compared between 24 SuperAgers and 16 cognitively average older-aged control (OACs) with stable cognitive profiles using resting-state functional MRI (rs-fMRI) from a single visit. Group classification was determined based on measures of episodic memory, executive functioning, verbal fluency and picture naming. Inclusion criteria required stable cognitive status across two visits. First, we investigated the FC within and between seven resting-state networks from a common atlas parcellation. A separate index of network segregation was also compared between groups. Second, we investigated the FC between six subcomponents of the default mode network (DMN), the neurocognitive network commonly associated with memory performance and disrupted in persons with ADRD. For each analysis, FCs were compared across groups using two-sample independent t-tests and corrected for multiple comparisons. There were no significant between-group differences in demographic characteristics including age, sex and education. At the group-level, within-network FC, between-network FC, and segregation measurements of seven large-scale networks, including subcomponents of the DMN, were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Thus, FC within or between large-scale networks does not appear to be a primary driver of the exceptional memory performance observed in SuperAgers. These results have relevance for differentiating the role of FC changes associated with cognitive aging from those associated with ADRD.

Successful cognitive aging is associated with thicker anterior cingulate cortex and lower tau deposition compared to typical aging.

INTRODUCTION: There is no consensus on either the definition of successful cognitive aging (SA) or the underlying neural mechanisms. METHODS: We examined the agreement between new and existing definitions using: (1) a novel measure, the cognitive age gap (SA-CAG, cognitive-predicted age minus chronological age), (2) composite scores for episodic memory (SA-EM), (3) non-memory cognition (SA-NM), and (4) the California Verbal Learning Test (SA-CVLT). RESULTS: Fair to moderate strength of agreement was found between the four definitions. Most SA groups showed greater cortical thickness compared to typical aging (TA), especially in the anterior cingulate and midcingulate cortices and medial temporal lobes. Greater hippocampal volume was found in all SA groups except SA-NM. Lower entorhinal 18 F-Flortaucipir (FTP) uptake was found in all SA groups. DISCUSSION: These findings suggest that a feature of SA, regardless of its exact definition, is resistance to tau pathology and preserved cortical integrity, especially in the anterior cingulate and midcingulate cortices. HIGHLIGHTS: Different approaches have been used to define successful cognitive aging (SA). Regardless of definition, different SA groups have similar brain features. SA individuals have greater anterior cingulate thickness and hippocampal volume. Lower entorhinal tau deposition, but not amyloid beta is related to SA. A combination of cortical integrity and resistance to tau may be features of SA.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging.

Background: Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods: MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings: Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion: Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Focal amyloid and asymmetric tau in an imaging-to-autopsy case of clinical primary progressive aphasia with Alzheimer disease neuropathology.

Quantification of in vivo amyloid and tau PET imaging relationships with postmortem measurements are critical for validating the sensitivity and specificity imaging biomarkers across clinical phenotypes with Alzheimer disease neuropathologic change (ADNC). This study examined the quantitative relationship between regional binding of in vivo 18F-florbetapir amyloid PET and 18F-flortaucipir tau PET with postmortem stereological counts of amyloid plaques and neurofibrillary tangles (NFT) in a case of primary progressive aphasia (PPA) with ADNC, where neurodegeneration asymmetrically targets the left hemisphere. Beginning 2 years prior to death, a 63-year-old right-handed man presenting with agrammatic variant PPA underwent a florbetapir and flortaucpir PET scan, and neuropsychological assessments and magnetic resonance imaging sessions every 6 months. Florbetapir and flortaucpir PET standard uptake value ratios (SUVRs) were quantified from 8 left and right hemisphere brain regions with stereological quantification of amyloid plaques and NFTs from corresponding postmortem sections. Pearson's correlations and measures of asymmetry were used to examine relationships between imaging and autopsy measurements. The three visits prior to death revealed decline of language measures, with marked progression of atrophy. Florbetapir PET presented with an atypical focal pattern of uptake and showed a significant positive correlation with postmortem amyloid plaque density across the 8 regions (r = 0.92; p = 0.001). Flortaucipir PET had a left-lateralized distribution and showed a significant positive correlation with NFT density (r = 0.78; p = 0.023). Flortaucipir PET and NFT density indicated a medial temporal lobe sparing presentation of ADNC, demonstrating that AD does not always target the medial temporal lobe. This study adds additional evidence, in a non-amnestic phenotype of ADNC, that there is a strong correlation between AD PET biomarkers, florbetapir and flortaucipir, with quantitative neuropathology. The atypical and focal presentation of plaque density and florbetapir PET uptake suggests not all amyloid pathology presents as diffuse across neocortex.

Relationships among tau burden, atrophy, age, and naming in the aphasic variant of Alzheimer's disease.

INTRODUCTION: Examination of pathologic, anatomic, and cognitive relationships has been limited in primary progressive aphasia (PPA) with underlying Alzheimer's disease (AD) neuropathology. METHODS: Spatial relationships between tau positron emission tomography (PET), cortical thickness, age, and naming on the Boston Naming Test (BNT) in PPA with biomarker evidence of AD (PPA-AD) were examined. RESULTS: Higher tau PET burden was associated with atrophy and younger age. There was a significant left-lateralized relationship between lower BNT and more atrophy, and between lower BNT and increased tau burden. Variance in naming was primarily shared between tau and atrophy (51%), but naming was uniquely explained more by atrophy (32%) than tau (16%). Higher left anterior temporal tau burden was associated with greater 1-year rate of decline in naming. DISCUSSION: PPA-AD has a similar relationship between abnormal biomarkers as first described in amnestic AD, with differing spatial extent, reflecting the left-lateralized nature of the language network.

Modularity and granularity across the language network-A primary progressive aphasia perspective.

Tests of grammar, repetition and semantics were administered to 62 prospectively enrolled right-handed participants with primary progressive aphasia (PPA). Structural brain images were obtained at the time of testing. Regression analyses uncovered 3 clearly delineated non-overlapping left hemisphere clusters where cortical thinning (atrophy) was significantly correlated with impaired performance. A morphosyntactic cluster associated with the grammaticality of sentence construction was located predominantly within the middle and inferior frontal gyri; a phonolexical cluster associated with language repetition was located in the temporoparietal junction; a lexicosemantic cluster associated with object naming and single word comprehension was located within the middle and anterior parts of the temporal lobe and extended into insular, orbitofrontal, and mediotemporal cortices. Commonality analyses were undertaken to explore whether these three clusters were as modular as indicated by the regression analyses or whether some underlying functional granularity could be uncovered. Modularity was defined as the exclusive association of an anatomical cluster with a single type of language task whereas granularity was defined as the association of a single anatomical cluster with more than one type of language task. The commonality analyses revealed a predominantly modular organization with quantitatively minor instances of inter-cluster granularity. The results also reconfirmed previous work on PPA which had shown that Wernicke's area is not essential for word comprehension, that naming impairments can be based either on deficits of lexical retrieval or word comprehension, and that the essential substrates of word comprehension encompass much wider areas of the temporal lobe than the temporal pole. The anatomy of the language network has traditionally been explored through patients with focal cerebrovascular accidents and experiments based on functional activation. Investigations on PPA are showing that focal neurodegenerations can add new perspectives to existing models of the language network.

Functional decline in the aphasic variant of Alzheimer's disease.

INTRODUCTION: Primary progressive aphasia (PPA) is a clinical dementia syndrome associated with frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD). Impairment in activities of daily living is essential for dementia diagnosis, yet less is known about the neuropathologic impact on functional decline in PPA, especially over time. METHODS: Activities of Daily Living Questionnaire (ADLQ) ratings were compared by suspected underlying pathology between 17 PPAAß+ and 11 PPAAß- participants at 6-month intervals for 2 years using a linear mixed-effects model. A general linear model examined associations between functional decline and cortical thickness at baseline. RESULTS: Groups did not differ in demographics or aphasia severity at baseline, yet overall and subdomain scores of the ADLQ were significantly worse for PPAAß+ compared to PPAAß- (P = .015) at each interval across 18 months. DISCUSSION: Functional decline appears more pronounced and disrupts more aspects of life activities for individuals with non-semantic PPA with suspected AD versus non-AD neuropathology.

Accumulation of neurofibrillary tangles and activated microglia is associated with lower neuron densities in the aphasic variant of Alzheimer's disease.

The neurofibrillary tangles (NFT) and amyloid-ß plaques (AP) that comprise Alzheimer's disease (AD) neuropathology are associated with neurodegeneration and microglial activation. Activated microglia exist on a dynamic spectrum of morphologic subtypes that include resting, surveillant microglia capable of converting to activated, hypertrophic microglia closely linked to neuroinflammatory processes and AD neuropathology in amnestic AD. However, quantitative analyses of microglial subtypes and neurons are lacking in non-amnestic clinical AD variants, including primary progressive aphasia (PPA-AD). PPA-AD is a language disorder characterized by cortical atrophy and NFT densities concentrated to the language-dominant hemisphere. Here, a stereologic investigation of five PPA-AD participants determined the densities and distributions of neurons and microglial subtypes to examine how cellular changes relate to AD neuropathology and may contribute to cortical atrophy. Adjacent series of sections were immunostained for neurons (NeuN) and microglia (HLA-DR) from bilateral language and non-language regions where in vivo cortical atrophy and Thioflavin-S-positive APs and NFTs were previously quantified. NeuN-positive neurons and morphologic subtypes of HLA-DR-positive microglia (i.e., resting [ramified] microglia and activated [hypertrophic] microglia) were quantified using unbiased stereology. Relationships between neurons, microglia, AD neuropathology, and cortical atrophy were determined using linear mixed models. NFT densities were positively associated with hypertrophic microglia densities (P < 0.01) and inversely related to neuron densities (P = 0.01). Hypertrophic microglia densities were inversely related to densities of neurons (P < 0.01) and ramified microglia (P < 0.01). Ramified microglia densities were positively associated with neuron densities (P = 0.02) and inversely related to cortical atrophy (P = 0.03). Our findings provide converging evidence of divergent roles for microglial subtypes in patterns of neurodegeneration, which includes hypertrophic microglia likely driving a neuroinflammatory response more sensitive to NFTs than APs in PPA-AD. Moreover, the accumulation of both NFTs and activated hypertrophic microglia in association with low neuron densities suggest they may collectively contribute to focal neurodegeneration characteristic of PPA-AD.

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.

Differential neurocognitive network perturbation in amnestic and aphasic Alzheimer disease.

OBJECTIVE: To determine if Alzheimer disease (AD) is associated with aphasic rather than amnestic dementias in certain circumstances related in part to perturbations in different networks. METHODS: Three groups were investigated: 14 participants suspected of having the neuropathology of AD based on clinically diagnosed amnestic dementia of the Alzheimer type (DAT), 26 individuals with primary progressive aphasia (PPA) with either a positive 18F-florbetapir amyloid PET scan or confirmed AD at autopsy, and 26 neurologically intact controls. The groups were compared using rs-fMRI. Seeds included the left hemisphere inferior frontal gyrus (IFG) for the language network, the left hippocampus for the episodic memory network, and the left posterior cingulate for the default mode network (DMN). RESULTS: Greater connectivity perturbations were found from the hippocampus for the DAT group and from the IFG for the PPA group. Furthermore, connectivity alterations in the PPA group were more asymmetric and favored the language-dominant left hemisphere. Loss of connectivity from the DMN seed was of a similar magnitude in the PPA and DAT groups. CONCLUSIONS: Despite the presumptive common underlying neuropathology of amyloid plaques and neurofibrillary tangles, the 2 groups displayed 2 different patterns of network perturbation, each concordant with the clinical presentation and the anatomy of neurodegeneration.

Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks.

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

Clinical and cortical decline in the aphasic variant of Alzheimer's disease.

INTRODUCTION: Primary progressive aphasia (PPA) displays variable progression trajectories that require further elucidation. METHODS: Longitudinal quantitation of atrophy and language over 12 months was completed for PPA patients with and without positive amyloid PET (PPAAß+ and PPAAß-), an imaging biomarker of underlying Alzheimer's disease. RESULTS: Over 12 months, both PPA groups showed significantly greater cortical atrophy rates in the left versus right hemisphere, with a more widespread pattern in PPAAß+. The PPAAß+ group also showed greater decline in performance on most language tasks. There was no obligatory relationship between the logopenic PPA variant and amyloid status. Effect sizes from quantitative MRI data were more robust than neuropsychological metrics. DISCUSSION: Preferential language network neurodegeneration is present in PPA irrespective of amyloid status. Clinical and anatomical progression appears to differ for PPA due to Alzheimer's disease versus non-Alzheimer's disease neuropathology, a distinction that may help to inform prognosis and the design of intervention trials.

Aphasic variant of Alzheimer disease: Clinical, anatomic, and genetic features.

OBJECTIVE: To identify features of primary progressive aphasia (PPA) associated with Alzheimer disease (AD) neuropathology. A related objective was to determine whether logopenic PPA is a clinical marker for AD. METHODS: A total of 139 prospectively enrolled participants with a root diagnosis of PPA constituted the reference set. Those with autopsy or biomarker evidence of AD, and who had been evaluated at mild disease stages (Aphasia Quotient ≥85), were included (n = 19). All had quantitative language testing and APOE genotyping. Fifteen had MRI morphometry. RESULTS: Impaired word-finding was the universal presenting complaint in the aphasic AD group. PPA clinical subtype was logopenic (n = 13) and agrammatic (n = 6). Fluency, repetition, naming, and grammaticality ranged from preserved to severely impaired. All had relative preservation of word comprehension. Eight of the 15 aphasic participants with AD showed no appreciable cortical atrophy at the individual level on MRI. As a group, atrophy was asymmetrically concentrated in the left perisylvian cortex. APOE ε4 frequency was not elevated. CONCLUSIONS: There is a close, but not obligatory, association between logopenic PPA and AD. No language measure, with the possible exception of word comprehension, can confirm or exclude AD in PPA. Biomarkers are therefore essential for diagnosis. Asymmetry of cortical atrophy and normal APOE ε4 prevalence constitute deviations from typical AD. These and additional neuropathologic features suggest that AD has biological subtypes, one of which causes PPA. Better appreciation of this fact should promote the inclusion of individuals with PPA and positive AD biomarkers into relevant clinical trials.

Frontotemporal networks and behavioral symptoms in primary progressive aphasia.

OBJECTIVE: To determine if behavioral symptoms in patients with primary progressive aphasia (PPA) were associated with degeneration of a ventral frontotemporal network. METHODS: We used diffusion tensor imaging tractography to quantify abnormalities of the uncinate fasciculus that connects the anterior temporal lobe and the ventrolateral frontal cortex. Two additional ventral tracts were studied: the inferior fronto-occipital fasciculus and the inferior longitudinal fasciculus. We also measured cortical thickness of anterior temporal and orbitofrontal regions interconnected by these tracts. Thirty-three patients with PPA and 26 healthy controls were recruited. RESULTS: In keeping with the PPA diagnosis, behavioral symptoms were distinctly less prominent than the language deficits. Although all 3 tracts had structural pathology as determined by tractography, significant correlations with scores on the Frontal Behavioral Inventory were found only for the uncinate fasciculus. Cortical atrophy of the orbitofrontal and anterior temporal lobe cortex was also correlated with these scores. CONCLUSIONS: Our findings indicate that damage to a frontotemporal network mediated by the uncinate fasciculus may underlie the emergence of behavioral symptoms in patients with PPA.

Is in vivo amyloid distribution asymmetric in primary progressive aphasia?

We aimed to determine whether (18) F-florbetapir amyloid positron emission tomography imaging shows a clinically concordant, left-hemisphere-dominant pattern of deposition in primary progressive aphasia (PPA). Elevated cortical amyloid (Aß(+) ) was found in 19 of 32 PPA patients. Hemispheric laterality of amyloid burden was compared between Aß(+) PPA and an Aß(+) amnestic dementia groups (n = 22). The parietal region showed significantly greater left lateralized amyloid uptake in the PPA group than the amnestic group (p < 0.007), consistent with the left lateralized pattern of neurodegeneration in PPA. These results suggest that the cortical distribution of amyloid may have a greater clinical concordance than previously reported.

Proof of concept demonstration of optimal composite MRI endpoints for clinical trials.

BACKGROUND: Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with traditional cognitive and functional outcome measures. METHODS: We investigated a composite atrophy index in 26 PPA participants with longitudinal MRIs separated by two years. Rogalski et al. [Neurology 2014;83:1184-1191] previously demonstrated that atrophy of the left perisylvian temporal cortex (PSTC) is a highly sensitive measure of disease progression in this population and a promising endpoint for clinical trials. Using methods described by Ard et al. [Pharmaceutical Statistics 2015;14:418-426], we constructed a composite atrophy index composed of a weighted sum of volumetric measures of 10 regions of interest within the left perisylvian cortex using weights that maximize signal-to-noise and minimize sample size required of trials using the resulting score. Sample size required to detect a fixed percentage slowing in atrophy in a two-year clinical trial with equal allocation of subjects across arms and 90% power was calculated for the PSTC and optimal composite surrogate biomarker endpoints. RESULTS: The optimal composite endpoint required 38% fewer subjects to detect the same percent slowing in atrophy than required by the left PSTC endpoint. CONCLUSIONS: Optimal composites can increase the power of clinical trials and increase the probability that smaller trials are informative, an observation especially relevant for PPA, but also for related neurodegenerative disorders including Alzheimer's disease.

Morphometric and histologic substrates of cingulate integrity in elders with exceptional memory capacity.

This human study is based on an established cohort of "SuperAgers," 80+-year-old individuals with episodic memory function at a level equal to, or better than, individuals 20-30 years younger. A preliminary investigation using structural brain imaging revealed a region of anterior cingulate cortex that was thicker in SuperAgers compared with healthy 50- to 65-year-olds. Here, we investigated the in vivo structural features of cingulate cortex in a larger sample of SuperAgers and conducted a histologic analysis of this region in postmortem specimens. A region-of-interest MRI structural analysis found cingulate cortex to be thinner in cognitively average 80+ year olds (n = 21) than in the healthy middle-aged group (n = 18). A region of the anterior cingulate cortex in the right hemisphere displayed greater thickness in SuperAgers (n = 31) compared with cognitively average 80+ year olds and also to the much younger healthy 50-60 year olds (p < 0.01). Postmortem investigations were conducted in the cingulate cortex in five SuperAgers, five cognitively average elderly individuals, and five individuals with amnestic mild cognitive impairment. Compared with other subject groups, SuperAgers showed a lower frequency of Alzheimer-type neurofibrillary tangles (p < 0.05). There were no differences in total neuronal size or count between subject groups. Interestingly, relative to total neuronal packing density, there was a higher density of von Economo neurons (p < 0.05), particularly in anterior cingulate regions of SuperAgers. These findings suggest that reduced vulnerability to the age-related emergence of Alzheimer pathology and higher von Economo neuron density in anterior cingulate cortex may represent biological correlates of high memory capacity in advanced old age.

Asymmetry of cortical decline in subtypes of primary progressive aphasia.

OBJECTIVE: The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. METHODS: Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. RESULTS: Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. CONCLUSIONS: Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA.

Naming vs knowing faces in primary progressive aphasia: a tale of 2 hemispheres.

OBJECTIVES: This study examines the anatomical correlates of naming vs recognizing faces using a novel measure that utilizes culturally relevant and age-appropriate items, the Northwestern University Famous Faces (NUFFACE) Test, in primary progressive aphasia (PPA), a syndrome characterized by progressive language deficits and associated with cortical atrophy in areas important for word and object representations. METHODS: NUFFACE Test performance of 27 controls (mean age 62.3 years) was compared with that of 30 patients with PPA (mean age 62 years). Associations between NUFFACE Test performance and cortical thickness measures were quantified within the PPA group. RESULTS: Patients with PPA displayed significant impairment on the NUFFACE Test, demonstrating that it is a useful measure of famous-face identification for individuals with relatively young-onset dementias. Despite widespread distribution of atrophy in the PPA group, face naming impairments were correlated with atrophy of the left anterior temporal lobe while face recognition impairments were correlated with bitemporal atrophy. CONCLUSIONS: In addition to their clinical relevance for highlighting the distinction between face naming and recognition impairments in individuals with young-onset dementia, these findings add new insights into the dissociable clinico-anatomical substrates of lexical retrieval and object knowledge.