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Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual’s course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field., This work was supported in part by the Allen H. and Selma W. Berkman Charitable Trust (Accelerating Research on Vascular Dementia) and National Institutes of Health Grant Nos. 1RF1AG054409 (to MH, CD, DW), R01 HL127659-04S1 (to MH), AG057832 (to CM), AG054519 (to CM), AG055005 (to DW, MH, CM), and P30-AG010124 (to DW, MH, CM). MJG is supported by the “Miguel Servet” program [CP19/00031] of the Spanish Instituto de Salud Carlos III (ISCIII-FEDER).

Introduction Mutations in the progranulin (GRN) gene are a major source of inherited frontotemporal degeneration (FTD) spectrum disorders associated with TDP-43 proteinopathy. We use structural MRI to identify regions of baseline differences and longitudinal changes in gray matter (GM) and white matter (WM) in presymptomatic GRN mutation carriers (pGRN+) compared to young controls (yCTL). Methods Cognitively intact first-degree relatives of symptomatic GRN+ FTD patients with identified GRN mutations (pGRN+; N = 11, mean age = 41.4) and matched yCTL (N = 11, mean age = 53.6) were identified. They completed a MRI session with T1-weighted imaging to assess GM density (GMD) and diffusion-weighted imaging (DWI) to assess fractional anisotropy (FA). Participants completed a follow-up session with T1 and DWI imaging (pGRN+ mean interval 2.20 years; yCTL mean interval 3.27 years). Annualized changes of GMD and FA were also compared. Results Relative to yCTL, pGRN+ individuals displayed reduced GMD at baseline in bilateral orbitofrontal, insular, and anterior temporal cortices. pGRN+ also showed greater annualized GMD changes than yCTL at follow-up in right orbitofrontal and left occipital cortices. We also observed reduced FA at baseline in bilateral superior longitudinal fasciculus, left corticospinal tract, and frontal corpus callosum in pGRN+ relative to yCTL, and pGRN+ displayed greater annualized longitudinal FA change in right superior longitudinal fasciculus and frontal corpus callosum. Conclusions Longitudinal MRI provides evidence of progressive GM and WM changes in pGRN+ participants relative to yCTL. Structural MRI illustrates the natural history of presymptomatic GRN carriers, and may provide an endpoint during disease-modifying treatment trials for pGRN+ individuals at risk for FTD., Highlights • Presymptomatic GRN mutation carriers (pGRN+) show decreased gray matter and white matter relative to young controls (yCTL) • pGRN+ show greater longitudinal changes in gray matter and white matter relative to yCTL • Regions with decreased gray matter and white matter in pGRN+ are in regions showing disease in symptomatic GRN+

Aims There is increasing interest in the influence of single nucleotide polymorphisms (SNPs) on the trajectory of cognitive decline in dementia. We evaluated the hypothesis that rs1768208 risk allele copies are associated with cognitive decline in behavioral variant frontotemporal degeneration (bvFTD), a common form of young-onset dementia. Methods Forty-three individuals diagnosed with bvFTD (mean baseline age=60.1 years ±7.7, mean disease duration at baseline 3.6 years ± 2.2, mean baseline MMSE 26.2 years ± 7.1) were studied. All subjects had at least 2 verbal fluency observations to assess executive function. Patients were genotyped for rs1768208 using a custom pan-neurodegenerative disease SNP genotyping panel and were coded according to the number of risk (T) alleles (0,1,2). Linear mixed-effects models assessed the effect of genotype on performance changes over time. To evaluate the neuroanatomic basis for longitudinal decline, regression analyses related performance change in executive function to grey matter (GM) and white matter (WM). Results There was a significant dose-dependent genotype by time interaction (F[2, 29]=8.42; p=0.001) for declining performance on verbal fluency (B values refer to words per month): 2 risk alleles (B=0.48; p Conclusions Individuals with rs1768208 risk alleles are at risk for a rapid decline in executive function and this is related to more severe neurodegeneration in key frontal regions. These findings suggest that individual genetic variations contribute to heterogeneity in clinical progression.

A growing amount of empirical data is showing that the ability to manipulate quantities in a precise and efficient fashion is rooted in cognitive mechanisms devoted to specific aspects of numbers processing. The analog number system (ANS) has a reasonable representation of quantities up to about 4, and represents larger quantities on the basis of a numerical ratio between quantities. In order to represent the precise cardinality of a number, the ANS may be supported by external algorithms such as language, leading to a "precise number system". In the setting of limited language, other number-related systems can appear. For example the parallel individuation system (PIS) supports a "chunking mechanism" that clusters units of larger numerosities into smaller subsets. In the present study we investigated number processing in non-aphasic patients with corticobasal syndrome (CBS) and posterior cortical atrophy (PCA), two neurodegenerative conditions that are associated with progressive parietal atrophy. The present study investigated these number systems in CBS and PCA by assessing the property of the ANS associated with smaller and larger numerosities, and the chunking property of the PIS. The results revealed that CBS/PCA patients are impaired in simple calculations (e.g., addition and subtraction) and that their performance strongly correlates with the size of the numbers involved in these calculations, revealing a clear magnitude effect. This magnitude effect was correlated with gray matter atrophy in parietal regions. Moreover, a numeral-dots transcoding task showed that CBS/PCA patients were able to take advantage of clustering in the spatial distribution of the dots of the array. The relative advantage associated with chunking compared to a random spatial distribution correlated with both parietal and prefrontal regions. These results shed light on the properties of systems for representing number knowledge in non-aphasic patients with CBS and PCA.

Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal–occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.

This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, non-fluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data.

article i nfo Article history: Accepted 26 November 2012 Available online 6 December 2012 Keywords: Semantic fMRI DTI Alzheimer's Temporal Prefrontal Patients with Alzheimer's disease have category-specifi cs emantic memory difficulty for natural relative to manufactured objects. We assessed the basis for this deficit by asking healthy adults and patients to judge whether pairs of words share a feature (e.g. "banana:lemon—COLOR"). In an fMRI study, healthy adults showed gray matter (GM) activation of temporal-occipital cortex (TOC) where visual-perceptual features may be represented,andprefrontalcortex(PFC)whichmaycontributetofeatureselection.Tractographyrevealeddorsal and ventral stream white matter (WM) projections between PFC and TOC. Patients had greater difficulty with natural than manufactured objects. This was associated with greater overlap between diseased GM areas corre- lated with natural kinds in patients and fMRI activation in healthy adults for natural kinds. The dorsal WM pro- jectionbetweenPFCandTOCinpatientscorrelatedonlywith judgmentsofnatural kinds.Patientsthusremained dependent on the same neural network as controls during judgments of natural kinds, despite disease in these areas. For manufactured objects, patients' judgments showed limited correlations with PFC and TOC GM areas activated by controls, and did not correlate with the PFC-TOC dorsal WM tract. Regions outside of the PFC- TOC network thus may help support patients' judgments of manufactured objects. We conclude that a large-scale neural network for semantic memory implicates both feature knowledge representations in modality-specific association cortex and heteromodal regions important for accessing this knowledge, and that patients' relative deficit for natural kinds is due in part to their dependence on this network despite disease in these areas.

Few studies have examined connected speech in demented and non-demented patients with Parkinson's disease (PD). We assessed the speech production of 35 patients with Lewy body spectrum disorder (LBSD), including non-demented PD patients, patients with PD dementia (PDD), and patients with dementia with Lewy bodies (DLB), in a semi-structured narrative speech sample in order to characterize impairments of speech fluency and to determine the factors contributing to reduced speech fluency in these patients. Both demented and non-demented PD patients exhibited reduced speech fluency, characterized by reduced overall speech rate and long pauses between sentences. Reduced speech rate in LBSD correlated with measures of between-utterance pauses, executive functioning, and grammatical comprehension. Regression analyses related non-fluent speech, grammatical difficulty, and executive difficulty to atrophy in frontal brain regions. These findings indicate that multiple factors contribute to slowed speech in LBSD, and this is mediated in part by disease in frontal brain regions.

While grammatical aspects of language are preserved, executive deficits are prominent in Lewy body spectrum disorder (LBSD), including Parkinson’s disease (PD), Parkinson’s dementia (PDD) and dementia with Lewy bodies (DLB). We examined executive control during sentence processing in LBSD by assessing temporary structural ambiguities. Using an on-line word detection procedure, patients heard sentences with a syntactic structure that has high-compatibility or low-compatibility with the main verb’s statistically preferred syntactic structure, and half of the sentences were lengthened strategically between the onset of the ambiguity and its resolution. We found selectively slowed processing of lengthened ambiguous sentences in the PDD/DLB subgroup. This correlated with impairments on measures of executive control. Regression analyses related the working memory deficit during ambiguous sentence processing to significant cortical thinning in frontal and parietal regions. These findings emphasize the role of prefrontal disease in the executive limitations that interfere with processing ambiguous sentences in LBSD.

Quantifiers are very common in everyday speech, but we know little about their cognitive basis or neural representation. The present study examined comprehension of three classes of quantifiers that depend on different cognitive components in patients with focal neurodegenerative diseases. Patients evaluated the truth-value of a sentence containing a quantifier relative to a picture illustrating a small number of familiar objects, and performance was related to MRI grey matter atrophy using voxel-based morphometry. We found that patients with corticobasal syndrome (CBS) and posterior cortical atrophy (PCA) are significantly impaired in their comprehension of cardinal quantifiers (e.g. "At least three birds are on the branch"), due in part to their deficit in quantity knowledge. MRI analyses related this deficit to temporal-parietal atrophy found in CBS/PCA. We also found that patients with behavioral variant frontotemporal dementia (bvFTD) are significantly impaired in their comprehension of logical quantifiers (e.g. "Some of the birds are on the branch"), associated with a simple form of perceptual logic, and this correlated with their deficit on executive measures. This deficit was related to disease in rostral prefrontal cortex in bvFTD. These patients were also impaired in their comprehension of majority quantifiers (e.g. "At least half of the birds are on the branch"), and this too was correlated with their deficit on executive measures. This was related to disease in the basal ganglia interrupting a frontal-striatal loop critical for executive functioning. These findings suggest that a large-scale frontal-parietal neural network plays a crucial role in quantifier comprehension, and that comprehension of specific classes of quantifiers may be selectively impaired in patients with focal neurodegenerative conditions in these areas.

The ventral medial prefrontal cortex (vmPFC) has been implicated in social and affectively influenced decision-making. Disease in this region may have clinical consequences for social judgments in patients with frontotemporal lobar degeneration (FTLD). To test this hypothesis, regional cortical activation was monitored with fMRI while healthy adults judged the acceptability of brief social scenarios such as cutting into a movie ticket line or going through a red light at 2 AM. The scenarios described: (i) a socially neutral condition, (ii) a variant of each scenario containing a negatively-valenced feature, and (iii) a variant containing a positively-valenced feature. Results revealed that healthy adults activated vmPFC during judgments of negatively-valenced scenarios relative to positive scenarios and neutral scenarios. In a comparative behavioral study, the same social decision-making paradigm was administered to patients with a social disorder due to FTLD. Patients differed significantly from healthy controls, specifically showing less sensitivity to negatively-valenced features. Comparative anatomical analysis revealed considerable overlap of vmPFC activation in healthy adults and vmPFC cortical atrophy in FTLD patients. These converging results support the role of vmPFC in social decision-making where potentially negative consequences must be considered.

We investigated the cognitive and neural bases of impaired speech fluency, a central feature of primary progressive aphasia. Speech fluency was assessed in 35 patients with frontotemporal lobar degeneration (FTLD) who presented with progressive non-fluent aphasia (PNFA, n=11), semantic dementia (SemD, n=12), or a social and executive disorder without aphasia (SOC/EXEC, n=12). Fluency was quantified as the number of words per minute in an extended, semi-structured speech sample. This was related to language characteristics of the speech sample and to neuropsychological measures. PNFA patients were significantly less fluent than controls and other FTLD patients. Fluency correlated with grammatical expression but not with speech errors or executive difficulty. SemD and SOC/EXEC patients were also less fluent than controls. In SemD, fluency was associated with semantically limited content. In SOC/EXEC, fluency was associated with executive limitations. Voxel-based morphometry analyses of high-resolution MRI related fluency to gray matter volume in left inferior frontal, insula, and superior temporal regions for the entire cohort of FTLD patients. This region overlapped partially distinct atrophic areas in each FTLD subgroup. It thus appears to play a crucial role in speech fluency, which can be interrupted in different ways in different FTLD subgroups.

To investigate the basis for impaired sentence comprehension in patients with frontotemporal dementia (FTD) we assessed grammatical comprehension and verbal working memory in 88 patients with three distinct presentations: progressive nonfluent aphasia (PNFA), semantic dementia (SD), and nonaphasic patients with a disorder of social comportment and executive processing (SOC/EXEC). We related sentence comprehension and working memory performance to regional cortical volume in a subgroup of 29 patients with structural MRI scans using voxel-based morphometry. PNFA patients exhibited the greatest difficulty with sentence comprehension and were especially impaired with grammatically complex sentences, which correlated with atrophy in left inferior frontal cortex. Working memory performance in these same patients correlated with a proximal but distinct left inferior frontal region. SD patients’ sentence comprehension scores correlated with left inferolateral temporal lobe damage, which we hypothesize and reflect impairments in lexical processing. We did not observe any consistent relationship between cortical atrophy and sentence comprehension impairment in SOC/EXEC patients, suggesting the deficits in this subgroup may be due to more variable declines in executive resources.

We monitored regional cerebral activity with BOLD fMRI during acquisition of a novel semantic category and subsequent categorization of test stimuli by a rule-based strategy or a similarity-based strategy. We observed different patterns of activation in direct comparisons of rule- and similarity-based categorization. During rule-based category acquisition, subjects recruited anterior cingulate, thalamic, and parietal regions to support selective attention to perceptual features, and left inferior frontal cortex to helps maintain rules in working memory. Subsequent rule-based categorization revealed anterior cingulate and parietal activation while judging stimuli whose conformity with the rules was readily apparent, and left inferior frontal recruitment during judgments of stimuli whose conformity was less apparent. By comparison, similarity-based category acquisition showed recruitment of anterior prefrontal and posterior cingulate regions, presumably to support successful retrieval of previously encountered exemplars from long-term memory, and bilateral temporal-parietal activation for perceptual feature integration. Subsequent similarity-based categorization revealed temporal-parietal, posterior cingulate, and anterior prefrontal activation. These findings suggest that large-scale networks support relatively distinct categorization processes during the acquisition and judgment of semantic category knowledge.

Generalized quantifiers like "all cars" are semantically well understood, yet we know little about their neural representation. Our model of quantifier processing includes a numerosity device, operations that combine number elements and working memory. Semantic theory posits two types of quantifiers: first-order quantifiers identify a number state (e.g. "at least 3") and higher-order quantifiers additionally require maintaining a number state actively in working memory for comparison with another state (e.g. "less than half"). We used BOLD fMRI to test the hypothesis that all quantifiers recruit inferior parietal cortex associated with numerosity, while only higher-order quantifiers recruit prefrontal cortex associated with executive resources like working memory. Our findings showed that first-order and higher-order quantifiers both recruit right inferior parietal cortex, suggesting that a numerosity component contributes to quantifier comprehension. Moreover, only probes of higher-order quantifiers recruited right dorsolateral prefrontal cortex, suggesting involvement of executive resources like working memory. We also observed activation of thalamus and anterior cingulate that may be associated with selective attention. Our findings are consistent with a large-scale neural network centered in frontal and parietal cortex that supports comprehension of generalized quantifiers.