Altered structural and functional connectivity in Posterior Cortical Atrophy and Dementia with Lewy bodies.

Posterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show distinct atrophy and overlapping hypometabolism profiles, but it is unknown how disruptions in structural and functional connectivity compare between these disorders and whether breakdowns in connectivity relate to either atrophy or hypometabolism. Thirty amyloid-positive PCA patients, 24 amyloid-negative DLB patients and 30 amyloid-negative cognitively unimpaired (CU) healthy individuals were recruited at Mayo Clinic, Rochester, MN, and underwent a 3T head MRI, including structural MRI, resting state functional MRI (rsfMRI) and diffusion tensor imaging (DTI) sequences, as well as [18F] fluorodeoxyglucose (FDG) PET. We assessed functional connectivity within and between 12 brain networks using rsfMRI and the CONN functional connectivity toolbox and calculated regional DTI metrics using the Johns Hopkins atlas. Multivariate linear-regression models corrected for multiple comparisons and adjusted for age and sex compared DTI metrics and within-network and between-network functional connectivity across groups. Regional gray-matter volumes and FDG-PET standard uptake value ratios (SUVRs) were calculated and analyzed at the voxel-level using SPM12. We used univariate linear-regression models to investigate the relationship between connectivity measures, gray-matter volume, and FDG-PET SUVR. On DTI, PCA showed degeneration in occipito-parietal white matter, posterior thalamic radiations, splenium of the corpus collosum and sagittal stratum compared to DLB and CU, with greater degeneration in the temporal white matter and the fornix compared to CU. We observed no white-matter degeneration in DLB compared to CU. On rsfMRI, reduced within-network connectivity was present in dorsal and ventral default mode networks (DMN) and the dorsal-attention network in PCA compared to DLB and CU, with reduced within-network connectivity in the visual and sensorimotor networks compared to CU. DLB showed reduced connectivity in the cerebellar network compared to CU. Between-network analysis showed increased connectivity in both cerebellar-to-sensorimotor and cerebellar-to-dorsal attention network connectivity in PCA and DLB. PCA showed reduced anterior DMN-to-cerebellar and dorsal attention-to-sensorimotor connectivity, while DLB showed reduced posterior DMN-to-sensorimotor connectivity compared to CU. PCA showed reduced dorsal DMN-to-visual connectivity compared to DLB. The multimodal analysis revealed weak associations between functional connectivity and volume in PCA, and between functional connectivity and metabolism in DLB. These findings suggest that PCA and DLB have unique connectivity alterations, with PCA showing more widespread disruptions in both structural and functional connectivity; yet some overlap was observed with both disorders showing increased connectivity from the cerebellum.

Design and validation of the ADNI MR protocol.

Phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the study's design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1-weighting, (2) pseudo-continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple-post-labeling-delay ASL, (4) 1 mm3 isotropic 3D fluid-attenuated inversion recovery, and (5) CS 3D T2-weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum. HIGHLIGHTS: A summary of MRI protocols for phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi-band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.

Premenopausal bilateral oophorectomy and brain white matter brain integrity in later-life.

INTRODUCTION: Premenopausal bilateral oophorectomy (PBO) is associated with later-life cognition, but the underlying brain changes remain unclear. We assessed the impact of PBO and PBO age on white matter integrity. METHODS: Female participants with regional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA) and mean diffusivity (MD) were included (22 with PBO < 40 years; 43 with PBO 40-45 years; 39 with PBO 46-49 years; 907 referents without PBO < 50 years). Linear regression models adjusted for age and apolipoprotein E (APOE) genotype. RESULTS: Females with PBO < 40 years, compared to referents, had lower FA and higher MD in the anterior corona radiata, genu of the corpus collosum, inferior fronto-occipital fasciculus, superior occipital, and superior temporal white matter. Females who underwent PBO between 45 and 49 also had some changes in white matter integrity. DISCUSSION: Females who underwent PBO < 40 years had reduced white matter integrity across multiple regions in later-life. These results are important for females considering PBO for noncancerous conditions. HIGHLIGHTS: Females with premenopausal bilateral oophorectomy (PBO) < 40 years had lower FA versus referents. Females with PBO < 40 years had higher MD in many regions versus referents. Adjusting for estrogen replacement therapy use did not attenuate results. Females with PBO 45-49 years also had some white matter changes versus referents.

Vascular risk, gait, behavioral, and plasma indicators of VCID.

INTRODUCTION: Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS: In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS: VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION: Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS: Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.

Overview of ADNI MRI.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

A face-off of MRI research sequences by their need for de-facing.

It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.

Cross-scanner harmonization methods for structural MRI may need further work: A comparison study.

The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.

Neuropathologic scales of cerebrovascular disease associated with diffusion changes on MRI.

Summarizing the multiplicity and heterogeneity of cerebrovascular disease (CVD) features into a single measure has been difficult in both neuropathology and imaging studies. The objective of this work was to evaluate the association between neuroimaging surrogates of CVD and two available neuropathologic CVD scales in those with both antemortem imaging CVD measures and postmortem CVD evaluation. Individuals in the Mayo Clinic Study of Aging with MRI scans within 5 years of death (N = 51) were included. Antemortem CVD measures were computed from diffusion MRI (dMRI), FLAIR, and T2* GRE imaging modalities and compared with postmortem neuropathologic findings using Kalaria and Strozyk Scales. Of all the neuroimaging measures, both regional and global dMRI measures were associated with Kalaria and Strozyk Scales (p < 0.05) and modestly correlated with global cognitive performance. The major conclusions from this study were: (i) microstructural white matter injury measurements using dMRI may be meaningful surrogates of neuropathologic CVD scales, because they aid in capturing diffuse (and early) changes to white matter and secondary neurodegeneration due to lesions; (ii) vacuolation in the corpus callosum may be associated with white matter changes measured on antemortem dMRI imaging; (iii) Alzheimer's disease neuropathologic change did not associate with neuropathologic CVD scales; and (iv) future work should be focused on developing better quantitative measures utilizing dMRI to optimally assess CVD-related neuropathologic changes.

Gray and White Matter Correlates of Dysphagia in Progressive Supranuclear Palsy.

BACKGROUND: Dysphagia is a common symptom of progressive supranuclear palsy often leading to aspiration pneumonia and death. OBJECTIVE: The aim of this study was to examine how impairments of the oral and pharyngeal phases of the swallow and airway incursion during liquid swallows relate to gray and white matter integrity. METHODS: Thirty-eight participants with progressive supranuclear palsy underwent videofluorographic swallowing assessment and structural and diffusion tensor head magnetic resonance imaging. Penalized linear regression models assessed relationships between swallowing metrics and regional gray matter volumes and white matter fractional anisotropy and mean diffusivity. RESULTS: Oral phase impairments were associated with reduced superior parietal volumes and abnormal diffusivity in parietal and sensorimotor white matter, posterior limb of the internal capsule, and superior longitudinal fasciculus. Pharyngeal phase impairments were associated with disruption to medial frontal lobe, corticospinal tract, and cerebral peduncle. No regions were predictive of airway incursion. CONCLUSIONS: Differential patterns of neuroanatomical impairment corresponded to oral and pharyngeal phase swallowing impairments. © 2021 International Parkinson and Movement Disorder Society.

Amyloid, Vascular, and Resilience Pathways Associated with Cognitive Aging.

OBJECTIVE: To investigate the multifactorial processes underlying cognitive aging based on the hypothesis that multiple causal pathways and mechanisms (amyloid, vascular, and resilience) influence longitudinal cognitive decline in each individual through worsening brain health. METHODS: We identified 1,230 elderly subjects (aged ≥50 years) with an average of 4.9 years of clinical follow-up and with amyloid positron emission tomography, diffusion tensor imaging, and structural magnetic resonance imaging scans from the population-based Mayo Clinic Study of Aging. We examined imaging markers of amyloid and brain health (white matter microstructural integrity and cortical thinning), systemic vascular health preceding the imaging markers, and early to midlife intellectual enrichment to predict longitudinal cognitive trajectories. We used latent growth curve models for modeling longitudinal cognitive decline. RESULTS: All the pathways (amyloid, vascular, resilience) converged through their effects on cortical thinning and worsening cognition and together explained patterns in cognitive decline. Resilience and vascular pathways (aging process, sex differences, education/occupation, and systemic vascular health) had significant impact on white matter microstructural integrity. Education/occupation levels contributed to white matter integrity through systemic vascular health. Worsening white matter integrity contributed to significant cortical thinning and subsequently longitudinal cognitive decline. Baseline amyloidosis contributed to a significant proportion of cognitive decline that accelerated with longer follow-up times, and its primary impact was through cortical thinning. INTERPRETATION: We developed an integrated framework to help explain the dynamic and complex process of cognitive aging by considering key causal pathways. Such an approach is important for both better comprehension of cognitive aging processes and will aid in the development of successful intervention strategies. ANN NEUROL 2019;86:866-877.

Progressive agrammatic aphasia without apraxia of speech as a distinct syndrome.

Agrammatic aphasia affects grammatical language production and can result from a neurodegenerative disease. Although it typically presents with concomitant apraxia of speech, this is not always the case. Little is known about the clinical course and imaging features of patients that present with agrammatism in the absence of apraxia of speech, which we will refer to as progressive agrammatic aphasia. We aimed to make a detailed description of the longitudinal clinical, linguistic, and neuroimaging features of a cohort of 11 patients with progressive agrammatic aphasia to provide a complete picture of this syndrome. All patients underwent detailed speech and language, neurological and neuropsychological assessments, 3 T structural and diffusion tensor imaging MRI, 18F-fluorodeoxyglucose and Pittsburgh compound B PET. The 11 patients were matched by age and gender to 22 patients who had mixed apraxia of speech and agrammatism. The progressive agrammatic aphasia patients performed abnormally on tests of language, general cognition, executive function, and functional ability at baseline and declined in these measures over time. Only two patients eventually developed apraxia of speech, while parkinsonism was absent-to-mild throughout all visits for all patients. When compared to the patients with mixed apraxia of speech and agrammatism, the patients with progressive agrammatic aphasia performed better on tests of motor speech and parkinsonism but more poorly, and declined faster over time, on tests of general aphasia severity, agrammatism, and naming. The patients with progressive agrammatic aphasia also showed different neuroimaging abnormalities, with greater atrophy, hypometabolism and white matter tract degeneration in the prefrontal and anterior temporal lobes compared to patients with mixed apraxia of speech and agrammatism. These differences were more pronounced as the disease progressed. These results demonstrate that progressive agrammatic aphasia has a different clinical disease course and different underlying neuroanatomical abnormalities than patients with the more common syndrome of mixed agrammatism and apraxia of speech. This supports the distinction of progressive agrammatic aphasia and has implications for the classification of patients with agrammatic aphasia.

Hearing loss and microstructural integrity of the brain in a dementia-free older population.

INTRODUCTION: As hearing loss has been identified as an important risk factor for dementia, we aimed to assess the association between hearing loss and microstructural integrity of the brain. METHODS: A total of 1086 dementia-free participants (mean age = 75.2 [standard deviation: 4.9], 61.4% female) of the population-based Atherosclerosis Risk in Communities (ARIC) study underwent hearing assessment (2016-2017) and magnetic resonance imaging of the brain (2011-2013). Microstructural integrity was determined with diffusion tensor imaging. Multivariable linear regression was used to investigate associations between hearing loss and microstructural integrity of different brain regions and white matter (WM) tracts. RESULTS: Hearing loss was associated with lower WM microstructural integrity in the temporal lobe, lower gray matter integrity of the hippocampus, and with lower WM microstructural integrity of the limbic tracts and the uncinate fasciculus. CONCLUSION: Our results demonstrate that hearing loss is indepedently associated with lower microstructural integrity in brain regions that are important for different cognitive processes.

Regional multimodal relationships between tau, hypometabolism, atrophy, and fractional anisotropy in atypical Alzheimer's disease.

Alzheimer's disease (AD) can present with atypical clinical forms where the prominent domain of deficit is not memory, that is, atypical AD. Atypical AD patients show cortical atrophy on MRI, hypometabolism on [18 F]fluorodeoxyglucose (FDG) PET, tau uptake on [18 F]AV-1451 PET, and white matter tract degeneration on diffusion tensor imaging (DTI). How these disease processes relate to each other locally and distantly remains unclear. We aimed to examine multimodal neuroimaging relationships in individuals with atypical AD, using univariate and multivariate techniques at region- and voxel-level. Forty atypical AD patients underwent MRI, FDG-PET, tau-PET, beta-amyloid PET, and DTI. Patients were all beta-amyloid positive. Partial Pearson's correlations were performed between tau and FDG standardized uptake value ratios, gray matter MRI-volumes and white matter tract fractional anisotropy. Sparse canonical correlation analysis was applied to identify multivariate relationships between the same quantities. Voxel-level associations across modalities were also assessed. Tau showed strong local negative correlations with FDG metabolism in the occipital and frontal lobes. Tau in frontal and parietal regions was negatively associated with temporoparietal gray matter MRI-volume. Fractional anisotropy in a set of posterior white matter tracts, including the splenium of the corpus callosum, cingulum, and posterior thalamic radiation, was negatively correlated with parietal and occipital tau, atrophy and, predominantly, with hypometabolism. These results support the view that tau is the driving force behind neurodegeneration in atypical AD, and that a breakdown in structural connectivity is related to cortical neurodegeneration, particularly hypometabolism.

Development of a cerebrovascular magnetic resonance imaging biomarker for cognitive aging.

OBJECTIVE: Recent availability of amyloid and tau positron emission tomography (PET) has provided us with a unique opportunity to measure the association of systemic vascular health with brain health after accounting for the impact of Alzheimer disease (AD) pathologies. We wanted to quantify early cerebrovascular health-related magnetic resonance imaging brain measures (structure, perfusion, microstructural integrity) and evaluate their utility as a biomarker for cerebrovascular health. METHODS: We used 2 independent samples (discovery, n = 390; validation, n = 1,035) of individuals, aged ≥ 60 years, along the cognitive continuum with imaging from the population-based sample of Mayo Clinic Study of Aging. We ascertained vascular health by summing up recently existing cardiovascular and metabolic conditions (CMC) from health care records (hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke). Using multiple regression models, we quantified associations between CMC and brain health after accounting for age, sex, education/occupation, and AD burden (from amyloid and tau PET). RESULTS: Systemic vascular health was associated with medial temporal lobe thinning, widespread cerebral hypoperfusion, and loss of microstructural integrity in several white matter tracts including the corpus callosum and fornix. Further investigations suggested that microstructural integrity of the genu of the corpus callosum was suitable for assessing prodromal cerebrovascular health, had similar distributions in the discovery and independent validation datasets, and predicted cognitive performance above and beyond amyloid deposition. INTERPRETATION: Systemic vascular health has significant impact on brain structure and function. Quantifying prodromal cerebrovascular health-related brain measures that are independent of AD pathology-related changes has great utility for cognitive aging. Ann Neurol 2018;84:713-724.

Longitudinal structural and molecular neuroimaging in agrammatic primary progressive aphasia.

The agrammatic variant of primary progressive aphasia affects normal grammatical language production, often occurs with apraxia of speech, and is associated with left frontal abnormalities on cross-sectional neuroimaging studies. We aimed to perform a detailed assessment of longitudinal change on structural and molecular neuroimaging to provide a complete picture of neurodegeneration in these patients, and to determine how patterns of progression compare to patients with isolated apraxia of speech (primary progressive apraxia of speech). We assessed longitudinal structural MRI, diffusion tensor imaging and 18F-fluorodeoxyglucose PET in 11 agrammatic aphasia subjects, 20 primary progressive apraxia of speech subjects, and 62 age and gender-matched controls with two serial assessments. Rates of change in grey matter volume and hypometabolism, and white matter fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity were assessed at the voxel-level and for numerous regions of interest. The greatest rates of grey matter atrophy in agrammatic aphasia were observed in inferior, middle, and superior frontal gyri, premotor and motor cortices, as well as medial temporal lobe, insula, basal ganglia, and brainstem compared to controls. Longitudinal decline in metabolism was observed in the same regions, with additional findings in medial and lateral parietal lobe. Diffusion tensor imaging changes were prominent bilaterally in inferior and middle frontal white matter and superior longitudinal fasciculus, as well as right inferior fronto-occipital fasciculus, superior frontal and precentral white matter. More focal patterns of degeneration of motor and premotor cortex were observed in primary progressive apraxia of speech. Agrammatic aphasia showed greater rates of grey matter atrophy, decline in metabolism, and white matter degeneration compared to primary progressive apraxia of speech in the left frontal lobe, predominantly inferior and middle frontal grey and white matter. Correlations were also assessed between rates of change on neuroimaging and rates of clinical decline. Progression of aphasia correlated with rates of degeneration in frontal and temporal regions within the language network, while progression of parkinsonism and limb apraxia correlated with degeneration of motor cortex and brainstem. These findings demonstrate that disease progression in agrammatic aphasia is associated with widespread neurodegeneration throughout regions of the language network, as well as connecting white matter tracts, but also with progression to regions outside of the language network that are responsible for the development of motor symptoms. The fact that patterns of progression differed from primary progressive apraxia of speech supports the clinical distinction of these syndromes.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease.

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion-weighted imaging (DWI) offers a non-invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm-autoMATE (automated Multi-Atlas Tract Extraction); we then extracted multiple DWI-derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method-FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191-1207, 2017. © 2016 Wiley Periodicals, Inc.

An investigation of cerebrovascular lesions in dementia with Lewy bodies compared to Alzheimer's disease.

INTRODUCTION: Cerebrovascular lesions on MRI are common in Alzheimer's disease (AD) dementia, but less is known about their frequency and impact on dementia with Lewy bodies (DLB). METHODS: White-matter hyperintensities (WMHs) and infarcts on MRI were assessed in consecutive DLB (n = 81) and AD dementia (n = 240) patients and compared to age-matched and sex-matched cognitively normal subjects (CN) from a population-based cohort. RESULTS: DLB had higher WMH volume compared to CN, and WMH volume was higher in the occipital and posterior periventricular regions in DLB compared to AD. Higher WMH volume was associated with history of cardiovascular disease and diabetes but not with clinical disease severity in DLB. Frequency of infarcts in DLB was not different from CN and AD dementia. DISCUSSION: In DLB, WMH volume is higher than AD and CN and appears to be primarily associated with history of vascular disease.