Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

BACKGROUND/OBJECTIVE: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

The Pitfall of Behavioral Variant Frontotemporal Dementia Mimics Despite Multidisciplinary Application of the FTDC Criteria.

BACKGROUND: The behavioral variant of frontotemporal dementia (bvFTD) has a broad differential diagnosis including other neurological and psychiatric disorders. Psychiatric misdiagnoses occur in up to 50% of bvFTD patients. Numbers on misdiagnosis of bvFTD in psychiatric disorders are lacking. OBJECTIVE: The aim of our study was to investigate the frequency and characteristics of bvFTD misdiagnoses in psychiatric disorders and other neurologic disorders. METHODS: Thirty-five patients with a (possible or probable) bvFTD diagnosis made by specialized memory clinic neurologists were included. Change in diagnosis after consulting a psychiatrist at baseline was recorded as well as change in diagnosis after two years of multidisciplinary neuropsychiatric follow-up. Differences in cognitive and behavioral profiles were investigated per diagnostic group after follow-up (bvFTD, psychiatry, other neurologic disorders). Clinical profiles are described in detail. RESULTS: In 17 patients (48.5%), the bvFTD baseline diagnosis changed: Two at baseline after psychiatric consultation, and 15 after two years of multidisciplinary follow-up. Eleven (64.5%) of these 17 patients (31.5% of total) were reclassified with a psychiatric diagnosis. We found no differences for cognitive baseline profiles between patients with bvFTD versus psychiatric diagnoses. CONCLUSION: In almost half of cases, the initial bvFTD diagnosis was changed after follow-up, most often into a psychiatric disorder. A multidisciplinary neuropsychiatric approach in the diagnostic process of bvFTD results in the identification of treatable disorders. Our findings illustrate a limited specificity of the [18F]FDG-PET-scan and the bvFTD criteria in a neuropsychiatric cohort, especially combined with certain clinical symptoms, like disinhibition, apathy, or loss of empathy.

Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease.

Both normal aging and Alzheimer's disease (AD) have been associated with a reduction in functional brain connectivity. It is unknown how connectivity patterns due to aging and AD compare. Here, we investigate functional brain connectivity in 12 young adults (mean age 22.8 ± 2.8), 12 older adults (mean age 73.1 ± 5.2) and 12 AD patients (mean age 74.0 ± 5.2; mean MMSE 22.3 ± 2.5). Participants were scanned during 6 different sessions with resting state functional magnetic resonance imaging (RS-fMRI), resulting in 72 scans per group. Voxelwise connectivity with 10 functional networks was compared between groups (p < 0.05, corrected). Normal aging was characterized by widespread decreases in connectivity with multiple brain networks, whereas AD only affected connectivity between the default mode network (DMN) and precuneus. The preponderance of effects was associated with regional gray matter volume. Our findings indicate that aging has a major effect on functional brain interactions throughout the entire brain, whereas AD is distinguished by additional diminished posterior DMN-precuneus coherence.

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease.

BACKGROUND/OBJECTIVE: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. We applied longitudinal resting state functional magnetic resonance imaging (fMRI) to delineate functional brain connections relevant for disease progression and diagnostic accuracy. METHODS: We used two-center resting state fMRI data of 20 AD patients (65.1±8.0 years), 12 bvFTD patients (64.7±5.4 years), and 22 control subjects (63.8±5.0 years) at baseline and 1.8-year follow-up. We used whole-network and voxel-based network-to-region analyses to study group differences in functional connectivity at baseline and follow-up, and longitudinal changes in connectivity within and between groups. RESULTS: At baseline, connectivity between paracingulate gyrus and executive control network, between cuneal cortex and medial visual network, and between paracingulate gyrus and salience network was higher in AD compared with controls. These differences were also present after 1.8 years. At follow-up, connectivity between angular gyrus and right frontoparietal network, and between paracingulate gyrus and default mode network was lower in bvFTD compared with controls, and lower compared with AD between anterior cingulate gyrus and executive control network, and between lateral occipital cortex and medial visual network. Over time, connectivity decreased in AD between precuneus and right frontoparietal network and in bvFTD between inferior frontal gyrus and left frontoparietal network. Longitudinal changes in connectivity between supramarginal gyrus and right frontoparietal network differ between both patient groups and controls. CONCLUSION: We found disease-specific brain regions with longitudinal connectivity changes. This suggests the potential of longitudinal resting state fMRI to delineate regions relevant for disease progression and for diagnostic accuracy, although no group differences in longitudinal changes in the direct comparison of AD and bvFTD were found.

Late life bipolar disorder evolving into frontotemporal dementia mimic.

OBJECTIVES: Although bipolar disorder has been understood classically as a cyclic disease with full recovery between mood episodes, in the last decade, evidence has accumulated supporting progressive features. The clinical picture of advanced or end-stage bipolar disorder is heterogeneous with possible deficits in cognition and behavior, as illustrated by our case series. CASES: From our neuropsychiatric outpatient clinic, we describe four cases with bipolar disorder gradually developing a clinical syndrome, including apathy, disinhibition, loss of empathy, stereotypical behavior, and compulsiveness, fulfilling the criteria for possible behavioral variant frontotemporal dementia. All cases were diagnosed with bipolar 1 disorder at least 10 years before the onset of the current symptoms, which were not due to recent mood episodes or switches of medication. In all cases, 3-7 years of follow-up yielded no progression. Repeated neuroimaging was within normal limits. Cerebrospinal fluid biomarker studies were not supportive of underlying neurodegenerative pathology. C9orf72 mutation status was negative in all cases. CONCLUSION: Symptoms fitting the criteria for possible behavioral variant frontotemporal dementia may be present in end-stage of bipolar disorder. An alternative neurodegenerative nature seems unlikely based on repeated normal neuroimaging and the absence of clinical progression. Functional involvement of the frontal-subcortical networks might play a role.

Diagnostic Accuracy of MRI and Additional [18F]FDG-PET for Behavioral Variant Frontotemporal Dementia in Patients with Late Onset Behavioral Changes.

BACKGROUND: Neuroimaging has a reasonable accuracy to differentiate behavioral variant frontotemporal dementia (bvFTD) from other neurodegenerative disorders, its value for the differentiation of bvFTD among subjects with acquired behavioral disturbances is unknown. OBJECTIVE: To determine the diagnostic accuracy of MRI, additional [18F]FDG-PET, and their combination for bvFTD among subjects with late onset behavioral changes. METHODS: Patients with late onset behavioral changes referred to a memory clinic or psychiatric services were included. At baseline, 111 patients had a brain MRI scan and 74 patients received an additional [18F]FDG-PET when the MRI was inconclusive. The consensus diagnosis after two-year-follow-up was used as the gold standard to calculate sensitivity and specificity for baseline neuroimaging. RESULTS: 27 patients had probable/definite bvFTD and 84 patients had a non-bvFTD diagnosis (primary psychiatric diagnosis or other neurological disorders). MRI had a sensitivity of 70% (95% CI 52-85%) with a specificity of 93% (95% CI 86-97%). Additional [18F]FDG-PET had a sensitivity of 90% (95% CI 66-100%) with a specificity of 68% (95% CI 56-79%). The sensitivity of combined neuroimaging was 96% (95% CI 85-100%) with a specificity of 73% (95% CI 63-81%). In 66% of the genetic FTD cases, MRI lacked typical frontotemporal atrophy. 40% of cases with a false positive [18F]FDG-PET scan had a primary psychiatric diagnosis. CONCLUSION: A good diagnostic accuracy was found for MRI and additional [18F]FDG-PET for bvFTD in patients with late onset behavioral changes. Caution with the interpretation of neuroimaging results should especially be taken in cases with a genetic background and in cases with a primary psychiatric differential diagnosis where [18F]FDG-PET is the only abnormal investigation.

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease.

Purpose To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. Materials and Methods Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. Results Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P < .01), good performance for AD versus MCI (AUC, 0.89; P < .01), and poor performance for MCI versus SCD (AUC, 0.63; P = .06). Application of the AD versus SCD discrimination map for prediction of MCI subgroups resulted in good performance for patients with MCI diagnosis converted to AD versus subjects with SCD (AUC, 0.84; P < .01) and fair performance for patients with MCI diagnosis converted to AD versus those with stable MCI (AUC, 0.71; P > .05). Conclusion With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values. © RSNA, 2016.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease.

We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical thinning per year. Decrease of thickness over time was highest in AD and generalized throughout the whole brain, most pronounced posteriorly, whereas bvFTD patients had a more selective loss in frontal cortex and in anterior parts of the temporal lobes. In a direct comparison, AD patients showed faster cortical thinning in the insula, temporal, and parietal regions, whereas bvFTD patients only showed faster cortical thinning in the orbitofrontal gyrus. Decline of cognitive performances was in line with cortical thinning and deteriorated the most in AD patients.

Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer's disease.

OBJECTIVE: To investigate whether subcortical grey matter atrophy predicts progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), and to compare subcortical volumes between AD, MCI and controls. To assess the correlation between subcortical grey matter volumes and severity of cognitive impairment. METHODS: We included 773 participants with three-dimensional T1-weighted MRI at 3 T, made up of 181 controls, who had subjective memory symptoms with normal cognition, 201 MCIs and 391 AD. During follow-up (2.0 ± 0.9 years), 35 MCIs converted to AD (progressive MCI) and 160 MCIs remained stable (stable MCI). We segmented volumes of six subcortical structures of the amygdala, thalamus, caudate nucleus, putamen, globus pallidus and nucleus accumbens, and of the hippocampus, using FMRIBs integrated registration and segmentation tool. RESULTS: Analysis of variances, adjusted for sex and age, showed that all structures, except the globus pallidus, were smaller in AD than in controls. In addition, the amygdala, thalamus, putamen, nucleus accumbens and hippocampus were smaller in MCIs than in controls. Across groups, all subcortical greymatter volumes, except the globus pallidus, showed a positive correlation with cognitive function, as measured by Mini Mental State Examination (MMSE) (0.16

Impact of Imaging and Cerebrospinal Fluid Biomarkers on Behavioral Variant Frontotemporal Dementia Diagnosis within a Late-Onset Frontal Lobe Syndrome Cohort.

BACKGROUND: The criteria for behavioral variant frontotemporal dementia (bvFTD) incorporate MRI and [18F]-FDG-PET. Cerebrospinal fluid (CSF) analysis is merely advised for excluding Alzheimer's disease. AIMS: We aimed to assess the impact of biomarkers on diagnostic certainty and contingent changes of bvFTD diagnosis within the clinically relevant neuropsychiatric differential diagnosis of subjects with a late-onset frontal lobe syndrome (LOF). METHODS: We included 137 patients with LOF, aged 45-75 years, 72% males. Biomarker disclosure was considered contributing after any substantial difference in diagnostic certainty or a diagnostic change. Percentages of contributing biomarkers were compared between three major diagnostic groups (bvFTD, psychiatry, other neurological disorders). Certainty levels in stable diagnostic groups were compared to those with a diagnostic change. RESULTS: Biomarkers contributed in 53, 60 and 41% of the LOF patients for MRI, [18F]-FDG-PET and CSF, respectively. Biomarkers changed the diagnosis in 14% of cases towards bvFTD and in 13% from bvFTD into an alternative. Those that changed had a lower level of a priori diagnostic certainty compared to stable diagnoses. CONCLUSION: Our study not only supports the widely accepted use of MRI and [18F]-FDG-PET in diagnosing or excluding bvFTD, but also shows that CSF biomarkers aid clinicians in the diagnostic process.

Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease.

Disease-specific patterns of gray matter atrophy in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) overlap with distinct structural covariance networks (SCNs) in cognitively healthy controls. This suggests that both types of dementia target specific structural networks. Here, we study SCNs in AD and bvFTD. We used structural magnetic resonance imaging data of 31 AD patients, 24 bvFTD patients, and 30 controls from two centers specialized in dementia. Ten SCNs were defined based on structural covariance of gray matter density using independent component analysis. We studied group differences in SCNs using F-tests, with Bonferroni corrected t-tests, adjusted for age, gender, and study center. Associations with cognitive performance were studied using linear regression analyses. Cross-sectional group differences were found in three SCNs (all P < 0.0025). In bvFTD, we observed decreased anterior cingulate network integrity compared with AD and controls. Patients with AD showed decreased precuneal network integrity compared with bvFTD and controls, and decreased hippocampal network and anterior cingulate network integrity compared with controls. In AD, we found an association between precuneal network integrity and global cognitive performance (P = 0.0043). Our findings show that AD and bvFTD target different SCNs. The comparison of both types of dementia showed decreased precuneal (i.e., default mode) network integrity in AD and decreased anterior cingulate (i.e., salience) network integrity in bvFTD. This confirms the hypothesis that AD and bvFTD have distinct anatomical networks of degeneration and shows that structural covariance gives valuable insights in the understanding of network pathology in dementia.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study.

We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.

Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. Early differentiation between both types of dementia may be challenging due to heterogeneity and overlap of symptoms. Here, we apply resting state functional magnetic resonance imaging (fMRI) to study functional brain connectivity differences between AD and bvFTD. METHODS: We used resting state fMRI data of 31 AD patients, 25 bvFTD patients, and 29 controls from two centers specialized in dementia. We studied functional connectivity throughout the entire brain, applying two different analysis techniques, studying network-to-region and region-to-region connectivity. A general linear model approach was used to study group differences, while controlling for physiological noise, age, gender, study center, and regional gray matter volume. RESULTS: Given gray matter differences, we observed decreased network-to-region connectivity in bvFTD between (a) lateral visual cortical network and lateral occipital and cuneal cortex, and (b) auditory system network and angular gyrus. In AD, we found decreased network-to-region connectivity between the dorsal visual stream network and lateral occipital and parietal opercular cortex. Region-to-region connectivity was decreased in bvFTD between superior temporal gyrus and cuneal, supracalcarine, intracalcarine cortex, and lingual gyrus. CONCLUSION: We showed that the pathophysiology of functional brain connectivity is different between AD and bvFTD. Our findings support the hypothesis that resting state fMRI shows disease-specific functional connectivity differences and is useful to elucidate the pathophysiology of AD and bvFTD. However, the group differences in functional connectivity are less abundant than has been shown in previous studies.

Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease.

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.

Identifying bvFTD Within the Wide Spectrum of Late Onset Frontal Lobe Syndrome: A Clinical Approach.

OBJECTIVE: The behavioral variant of frontotemporal dementia (bvFTD) can be difficult to diagnose because of the extensive differential diagnosis, including many other diseases presenting with a frontal lobe syndrome. We aimed to identify the diagnostic spectrum causing a late onset frontal lobe syndrome and examine the quality of commonly used instruments to distinguish between bvFTD and non-bvFTD patients, within this syndrome. METHODS: A total of 137 patients fulfilling the criteria of late onset frontal lobe syndrome, aged 45 to 75 years, were included in a prospective observational study. Diagnoses were made after clinical and neuropsychological examination, and neuroimaging and cerebral spinal fluid results were taken into account. Baseline characteristics and the scores on the Mini-Mental State Exam (MMSE), frontal assessment battery (FAB), Frontal Behavioral Inventory (FBI), and Stereotypy Rating Inventory (SRI) were compared between the bvFTD and the non-bvFTD group. RESULTS: Fifty-five (40%) of the patients received a bvFTD diagnosis (33% probable and 7% possible bvFTD). Fifty-one patients (37%) had a psychiatric disorder, including 20 with major depressive disorder. Thirty-one patients received an alternative neurological, including neurodegenerative, diagnosis. MMSE and FAB scores were unspecific for a particular diagnosis. A score above 12 on the positive FBI subscale or a score above 5 on the SRI were indicative of a bvFTD diagnosis. CONCLUSION: A broad spectrum of both neurological and psychiatric disorders underlies late onset frontal lobe syndrome, of which bvFTD was the most prevalent diagnosis in our cohort. The commonly used MMSE and the FAB could not successfully distinguish between bvFTD and non-bvFTD, but this could be achieved with the more specific FBI and SRI.

More atrophy of deep gray matter structures in frontotemporal dementia compared to Alzheimer's disease.

BACKGROUND: The involvement of frontostriatal circuits in frontotemporal dementia (FTD) suggests that deep gray matter structures (DGM) may be affected in this disease. OBJECTIVE: We investigated whether volumes of DGM structures differed between patients with behavioral variant FTD (bvFTD), Alzheimer's disease (AD), and subjective complaints (SC) and explored relationships between DGM structures, cognition, and neuropsychiatric functioning. METHODS: For this cross-sectional study, we included 24 patients with FTD and matched them based on age, gender, and education at a ratio of 1:3 to 72 AD patients and 72 patients with SC who served as controls. Volumes of hippocampus, amygdala, thalamus, caudate nucleus, putamen, globus pallidus, and nucleus accumbens were estimated by automated segmentation of 3D T1-weighted MRI. MANOVA with Bonferroni adjusted post-hoc tests was used to compare volumes between groups. Relationships between volumes, cognition, and neuropsychiatric functioning were examined using multivariate linear regression and Spearman correlations. RESULTS: Nucleus accumbens and caudate nucleus discriminated all groups, with most severe atrophy in FTD. Globus pallidus volumes were smallest in FTD and discriminated FTD from AD and SC. Hippocampus, amygdala, thalamus, and putamen were smaller in both dementia groups compared to SC. Associations between amygdala and memory were found to be different in AD and FTD. Globus pallidus and nucleus accumbens were related to attention and executive functioning in FTD. CONCLUSION: Nucleus accumbens, caudate nucleus, and globus pallidus were more severely affected in FTD than in AD and SC. The associations between cognition and DGM structures varied between the diagnostic groups. The observed difference in volume of these DGM structures supports the idea that next to frontal cortical atrophy, DGM structures, as parts of the frontal circuits, are damaged in FTD rather than in AD.

Distinct perfusion patterns in Alzheimer's disease, frontotemporal dementia and dementia with Lewy bodies.

OBJECTIVES: To compare pseudo-continuous arterial spin-labelled (PCASL) magnetic resonance imaging (MRI) measured quantitative cerebral blood flow (CBF) of patients with frontotemporal dementia (FTD), dementia with Lewy Bodies (DLB), Alzheimer's disease (AD) and controls, in a region of interest (ROI) and voxel-wise fashion. METHODS: We analysed whole-brain 3D fast-spin-echo PCASL images of 20 FTD patients, 14 DLB patients, 48 AD patients and 50 controls from the Amsterdam Dementia Cohort. Regional CBF patterns were compared using analyses of variance for repeated measures. Permutation tests were used for voxel-wise comparisons. Analyses were performed using uncorrected and partial volume corrected (PVC) maps. All analyses were corrected for age and sex. RESULTS: There was an interaction between diagnosis and region (p < 0.001), implying differences in regional CBF changes between diagnostic groups. In AD patients, CBF was decreased in all supratentorial regions, most prominently so in the posterior regions. DLB patients showed lowest CBF values throughout the brain, but temporal CBF was preserved. Supratentorial PVC cortical CBF values were lowest in the frontal lobes in FTD patients, and in the temporal lobes in AD patients. CONCLUSIONS: Patients with AD, FTD and DLB display distinct patterns of quantitative regional CBF changes. 3D-PCASL may provide additional value in the workup of dementia patients. KEY POINTS: Patterns of regional CBF changes differ between AD, FTD and DLB patients. CBF is lower throughout the brain in DLB than AD and FTD. 3D-PCASL MRI is a potential non-invasive and easily accessible alternative to FDG-PET. 3D-PCASL MRI may be of additional value in the workup of dementia.

Single-subject gray matter graph properties and their relationship with cognitive impairment in early- and late-onset Alzheimer's disease.

Abstract We investigated the relationships between gray matter graph properties and cognitive impairment in a sample of 215 patients with Alzheimer's disease (AD) and also whether age of disease onset modifies such relationships. We expected that more severe cognitive impairment in AD would be related to more random graph topologies. Single-subject gray matter graphs were constructed from T1-weighted magnetic resonance imaging scans. The following global and local graph properties were calculated: betweenness centrality, normalized clustering coefficient γ, and normalized path length λ. Local clustering, path length, and betweenness centrality measures were determined for 90 anatomically defined areas. Regression models with as interaction term age of onset (i.e., early onset when patients were ≤65 years old and late onset when they were >65 years old at the time of diagnosis)×graph property were used to assess the relationships between cognitive functioning in five domains (memory, language, visuospatial, attention, and executive). Worse cognitive impairment was associated with more random graphs, as indicated by low γ, λ, and betweenness centrality values. Three interaction effects for age of onset×global graph property were found: Low γ and λ values more strongly related to memory impairment in early-onset patients; low beta values were significantly related to impaired visuospatial functioning in late-onset patients. For the local graph properties, language impairment showed the strongest relationship with decreased clustering coefficient in the left superior temporal gyrus across the entire sample. Our study shows that single-subject gray matter graph properties are associated with individual differences in cognitive impairment.