Motor performance differentiates individuals with Lewy body dementia, Parkinson's and Alzheimer's disease.

INTRODUCTION: Differential diagnosis of dementia with Lewy bodies (DLB), Parkinson's disease with dementia (PDD), Parkinson's disease (PD) and Alzheimer's disease (AD) is challenging. Comparative motor profiles of these neurodegenerative disorders may aid in earlier diagnosis but have not been extensively studied. METHODS: Groups were rigorously matched by age, education, and sex. DLB/PDD participants were matched by Mini-Mental State Examination Score to individuals with AD and by Unified Parkinson's Disease Rating Scale motor scores to individuals with PD. Gait, balance, dual task walking and hand dexterity measures were compared between a combined group (n=21) of individuals with Lewy body dementia (LBD) consisting of those with DLB (n=11) and PDD (n=10) to individuals with PD (n=21) or AD (n=21). RESULTS: Individuals at the same disease stage with LBD walked significantly slower with shorter stride lengths (p<0.05), demonstrated poorer balance on both the Tinetti and Berg Balance Scale, and poorer performance on dual-task and figure-of-eight walking compared to PD and AD (p<0.05 for all) groups. Upper extremity coordination on the 9-hole peg test differentiated LBD from both PD and AD and was the only motor test in which individuals with AD performed worse than those with PD. Tinetti balance subscores were significantly lower in PDD compared to DLB participants (10.4±2.3 versus 12.8±2.3; p=0.027). CONCLUSIONS: Motor features distinguish individuals with LBD from those with AD and PD. Measures of gait, balance and finger dexterity provide an additional means of differentiating individuals with LBD from those with AD and PD.

Imaging and machine learning techniques for diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

Paired Studies Comparing Clinical Profiles of Lewy Body Dementia with Alzheimer's and Parkinson's Diseases.

Limited data compares clinical profiles of Lewy Body Dementia (LBD) with Alzheimer's disease (AD) and Parkinson's disease (PD). Twenty-one mildly demented ambulatory LBD subjects were individually matched by MMSE score with 21 AD subjects and by UPDRS motor score with 21 PD subjects. Matched by age, gender, education, and race, pairs were compared using cognitive, functional, behavioral, and motor measures. LBD group performed worse than PD on axial motor, gait, and balance measures. AD had more amnesia and orientation impairments, but less executive and visuospatial deficits than LBD subjects. LBD group had more sleepiness, cognitive/behavioral fluctuations, hallucinations, and sleep apnea than AD or PD. Axial motor, gait, and balance disturbances correlated with executive, visuospatial, and global cognition deficits. LBD is differentiated from AD and PD by retrieval memory, visuospatial, and executive deficits; axial motor, gait and balance impairments; sleepiness, cognitive/behavioral fluctuations, hallucinations, and sleep apnea.

A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG).

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, greater than expected by age. A new methodology is presented to identify MCI patients during a working memory task using MEG signals. The methodology consists of four steps: In step 1, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose the MEG signal into a set of adaptive sub-bands according to its contained frequency information. In step 2, a nonlinear dynamics measure based on permutation entropy (PE) analysis is employed to analyze the sub-bands and detect features to be used for MCI detection. In step 3, an analysis of variation (ANOVA) is used for feature selection. In step 4, the enhanced probabilistic neural network (EPNN) classifier is applied to the selected features to distinguish between MCI and healthy patients. The usefulness and effectiveness of the proposed methodology are validated using the sensed MEG data obtained experimentally from 18 MCI and 19 control patients.

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task.

OBJECTIVES: The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. METHODS: MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks' complexities are measured by Graph Index Complexity (C(r)) and Efficiency Complexity (C(e)). RESULTS: The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. CONCLUSIONS: C(e) would be a better measurement for showing the global differences between normal and MCI brains compared with C(r). SIGNIFICANCE: The high accuracy of the classification shows C(e) at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MCI.

The GGGGCC repeat expansion in C9ORF72 in a case with discordant clinical and FDG-PET findings: PET trumps syndrome.

A hexanucleotide repeat expansion in the chromosome 9 open reading frame 72 (C9ORF72) gene was recently discovered as the cause underlying frontotemporal degeneration (FTD) and/or amyotrophic lateral sclerosis (ALS) linked to chromosome 9 (c9FTD/ALS). In this atypical case of c9FTD/ALS, the proband presented with amnestic mild cognitive impairment which evolved into Alzheimer's disease (AD)-type dementia and later developed ALS. Fluorodeoxyglucose-positron emission tomography of the brain demonstrated mild hypometabolism involving the medial frontal and lateral temporal lobes, left more so than right, which progressed over time. He was subsequently confirmed to have the C9ORF72 expansion. This report highlights the need to consider mutations in the FTD-associated genes when a familial disorder is suggested and neuroimaging studies reveal findings atypical of an AD pathophysiological process despite the typical anterograde amnestic syndrome.

Resolution of neuropsychological and FDG-PET abnormalities in a patient with neuropsychiatric systemic lupus erythematosus.

Many patients with systemic lupus erythematosus have central nervous system involvement. Routine diagnostic studies may not yield evidence of neuropsychiatric dysfunction and are therefore not useful as objective measures to monitor treatment response. We present a case of a 64-year-old woman whom we diagnosed with systemic lupus erythematosus by the American College of Rheumatology criteria after she reported recent cognitive decline. Neuropsychological assessment showed prominent deficits, and an F-18 fluorodeoxyglucose positron emission tomography scan of the brain showed significant abnormalities. Both the neuropsychiatric and scan abnormalities improved dramatically with immunosuppressive treatment. F-18 fluorodeoxyglucose positron emission tomography shows promise in the diagnosis and treatment monitoring of patients who have lupus with neuropsychiatric involvement.

Ideomotor apraxia in agrammatic and logopenic variants of primary progressive aphasia.

There are few studies examining praxis in subjects with primary progressive aphasia. The aim of this study was to examine the pattern and severity of ideomotor apraxia in subjects with logopenic and agrammatic variants of primary progressive aphasia and to determine if the presence of ideomotor apraxia correlated with specific neuroanatomical structural abnormalities. Subjects with primary progressive aphasia were prospectively recruited and classified according to published criteria. Using the apraxia subtest of the Western Aphasia Battery, pattern and severity of ideomotor apraxia was examined in all subjects diagnosed with agrammatic and logopenic variants of primary progressive aphasia. The study included 47 subjects, 21 diagnosed with agrammatic variant of primary progressive aphasia and 26 with logopenic variant primary progressive aphasia. Subjects with agrammatic aphasia were older at onset than the logopenic variant (67.2 vs. 61.7 years, p = 0.02), but there was no difference in illness duration prior to evaluation. Those with logopenic aphasia showed more cognitive impairment on the Mini-Mental Status Examination (agrammatic = 26.7/30, logopenic = 22/30, p = 0.002), and a trend for more severe language impairment as measured by the Western Aphasia Battery-Aphasia Quotient (agrammatic = 82.3, logopenic = 75.2, p = 0.11). Strong correlations were found between Western Aphasia Battery-Aphasia Quotient and total apraxia, instrumental apraxia, and complex apraxia, while average to modest correlations were seen with upper limb apraxia and facial apraxia. After adjusting for age, mental status performance, and Western Aphasia Battery-Aphasia Quotient score, those with agrammatic aphasia had a higher degree of total apraxia (p = 0.004), facial apraxia (p = 0.03), instrumental apraxia (p = 0.0006), and complex apraxia (p = 0.0006) than those with logopenic aphasia. The agrammatic variant of primary progressive aphasia was associated with greater praxis deficits but less cognitive impairment than the logopenic variant. The presence of ideomotor apraxia was associated with grey matter loss in the left lateral premotor cortex with extension into the motor cortex. These findings suggest that although some affected areas in the agrammatic and logopenic variants of primary progressive aphasia overlap, there exists an area that is more affected in the agrammatic variant than the logopenic variant that accounts for the greater association of agrammatic aphasia with ideomotor apraxia.

Clinical and electrophysiologic variability in amyotrophic lateral sclerosis within a kindred harboring the C9ORF72 repeat expansion.

Our objective was to characterize the motor neuron disease features within a large c9FTD/ALS kindred. We analyzed clinical, electrophysiologic and neuropathologic data in a c9FTD/ALS kindred of Scandinavian ancestry. Results showed that of six family members affected, three had only ALS, two had FTD and one had FTD and ALS. Each patient with motor neuron disease had a different clinical presentation: one patient had only bulbar symptoms, one had bulbar and limb involvement, one had limb symptoms, and one had primarily upper motor neuron disease. Later in the course of disease, all ALS patients developed bulbar involvement and died from respiratory causes. Survival was uniformly short (two to five years). Electrophysiologic studies recorded progressive lower motor neuron dysfunction except in the patient with predominantly upper motor neuron features. In conclusion, this kindred demonstrates that the presentation of ALS within c9FTD/ALS families may vary considerably and electrophysiologic findings reflect this heterogeneity.

Wavelet coherence model for diagnosis of Alzheimer disease.

This article presents a wavelet coherence investigation of electroencephalograph (EEG) readings acquired from patients with Alzheimer disease (AD)  and healthy controls. Pairwise electrode wavelet coherence is calculated over each frequency band (delta, theta, alpha, and beta). For comparing the synchronization fraction of 2 EEG signals, a wavelet coherence fraction is proposed which is defined as the fraction of the signal time during which the wavelet coherence value is above a certain threshold. A one-way analysis of variance test shows a set of statistically significant differences in wavelet coherence between AD and controls. The wavelet coherence method is effective for studying cortical connectivity at a high temporal resolution. Compared with other conventional AD coherence studies, this study takes into account the time-frequency changes in coherence of EEG signals and thus provides more correlational details. A set of statistically significant differences was found in the wavelet coherence among AD and controls. In particular, temporocentral regions show a significant decrease in wavelet coherence in AD in the delta band, and the parietal and central regions show significant declines in cortical connectivity with most of their neighbors in the theta and alpha bands. This research shows that wavelet coherence can be used as a powerful tool to differentiate between healthy elderly individuals and probable AD patients.

Characterization of a family with c9FTD/ALS associated with the GGGGCC repeat expansion in C9ORF72.

BACKGROUND: The hexanucleotide repeat in the chromosome 9 open reading frame 72 (C9ORF72) gene was recently discovered as the underlying genetic cause of many families with frontotemporal dementia (FTD) and/or amyotrophic lateral sclerosis (ALS) linked to chromosome 9 (c9FTD/ALS). We report the clinical, neuropsychologic, and neuroimaging findings of a family with the C9ORF72 mutation and clinical diagnoses bridging the FTD, parkinsonism, and ALS spectrum. OBJECTIVE: To characterize the antemortem characteristics of a family with c9FTD/ALS associated with the GGGGCC repeat expansion in C9ORF72. DESIGN: Clinical series. SETTING: Tertiary care academic medical center. PATIENTS The members of a family affected by the mutation with features of FTD and/or ALS. MAIN OUTCOME MEASURES: Clinical, neuropsychologic, and neuroimaging assessments. RESULTS: All 3 examined subjects had the hexanucleotide expansion detected in C9ORF72. All had personality/behavioral changes early in the course of the disease. One case had levodopa-unresponsive parkinsonism, and 1 had ALS. Magnetic resonance imaging showed symmetric bilateral frontal, temporal, insular, and cingulate atrophy. CONCLUSIONS: This report highlights the clinical and neuroimaging characteristics of a family with c9FTD/ALS. Further studies are needed to better understand the phenotypical variability and the cliniconeuroimaging-neuropathologic correlations.

Characterization of frontotemporal dementia and/or amyotrophic lateral sclerosis associated with the GGGGCC repeat expansion in C9ORF72.

Numerous kindreds with familial frontotemporal dementia and/or amyotrophic lateral sclerosis have been linked to chromosome 9, and an expansion of the GGGGCC hexanucleotide repeat in the non-coding region of chromosome 9 open reading frame 72 has recently been identified as the pathogenic mechanism. We describe the key characteristics in the probands and their affected relatives who have been evaluated at Mayo Clinic Rochester or Mayo Clinic Florida in whom the hexanucleotide repeat expansion were found. Forty-three probands and 10 of their affected relatives with DNA available (total 53 subjects) were shown to carry the hexanucleotide repeat expansion. Thirty-six (84%) of the 43 probands had a familial disorder, whereas seven (16%) appeared to be sporadic. Among examined subjects from the 43 families (n = 63), the age of onset ranged from 33 to 72 years (median 52 years) and survival ranged from 1 to 17 years, with the age of onset <40 years in six (10%) and >60 in 19 (30%). Clinical diagnoses among examined subjects included behavioural variant frontotemporal dementia with or without parkinsonism (n = 30), amyotrophic lateral sclerosis (n = 18), frontotemporal dementia/amyotrophic lateral sclerosis with or without parkinsonism (n = 12), and other various syndromes (n = 3). Parkinsonism was present in 35% of examined subjects, all of whom had behavioural variant frontotemporal dementia or frontotemporal dementia/amyotrophic lateral sclerosis as the dominant clinical phenotype. No subject with a diagnosis of primary progressive aphasia was identified with this mutation. Incomplete penetrance was suggested in two kindreds, and the youngest generation had significantly earlier age of onset (>10 years) compared with the next oldest generation in 11 kindreds. Neuropsychological testing showed a profile of slowed processing speed, complex attention/executive dysfunction, and impairment in rapid word retrieval. Neuroimaging studies showed bilateral frontal abnormalities most consistently, with more variable degrees of parietal with or without temporal changes; no case had strikingly focal or asymmetric findings. Neuropathological examination of 14 patients revealed a range of transactive response DNA binding protein molecular weight 43 pathology (10 type A and four type B), as well as ubiquitin-positive cerebellar granular neuron inclusions in all but one case. Motor neuron degeneration was detected in nine patients, including five patients without ante-mortem signs of motor neuron disease. While variability exists, most cases with this mutation have a characteristic spectrum of demographic, clinical, neuropsychological, neuroimaging and especially neuropathological findings.

Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease.

Recently the senior author and his associates developed a spatiotemporal wavelet-chaos methodology for the analysis of electroencephalograms (EEGs) and their subbands for discovering potential markers of abnormality in Alzheimer disease (AD). In this study, fractal dimension (FD) is used for the evaluation of the dynamical changes in the AD brain. The approach presented in this study is based on the research ideology that nonlinear features, such as FD, may not show significant differences between the AD and the control groups in the band-limited EEG, but may manifest in certain subbands. First, 2 different FD algorithms for computing the fractality of EEGs are investigated and their efficacy for yielding potential mathematical markers of AD is compared. They are Katz FD (KFD) and Higuchi FD. Significant features in different loci and different EEG subbands or band-limited EEG for discrimination of the AD and the control groups are determined by analysis of variation. The most discriminative FD and the corresponding loci and EEG subbands for discriminating between AD and healthy EEGs are discovered. As KFD of all loci in the ß subband showed very high ability (P value <0.001) in discriminating between the groups, all KFDs are abstracted in 1 global KFD by averaging across loci in each of the 2 eyes-closed and eyes-open conditions. This leads to a more robust classification in terms of common variation of electrode positions than a classification based on separate KFDs of certain loci. Finally, based on the 2 global features separately and together, linear discriminant analysis is used to classify EEGs of AD and elderly normal patients. A high accuracy of 99.3% was obtained for the diagnosis of the AD based on the global KFD in the ß-band of the eyes-closed condition with a sensitivity of 100% and a specificity of 97.8%.

Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer's disease.

OBJECTIVES: This paper presents a comprehensive EEG study for interhemispheric, intrahemispheric, and distal coherence in Alzheimer's disease (AD) patients. The objective is to glean new insights into the brain of AD patients. METHODS: EEGs are obtained from 20 AD-probable patients and 7 healthy (control) subjects. Pair-wise electrode coherence is calculated over each frequency band (delta, theta, alpha, and beta). RESULTS: One-way ANOVA test shows a set of statistically significant differences in electrode coherence between AD and controls. In general, AD patients present a significant pattern of increase in the left intrahemispheric frontal coherence in the delta, theta, and alpha bands, an increase in the left intrahemispheric temporo-parietal coherence in all bands, and a decrease in the right temporo-parieto-central coherence in all bands. The increase in left temporo-parietal, temporo-central, and frontal coherence of AD patients in specific bands, being most pronounced in the theta band, could be attributed to compensatory mechanisms that attempt to make up for the decrease in memory and cognitive functions caused by the progression of AD. CONCLUSIONS: The coherence study presented in this paper shows a pattern of decrease in AD coherence, indicating a decline in cortical connectivity. Exceptions are seen in specific bands where an increase in coherence can be attributed to compensatory mechanisms. SIGNIFICANCE: The research shows that coherence studies have potentials in differentiating between healthy elderly and probable AD patients.

New diagnostic EEG markers of the Alzheimer's disease using visibility graph.

A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.