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Background There is less data available regarding the characteristics of cognitive impairment in patients with amyotrophic lateral sclerosis (ALS) in a population-based series. Methodology Patients with ALS incident in Piemonte, Italy, between 2009 and 2011 underwent an extensive neuropsychological battery. Cognitive status was classified as follows: normal cognition, frontotemporal dementia (ALS-FTD), executive cognitive impairment (ALS-ECI), non-executive cognitive impairment (ALS-NECI), behavioural impairment (ALS-Bi), non-classifiable cognitive impairment. We also assessed 127 age-matched and gender-matched controls identified through patients’ general practitioners. Results Out of the 281 incident patients, 207 (71.9%) underwent the neuropsychological testing; of these, 19 were excluded from the analysis due previous conditions affecting cognition. Ninety-one (49.7%) patients were cognitively normal, 23 (12.6%) had ALS-FTD, 36 (19.7%) ALS-ECI, 10 (5.5%) ALS-NECI, 11 (6.0%) ALS-Bi and 11 (6.0%) non-classifiable cognitive impairment, 1 had comorbid Alzheimer's disease. Patients with ALS-FTD were older, had a lower education level, and had a shorter survival than any other cognitive group. Of the nine cases with C9ORF72 mutation, six had ALS-FTD, two ALS-ECI and one was cognitively normal; one of the five patients with SOD1 mutations and one of the five patients with TARBDP mutations had ALS-Bi. Conclusions About 50% of Italian patients with ALS had some degree of cognitive impairment, in keeping with a previous Irish study, despite the largely different genetic background of the two populations. The lower educational attainment in patients with ALS-FTD indicated a possible role of cognitive reserve in ALS-related cognitive impairment. ALS-ECI and ALS-NECI may represent discrete cognitive syndromes in the continuum of ALS and FTD.

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18months and 56 MCI non-converters who had not converted to AD within 18months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers. ► We propose to combine MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. ► A high accuracy of 93.2% for AD classification and a high sensitivity of 91.5% (for MCI converters) for MCI classification. ► Each modality is indispensable for achieving good classification. ► CSF and PET have the highest complementary information and MRI and PET have the highest similar information for classification.

Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. However, these methods were assessed on different populations, making it difficult to compare their performance. In this paper, we evaluated the performance of ten approaches (five voxel-based methods, three methods based on cortical thickness and two methods based on the hippocampus) using 509 subjects from the ADNI database. Three classification experiments were performed: CN vs AD, CN vs MCIc (MCI who had converted to AD within 18months, MCI converters — MCIc) and MCIc vs MCInc (MCI who had not converted to AD within 18months, MCI non-converters — MCInc). Data from 81 CN, 67 MCInc, 39 MCIc and 69 AD were used for training and hyperparameters optimization. The remaining independent samples of 81 CN, 67 MCInc, 37 MCIc and 68 AD were used to obtain an unbiased estimate of the performance of the methods. For AD vs CN, whole-brain methods (voxel-based or cortical thickness-based) achieved high accuracies (up to 81% sensitivity and 95% specificity). For the detection of prodromal AD (CN vs MCIc), the sensitivity was substantially lower. For the prediction of conversion, no classifier obtained significantly better results than chance. We also compared the results obtained using the DARTEL registration to that using SPM5 unified segmentation. DARTEL significantly improved six out of 20 classification experiments and led to lower results in only two cases. Overall, the use of feature selection did not improve the performance but substantially increased the computation times. ► Alzheimer vs controls : high accuracies with whole-brain methods (up to 81% sensitivity - 95% specificity). ► For the detection of prodromal Alzheimer, the sensitivity was substantially lower. ► For the prediction of conversion, the accuracy was only slightly higher than chance.

The extant major psychiatric classifications DSM-IV and ICD-10 are purportedly atheoretical and largely descriptive. Although this achieves good reliability, the validity of a medical diagnosis is greatly enhanced by an understanding of the etiology. In an attempt to group mental disorders on the basis of etiology, five clusters have been proposed. We consider the validity of the fifth cluster, externalizing disorders, within this proposal. We reviewed the literature in relation to 11 validating criteria proposed by the Study Group of the DSM-V Task Force, in terms of the extent to which these criteria support the idea of a coherent externalizing spectrum of disorders. This cluster distinguishes itself by the central role of disinhibitory personality in mental disorders spread throughout sections of the current classifications, including substance dependence, antisocial personality disorder and conduct disorder. Shared biomarkers, co-morbidity and course offer additional evidence for a valid cluster of externalizing disorders. Externalizing disorders meet many of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster.

A dearth of population-based epidemiological research examines neuropsychiatric symptom (NPS) in sub-clinical populations across the spectrum from normal aging to mild cognitive impairment (MCI). The construct of mild behavioral impairment (MBI) describes the emergence of sustained and impactful NPS in advance of or in combination with MCI. This is the first epidemiological study to operationalize the recently published diagnostic criteria for MBI and determine prevalence estimates across the spectrum from cognitively normal to MCI. MBI was assessed in 1,377 older (age range 72–79 years; 52% male; MCI ;= 133; cognitively normal, but-at-risk = 397; cognitively healthy = 847). MBI was assessed in accordance with the ISTAART-AA diagnostic criteria for MBI using the neuropsychiatric inventory. 34.1% of participants met the criteria for MBI. High prevalence of MBI across the cognitive spectrum was reported (48.9% vs. 43.1% vs. 27.6%). Irrespective of level of cognitive impairment, impulse dyscontrol (33.8% vs. 28.7% vs. 17.2%) and decreased motivation (32.3% vs. 26.2% vs. 16.3%) were the most frequently met MBI domains. MBI was more prevalent in men (χ2 = 4.98, p = 0.026), especially the domains of decreased motivation and impulse dyscontrol. This study presents the first population-based prevalence estimates for MBI using the recently published ISTAART-AA diagnostic criteria. Findings indicate relatively high prevalence of MBI in pre-dementia clinical states and amongst cognitively healthy older adults. Findings were gender-specific, with MBI affecting more men than women. Knowing the estimates of these symptoms in the population is essential for understanding and differentiating the very early development of clinical disorders.

Operational definitions of cognitive impairment have varied widely in diagnosing mild cognitive impairment (MCI). Identifying clinical subtypes of MCI has further challenged diagnostic approaches because varying the components of the objective cognitive assessment can significantly impact diagnosis. Therefore, the authors investigated the applicability of diagnostic criteria for clinical subtypes of MCI in a naturalistic research sample of community elders and quantified the variability in diagnostic outcomes that results from modifying the neuropsychological definition of objective cognitive impairment. Cross-sectional and longitudinal study. San Diego, CA, Veterans Administration Hospital. Ninety nondemented, neurologically normal, community-dwelling older adults were initially assessed and 73 were seen for follow-up approximately 17 months later. Participants were classified via consensus diagnosis as either normally aging or having MCI via each of the five diagnostic strategies, which varied the cutoff for objective impairment and the number of neuropsychological tests considered in the diagnostic process. A range of differences in the percentages identified as MCI versus cognitively normal were demonstrated, ranging from 10–74%, depending on the classification criteria used. A substantial minority of individuals demonstrated diagnostic instability over time and across diagnostic approaches. The single domain nonamnestic subtype diagnosis was particularly unstable (e.g., prone to reclassification as normal at follow up). Our findings provide empirical support for a neuropsychologically derived operational definition of clinical subtypes of MCI and point to the importance of using comprehensive neuropsychological assessments. Diagnoses, particularly involving nonamnestic MCI, were variable over time. The applicability and utility of this particular MCI subtype warrants further investigation.

Cerebrovascular disease is the second most common cause of acquired cognitive impairment and dementia and contributes to cognitive decline in the neurodegenerative dementias. The current narrow definitions of vascular dementia should be broadened to recognise the important part cerebrovascular disease plays in several cognitive disorders, including the hereditary vascular dementias, multi-infarct dementia, post-stroke dementia, subcortical ischaemic vascular disease and dementia, mild cognitive impairment, and degenerative dementias (including Alzheimer's disease, frontotemporal dementia, and dementia with Lewy bodies). Here we review the current state of scientific knowledge on the subject of vascular brain burden. Important non-cognitive features include depression, apathy, and psychosis. We propose use of the term vascular cognitive impairment, which is characterised by a specific cognitive profile involving preserved memory with impairments in attentional and executive functioning. Diagnostic criteria have been proposed for some subtypes of vascular cognitive impairment, and there is a pressing need to validate and further refine these. Clinical trials in vascular cognitive impairment are in their infancy but support the value of therapeutic interventions for symptomatic treatment.

Clinical and epidemiologic research has focused on the identification of risk factors that may be modified in predementia syndromes, at a preclinical and early clinical stage of dementing disorders, with specific attention to the role of depression. Our goal was to provide an overview of these studies and more specifically to describe the prevalence and incidence of depression in individuals with mild cognitive impairment (MCI), the possible impact of depressive symptoms on incident MCI, or its progression to dementia and the possible mechanisms behind the observed associations. Prevalence and incidence of depressive symptoms or syndromes in MCI vary as a result of different diagnostic criteria and different sampling and assessment procedures. The prevalence of depression in individuals with MCI was higher in hospital-based studies (median: 44.3%, range: 9%–83%) than in population-based studies (median: 15.7%, range: 3%–63%), reflecting different referral patterns and selection criteria. Incidence of depressive symptoms varied from 11.7 to 26.6/100 person-years in hospital-based and population-based studies. For depressed normal subjects and depressed patients with MCI, the findings on increased risk of incident MCI or its progression to dementia were conflicting. These contrasting findings suggested that the length of the follow-up period, the study design, the sample population, and methodological differences may be central for detecting an association between baseline depression and subsequent development of MCI or its progression to dementia. Assuming that MCI may be the earliest identifiable clinical stage of dementia, depressive symptoms may be an early manifestation rather than a risk factor for dementia and Alzheimer disease, arguing that the underlying neuropathological condition that causes MCI or dementia also causes depressive symptoms. In this scenario, at least in certain subsets of elderly patients, late-life depression, MCI, and dementia could represent a possible clinical continuum.

A molecular test for Alzheimer's disease could lead to better treatment and therapies. We found 18 signaling proteins in blood plasma that can be used to classify blinded samples from Alzheimer's and control subjects with close to 90% accuracy and to identify patients who had mild cognitive impairment that progressed to Alzheimer's disease 2–6 years later. Biological analysis of the 18 proteins points to systemic dysregulation of hematopoiesis, immune responses, apoptosis and neuronal support in presymptomatic Alzheimer's disease.

A comprehensive investigation of the neuropsychological strengths and weaknesses of children with autism may help to better describe their cognitive abilities and to design appropriate interventions. To this end we compared the NEPSY-II profiles of 22 children with high-functioning autism spectrum disorders (HFASD) with those of 44 healthy control (HC) children 2:1 matched by gender, age, race and education. Results showed that only Visuospatial Processing was relatively spared in HFASD, while deficits were observed in Attention and Executive Functions, Language, Learning and Memory, and Sensorimotor Processing. Theory of Mind difficulties were observed in verbal tasks but not in the understanding of emotional contexts, suggesting that appropriate contextual cues might help emotion understanding in HFASD children. These widespread neuropsychological impairments reflect alterations in multiple cognitive domains in HFASD.