Towards more ecoefficient food production: MFA approach

The key for the sustainable development is dematerialisation and ecoefficiency. Applied to agriculture ecoefficiency means production of nutritionally better food by using less inputs and by reducing the environmental burden. In restricting the material throughput it is essential to identify the most voluminous material flows and to direct the measures to them. Improving ecoefficiency of the food production requires that the benefits and the inputs are quantified in an unambiguous way and that the inputs are estimated for the whole production chain. A comprehensive view of the whole system is necessary. The food system comprises four mutually linked loops: 1) the plant production 2) the livestock husbandry, 3) the food processing industry and 4) the human consumption. In the present paper MFA approach has been used to describe the system. A general framework for estimating and balancing the materials flow is outlined. The focus is on agriculture, specifically on the materials flow created by the biological metabolism of the animal husbandry. The holistic MFA approach provides means to evaluate environmental and economic consequences of the production. For the decision-makers the MFA approach is a tool to guide the development and to assess the progress towards increasing ecoefficiency within the food system. The results can be used in developing new sustainability indicators. Some of the possibilities are shortly discussed. The study is the first step in developing MFA methods to analyse and to monitor the materials flow of the Finnish food systems. It is a part of the project “The Materials Flow and Ecoefficiency of Agriculture and the Sustainable Compatibility of the Food Production” carried out in collaboration between the MTT - Agrifood Research Finland and the Thule Institute at the University of Oulu. The results are used also in compiling the Finnish physical input-output tables. The study, thus, contributes to the overall development of the materials flow accounting statistics

Towards more ecoefficient food production: MFA approach

The food flux comprises four mutually linked loops: 1)plant production, 2)livestock husbandry, 3) food processing industry and 4) human consumption. In the present paper MFA approach has been used to describe the system. A general framework and practical solutions for estimating and balancing the materials flow are outlined. The focus in this paper is agriculture

The association between distinct frontal brain volumes and behavioral symptoms in mild cognitive impairment, alzheimer's disease, and frontotemporal dementia

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features

Detecting frontotemporal dementia syndromes using MRI biomarkers

BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia

Brain volumes and cortical thickness on MRI in the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER)

BackgroundThe Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) was a multicenter randomized controlled trial that reported beneficial effects on cognition for a 2-year multimodal intervention (diet, exercise, cognitive training, vascular risk monitoring) versus control (general health advice). This study reports exploratory analyses of brain MRI measures.MethodsFINGER targeted 1260 older individuals from the general Finnish population. Participants were 60-77years old, at increased risk for dementia but without dementia/substantial cognitive impairment. Brain MRI scans were available for 132 participants (68 intervention, 64 control) at baseline and 112 participants (59 intervention, 53 control) at 2years. MRI measures included regional brain volumes, cortical thickness, and white matter lesion (WML) volume. Cognition was assessed at baseline and 1- and 2-year visits using a comprehensive neuropsychological test battery. We investigated the (1) differences between the intervention and control groups in change in MRI outcomes (FreeSurfer 5.3) and (2) post hoc sub-group analyses of intervention effects on cognition in participants with more versus less pronounced structural brain changes at baseline (mixed-effects regression models, Stata 12).ResultsNo significant differences between the intervention and control groups were found on the changes in MRI measures. Beneficial intervention effects on processing speed were more pronounced in individuals with higher baseline cortical thickness in Alzheimer's disease signature areas (composite measure of entorhinal, inferior and middle temporal, and fusiform regions). The randomization groupxtimexcortical thickness interaction coefficient was 0.198 (p=0.021). A similar trend was observed for higher hippocampal volume (groupxtimexhippocampus volume interaction coefficient 0.1149, p=0.085).ConclusionsThe FINGER MRI exploratory sub-study did not show significant differences between the intervention and control groups on changes in regional brain volumes, regional cortical thicknesses, or WML volume after 2years in at-risk elderly without substantial impairment. The cognitive benefits on processing speed of the FINGER intervention may be more pronounced in individuals with fewer structural brain changes on MRI at baseline. This suggests that preventive strategies may be more effective if started early, before the occurrence of more pronounced structural brain changes.Trial registrationClinicalTrials.gov, NCT01041989. Registered January 5, 2010

Evaluating combinations of diagnostic tests to discriminate different dementia types

INTRODUCTION: We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. METHODS: In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. RESULTS: Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. DISCUSSION: Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making