Fractal dimension of cerebral white matter : A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age \ub1 standard deviation, 74.6 \ub1 6.9, education 7.9 \ub1 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age \ub1 standard deviation, 72.3 \ub1 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =.039), Symbol Digit Modalities Test scores (p-value =.039), and Trail Making Test Part A scores (p-value =.025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors

YesWireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Risk factors for cognitive decline in older people with type 2 diabetes

People with type 2 diabetes are at increased risk of age-related cognitive impairment. Previous literature has focused on case-control studies comparing rates of cognitive impairment in patients with and without diabetes. Investigations of potential risk factors for cognitive impairment (including those with increased prevalence in diabetes, such as macrovascular disease, and diabetes-specific factors such as hypoglycaemia) in study populations consisting exclusively of patients with type 2 diabetes have been largely neglected. Moreover, previous studies have failed to take advantage of the extensive characterisation and prospective nature of longitudinal cohort studies to investigate the relative predictive ability of a wider range of potential risk factors for cognitive decline. Using data from the prospective Edinburgh Type 2 Diabetes Study (ET2DS) the present thesis aimed (i) to determine associations of cognitive decline with macrovascular disease and with severe hypoglycaemia, and (ii) to compare a wider range of potential risk factors in their ability to predict cognitive decline. In 2006/2007, 1066 patients with type 2 diabetes (aged 60 to 75 years) attended the baseline ET2DS clinic and 831 returned for the follow-up at year 4. Subjects were extensively characterised for risk factor profiles at baseline, and at year 4 for incidence of severe hypoglycaemia. Socioeconomic status was estimated using postcode data. Scores on seven tests of age-sensitive ‘fluid’ cognitive function, which were administered at baseline and at year 4, were used to derive a general cognitive component (‘g’). A vocabulary-based test, administered at baseline, estimated pre-morbid ability. Findings are reported in three parts. 1.) Macrovascular disease and cognition: Subjects with higher levels of biomarkers indicative of subclinical macrovascular disease, including plasma N-terminal pro-brain natriuretic peptide and carotid intima-media thickness, had significantly steeper four-year cognitive decline, independent of traditional cardiovascular risk factors, stroke, socioeconomic status and estimated pre-morbid cognitive ability. For ankle-brachial pressure index, the association fell just short of statistical significance. Effect sizes were overall modest, with fully adjusted standardised beta coefficients ranging from 0.06 to -0.12. Little evidence was found for associations of the symptomatic markers of macrovascular disease with four-year change in cognitive function that was independent of participants’ pre-morbid ability and socioeconomic status. 2.) Severe hypoglycaemia and cognition: Subjects with lower cognitive ability at baseline were at two-fold increased risk of experiencing their first-ever incident severe hypoglycaemia during follow-up. The rate of four-year cognitive decline was significantly steeper in those exposed to hypoglycaemia compared with hypoglycaemia-free participants, independently of cardiovascular risk factors, microand macrovascular disease and of estimated pre-morbid cognitive ability. Effect sizes again were overall modest (Cohen’s d = 0.2 to 0.3 for statistically significant differences in four-year cognitive decline between subjects with and those without hypoglycaemia, following multivariable adjustment) 3.) Consideration of a wider range of risk factors and cognition: A stepwise linear regression model including a total of 15 metabolic and vascular risk factors identified inflammation, smoking and poorer glycaemic control (in addition to some of the subclinical markers of macrovascular disease) as predictive of a steeper four-year cognitive decline. Other traditional cardiovascular risk factors, diabetic retinopathy, clinical macrovascular disease and a baseline history of severe hypoglycaemia were not included in this model. The interpretation of the latter finding is limited, however, by the fact that the stepwise regression procedure may exclude true predictors from a model when they correlate with already included risk factors. This thesis has demonstrated associations of later-life cognitive decline in people with type 2 diabetes with markers of subclinical macrovascular disease and poor glycaemic control (including hypoglycaemia) as well as other cardiometabolic risk factors (inflammation, smoking). Findings suggest that associations are relatively weak and complex due to inter-relationships amongst risk factors, and indicate a role of pre-morbid ability and socioeconomic status (which as risk factors are difficult to modify) in the relationships of risk factors with cognitive decline. Future research including case-control studies to compare risk factor associations between people with type 2 diabetes and non-diabetic older adults and randomised controlled trials to evaluate potential causal effects of individual modifiable risk factors on cognitive decline, will help to evaluate the mechanisms underlying the observation that people with type 2 diabetes are at risk of cognitive impairment in later life

An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi

Multifractal analysis of deep white matter microstructural changes on MRI in relation to early-stage atherosclerosis

Multifractal analysis based on generalized concepts of fractals has been applied to evaluate biological tissues composed of complex structures. This type of analysis can provide a precise quantitative description of a broad range of heterogeneous phenomena. Previously, we applied multifractal analysis to describe heterogeneity in white matter signal fluctuation on T2-weighted MR images as a new method of texture analysis and established Δα as the most suitable index for evaluating white matter structural complexity (Takahashi et al. J. Neurol. Sci., 2004; 225: 33−37). Considerable evidence suggests that pathophysiological processes occurring in deep white matter regions may be partly responsible for cognitive deterioration and dementia in elderly subjects. We carried out a multifractal analysis in a group of 36 healthy elderly subjects who showed no evidence of atherosclerotic risk factors to examine the microstructural changes of the deep white matter on T2-weighted MR images. We also performed conventional texture analysis, i.e., determined the standard deviation of signal intensity divided by mean signal intensity (SD/MSI) for comparison with multifractal analysis. Next, we examined the association between the findings of these two types of texture analysis and the ultrasonographically measured intima–media thickness (IMT) of the carotid arteries, a reliable indicator of early carotid atherosclerosis. The severity of carotid IMT was positively associated with Δα in the deep white matter region. In addition, this association remained significant after excluding 12 subjects with visually detectable deep white matter hyperintensities on MR images. However, there was no significant association between the severity of carotid IMT and SD/MSI. These results indicate the potential usefulness of applying multifractal analysis to conventional MR images as a new approach to detect the microstructural changes of apparently normal white matter during the early stages of atherosclerosis