Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved

Analyzing Heterogeneity In Neuroimaging With Probabilistic Multivariate Clustering Approaches

Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis. In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data. The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimer’s Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimer’s disease

Imaging techniques in Alzheimer's disease: a review of applications in early diagnosis and longitudinal monitoring

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death.\nIn AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD.\nIn this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed.\nReduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure.\nSeveral biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.Pharmacolog

Overview of Good Clinical Practices, Barriers, and Gaps for Integrated Care

Albuquerque, M., Meira, B., Barros, R., Pavão, J. F., Lopes, H., & Valverde, A. M. H. (2023). Dementia Appraisal: Overview of Good Clinical Practices, Barriers, and Gaps for Integrated Care. International Journal of Geriatrics and Gerontology, 7(1), 1-22. [173]. https://doi.org/10.29011/2577-0748.100073Introduction: The increasing prevalence of dementia and mild cognitive impairment is a priority for health systems and society in the coming years. The aim of this study was to provide an overview of good clinical practices, barriers, and gaps for integrated dementia care. Methods: An electronic search was conducted on PubMed database for the last five years for articles relevant to the scope of the study, conducted in humans, written in Portuguese or English, and open access. National and international guidelines and consensus documents recognized in Europe were also included. Results: With increasing life expectancy and aging as major risk factors, the number of people living with dementia will become unsustainable for medical, social, and informal care. Ineffective care pathways lead to unnecessary medical interventions and suboptimal care. People with dementia should be involved in all stages of care and research. High-quality epidemiological data by disease severity and dementia subtype are needed. The development of novel technologies to improve clinical assessment of cognition and function that are sensitive and accurate in early stages of dementia and can be used in primary care is also an unmet need. A strategy to improve dementia care from diagnosis to end of life is lacking. Research into effective models of care and new treatment pathways with a more accurate selection of patients in early stages of the disease is crucial. Conclusions: There are currently several gaps in dementia care. Integrated care pathways, patient-centered approaches, and the establishment of a workforce based on a comprehensive and pragmatic framework are priorities that should be included in public health strategies.publishersversionpublishe

Converting Neuroimaging Big Data to information: Statistical Frameworks for interpretation of Image Driven Biomarkers and Image Driven Disease Subtyping

Large scale clinical trials and population based research studies collect huge amounts of neuroimaging data. Machine learning classifiers can potentially use these data to train models that diagnose brain related diseases from individual brain scans. In this dissertation we address two distinct challenges that beset a wider adoption of these tools for diagnostic purposes. The first challenge that besets the neuroimaging based disease classification is the lack of a statistical inference machinery for highlighting brain regions that contribute significantly to the classifier decisions. In this dissertation, we address this challenge by developing an analytic framework for interpreting support vector machine (SVM) models used for neuroimaging based diagnosis of psychiatric disease. To do this we first note that permutation testing using SVM model components provides a reliable inference mechanism for model interpretation. Then we derive our analysis framework by showing that under certain assumptions, the permutation based null distributions associated with SVM model components can be approximated analytically using the data themselves. Inference based on these analytic null distributions is validated on real and simulated data. p-Values computed from our analysis can accurately identify anatomical features that differentiate groups used for classifier training. Since the majority of clinical and research communities are trained in understanding statistical p-values rather than machine learning techniques like the SVM, we hope that this work will lead to a better understanding SVM classifiers and motivate a wider adoption of SVM models for image based diagnosis of psychiatric disease. A second deficiency of learning based neuroimaging diagnostics is that they implicitly assume that, `a single homogeneous pattern of brain changes drives population wide phenotypic differences\u27. In reality it is more likely that multiple patterns of brain deficits drive the complexities observed in the clinical presentation of most diseases. Understanding this heterogeneity may allow us to build better classifiers for identifying such diseases from individual brain scans. However, analytic tools to explore this heterogeneity are missing. With this in view, we present in this dissertation, a framework for exploring disease heterogeneity using population neuroimaging data. The approach we present first computes difference images by comparing matched cases and controls and then clusters these differences. The cluster centers define a set of deficit patterns that differentiates the two groups. By allowing for more than one pattern of difference between two populations, our framework makes a radical departure from traditional tools used for neuroimaging group analyses. We hope that this leads to a better understanding of the processes that lead to disease and also that it ultimately leads to improved image based disease classifiers

Proceedings of the Fourth International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Biological Shape Variability Modeling (MFCA 2013), Nagoya, Japan

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the first edition of this workshop in 2006, second edition in New-York in 2008, the third edition in Toronto in 2011, the forth edition was held in Nagoya Japan on September 22 2013

Asociace metabolických faktorů a strukturálních změn mozku u psychotických poruch

Úvod: Schizofrenie (SZ) a bipolární afektivní porucha (BD) jsou často správně diagnostikovány až několik let po prvních projevech psychické poruchy. Neurozobrazovací techniky by mohly poskytnout podporu při časné diferenciální diagnostice, nicméně širší využití v klinické praxi je komplikováno výraznou heterogenitou výsledků mezi jednotlivými studiemi. Obezita, dyslipidémie a inzulinová rezistence (IR) jsou časté komorbidity psychických poruch, které mohou přispívat k heterogenitě nálezů/mozkovým změnám. Studovali jsme BD a SZ v různých stádiích onemocnění a zkoumali jsme účinky metabolických parametrů na struktury a funkce mozku. Metody: Ve Studii 1 jsme pomocí algoritmu strojového učení odhadovali individuální věk mozku ze snímků magnetické rezonance u 120 pacientů s první epizodou schizofrenie (FES) a u 114 kontrol. Počítali jsme BrainAGE skóre, jež tvoří rozdíl mezi odhadovaným věkem mozku a chronologickým věkem. Za účelem lokalizace mozkových abnormit asociovaných s obezitou nebo psychózou jsme provedli voxel-based morfometrii (VBM). Ve Studii 2 jsme za pomoci biochemických a kognitivní dat od 100 euthymních pacientů s BD zkoumali souvislost mezi inzulínovou rezistenci (IR) a pamětí. Ve Studii 3 jsme se zaměřili na neurostrukturální rozdíly mezi časnými stádii SZ (43 pacientů), BD (96 potomků...Background: Schizophrenia (SZ) and bipolar disorders (BD) are often correctly diagnosed only years after the initial manifestations. Brain imaging may provide support for early differential diagnosis, but is complicated by marked heterogeneity of results between studies. Obesity, dyslipidemia and insulin resistance (IR) are frequent in psychiatric disorders and may contribute to brain alterations/heterogeneity. We studied BD and SZ in different stages of illness and specifically investigated the effects of metabolic parameters on brain structure and function. Methods: In Study 1 we used machine learning algorithm to estimate the individual brain age from MRI scans of 120 participants with first episode schizophrenia (FES) and 114 controls. We calculated the brain age gap (BrainAGE) score by subtracting the chronological age from the brain age. We also performed voxel-based morphometry (VBM) study to localize obesity or psychosis related pathology. In Study 2, we acquired biochemical and cognitive measures from 100 euthymic BD patients and explored the association between IR and memory. In Study 3, we explored differences in BrainAGE in early stages of SZ (43 participants) or BD (96 offspring of BD parents) and healthy controls (HC). In Study 4, we performed MRI cerebellar volume analyses on 648...Department of Psychiatry and Medical Psychology - Department of PsychiatryKlinika psychiatrie a lékařské psychologie - klinika psychiatrieThird Faculty of Medicine3. lékařská fakult

An insight into the brain of patients with type-2 diabetes mellitus and impaired glucose tolerance using multi-modal magnetic resonance image processing

The purpose of this thesis was to investigate brain anatomy and physiology of subjects with impaired glucose tolerance (IGT - 12 subjects), type-2 diabetes (T2DM - 17 subjects) and normoglycemia (16 subjects) using multi-modal magnetic resonance imaging (MRI) at 3T. Perfusion imaging using quantitative STAR labeling of arterial regions (QUASAR) arterial spin labeling (ASL) was the core dataset. Optimization of the post-processing methodology for this sequence was performed and the outcome was used for hemodynamic analysis of the cohort. Typical perfusion-related parameters, along with novel hemodynamic features were quantified. High-resolution structural, angiographic and carotid flow scans were also acquired and processed. Functional acquisitions were repeated following a vasodilating stimulus. Differences between the groups were examined using statistical analysis and a machine-learning framework. Hemodynamic parameters differing between the groups emerged from both baseline and post-stimulus scans for T2DM and mainly from the post-stimulus scan for IGT. It was demonstrated that quantification of not-typically determined hemodynamic features could lead to optimal group-separation. Such features captured the pattern of delayed delivery of the blood to the arterial and tissue compartments of the hyperglycemic groups. Alterations in gray and white matter, cerebral vasculature and carotid blood flow were detected for the T2DM group. The IGT cohort was structurally similar to the healthy cohort but demonstrated functional similarities to T2DM. When combining all extracted MRI metrics, features driving optimal separation between different glycemic conditions emerged mainly from the QUASAR scan. The only highly discriminant non-QUASAR feature, when comparing T2DM to healthy subjects, emerged from the cerebral angiogram. In this thesis, it was demonstrated that MRI-derived features could lead to potentially optimal differentiation between normoglycemia and hyperglycemia. More importantly, it was shown that an impaired cerebral hemodynamic pattern exists in both IGT and T2DM and that the IGT group exhibits functional alterations similar to the T2DM group

Pilot study for subgroup classification for autism spectrum disorder based on dysmorphology and physical measurements in Chinese children

Poster Sessions: 157 - Comorbid Medical Conditions: abstract 157.058 58BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder affecting individuals along a continuum of severity in communication, social interaction and behaviour. The impact of ASD significantly varies amongst individuals, and the cause of ASD can originate broadly between genetic and environmental factors. Objectives: Previous ASD researches indicate that early identification combined with a targeted treatment plan involving behavioural interventions and multidisciplinary therapies can provide substantial improvement for ASD patients. Currently there is no cure for ASD, and the clinical variability and uncertainty of the disorder still remains. Hence, the search to unravel heterogeneity within ASD by subgroup classification may provide clinicians with a better understanding of ASD and to work towards a more definitive course of action. METHODS: In this study, a norm of physical measurements including height, weight, head circumference, ear length, outer and inner canthi, interpupillary distance, philtrum, hand and foot length was collected from 658 Typical Developing (TD) Chinese children aged 1 to 7 years (mean age of 4.19 years). The norm collected was compared against 80 ASD Chinese children aged 1 to 12 years (mean age of 4.36 years). We then further attempted to find subgroups within ASD based on identifying physical abnormalities; individuals were classified as (non) dysmorphic with the Autism Dysmorphology Measure (ADM) from physical examinations of 12 body regions. RESULTS: Our results show that there were significant differences between ASD and TD children for measurements in: head circumference (p=0.009), outer (p=0.021) and inner (p=0.021) canthus, philtrum length (p=0.003), right (p=0.023) and left (p=0.20) foot length. Within the 80 ASD patients, 37(46%) were classified as dysmorphic (p=0.00). CONCLUSIONS: This study attempts to identify subgroups within ASD based on physical measurements and dysmorphology examinations. The information from this study seeks to benefit ASD community by identifying possible subtypes of ASD in Chinese population; in seek for a more definitive diagnosis, referral and treatment plan.published_or_final_versio