Automated medical diagnosis of alzheimer´s disease using an Efficient Net convolutional neural network

Producción CientíficaAlzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach—"fusion of end-to-end and transfer learning"—to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

EMOTIONAL ENHANCEMENT AND REPETITION EFFECTS DURING WORKING MEMORY IN PERSONS WITH MILD COGNITIVE IMPAIRMENT

This dissertation introduces a framework for understanding differences in how emotional enhancement effects might influence memory in aging adults and then summarizes the findings of three studies of how repetition effects and emotional enhancement effects influence working memory in older adults without cognitive impairment (NC), older adults with amnestic mild cognitive impairment (MCI), and older adults with mild Alzheimer’s disease (AD). In these experiments, individuals with AD showed cognitive impairment in terms of accuracy and reaction time, but individuals with MCI showed milder behavioral impairment that was confined to manipulations of working memory. Individuals with AD showed relative sparing of repetition effects in behavioral performance, and this sparing was linked to an altered cortical repetition effect using event-related potentials (ERPs). Repetition effects in MCI appear absent in emotional tasks that lack a working memory component, but are present in a neural repetition mechanism that is evoked in the presence of working memory. Finally, persons with MCI showed working memory processing similar to persons without impairment when working with stimuli of low arousal and positive hedonic valence, but when working with stimuli of high arousal and negative hedonic valence, their working memory processing more resembled the AD phenotype

AN FMRI STUDY OF DEFAULT MODE NETWORK CONNECTIVITY IN COMATOSE PATIENTS

Functional connectivity within a resting state network of the brain, termed the default mode network (DMN), has been suggested to represent the neural correlate o f the stream of consciousness. Altered states of consciousness where awareness is thought to be absent could provide insight into the function o f the DMN. Here I examined the functional connectivity in the DMN in both reversible and irreversible coma using fMRI. Twelve healthy control subjects and thirteen comatose patients following cardiac arrest were included in the study. DMN connectivity was observed in healthy controls and two patients who regained consciousness. DMN connectivity was absent in the eleven patients who failed to regain consciousness. Functional connectivity in the DMN is preserved in the comatose patients who regained consciousness but absent in those who did not recover consciousness indicating that potentially the DMN is necessary but not sufficient to support consciousness

Dynamics of large-scale brain activity in health and disease

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Cognition relies on the integration of information processed in widely distributed brain regions. Neuronal oscillations are thought to play an important role in the supporting local and global coordination of neuronal activity. This study aimed at investigating the dynamics of the ongoing healthy brain activity and early changes observed in patients with Alzheimer's disease (AD). Electro- and magnetoencephalography (EEG/MEG) were used due to high temporal resolution of these techniques. In order to evaluate the functional connectivity in AD, a novel algorithm based on the concept of generalized synchronization was improved by defining the embedding parameters as a function of the frequency content of interest. The time-frequency synchronization likelihood (TF SL) revealed a loss of fronto-temporal/parietal interactions in the lower alpha (8 10 Hz) oscillations measured by MEG that was not found with classical coherence. Further, long-range temporal (auto-) correlations (LRTC) in ongoing oscillations were assessed with detrended fluctuation analysis (DFA) on times scales from 1 25 seconds. Significant auto-correlations indicate a dependence of the underlying dynamical processes at certain time scales of separation, which may be viewed as a form of "physiological memory". We tested whether the DFA index could be related to the decline in cognitive memory in AD. Indeed, a significant decrease in the DFA exponents was observed in the alpha band (6 13 Hz) over temporo-parietal regions in the patients compared with the age-matched healthy control subjects. Finally, the mean level of SL of EEG signals was found to be significantly decreased in the AD patients in the beta (13 30 Hz) and in the upper alpha (10 13 Hz) and the DFA exponents computed as a measure of the temporal structure of SL time series were larger for the patients than for subjects with subjective memory complaint. The results obtained indicate that the study of spatio-temporal dynamics of resting-state EEG/MEG brain activity provides valuable information about the AD pathophysiology, which potentially could be developed into clinically useful indices for assessing progression of AD or response to medication

Frameworks to Investigate Robustness and Disease Characterization/Prediction Utility of Time-Varying Functional Connectivity State Profiles of the Human Brain at Rest

Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function. Large scale functional organization of the brain is often studied by viewing the brain as a complex, integrative network composed of spatially distributed, but functionally interacting, sub-networks that continually share and process information. Such whole-brain functional interactions, also referred to as patterns of functional connectivity (FC), are typically examined as levels of synchronous co-activation in the different functional networks of the brain. More recently, there has been a major paradigm shift from measuring the whole-brain FC in an oversimplified, time-averaged manner to additional exploration of time-varying mechanisms to identify the recurring, transient brain configurations or brain states, referred to as time-varying FC state profiles in this dissertation. Notably, prior studies based on time-varying FC approaches have made use of these relatively lower dimensional fMRI features to characterize pathophysiology and have also been reported to relate to demographic characterization, consciousness levels and cognition. In this dissertation, we corroborate the efficacy of time-varying FC state profiles of the human brain at rest by implementing statistical frameworks to evaluate their robustness and statistical significance through an in-depth, novel evaluation on multiple, independent partitions of a very large rest-fMRI dataset, as well as extensive validation testing on surrogate rest-fMRI datasets. In the following, we present a novel data-driven, blind source separation based multimodal (sMRI-fMRI) data fusion framework that uses the time-varying FC state profiles as features from the fMRI modality to characterize diseased brain conditions and substantiate brain structure-function relationships. Finally, we present a novel data-driven, deep learning based multimodal (sMRI-fMRI) data fusion framework that examines the degree of diagnostic and prognostic performance improvement based on time-varying FC state profiles as features from the fMRI modality. The approaches developed and tested in this dissertation evince high levels of robustness and highlight the utility of time-varying FC state profiles as potential biomarkers to characterize, diagnose and predict diseased brain conditions. As such, the findings in this work argue in favor of the view of FC investigations of the brain that are centered on time-varying FC approaches, and also highlight the benefits of combining multiple neuroimaging data modalities via data fusion

Prodromal Variability in Huntington\u27s Disease Progression and Resistance

Huntington’s disease (HD) is a neurodegenerative movement disorder caused by abnormal cytosine-adenine-guanine (CAG) expansion on the HTT gene. As both a proteinopathy and the most common PolyQ disease, HD shares key features with several disorders that disproportionately affect the growing elderly population in the United States, including delayed-onset, selective neuronal death, and protein misfolding. Across these conditions, there are few treatments and no known cures; however, their shared features suggest common underlying mechanisms, and delayed-onset hints at possible prevention or reversal. CAG-expansion-number and age are related to diagnosis and can be used to estimate age-of-onset for prodromal (pre-diagnosis) individuals, who possess the causal mutation but have not manifested diagnosis-associated motor symptoms. Over a decade before diagnosis, prodromal individuals differ from controls in brain structure and connectivity, cognition, and motor functioning. Although age and CAG-number account for most observed variability in HD-onset, persons with identical CAG-numbers often develop symptoms at different ages, indicating that additional genetic and environmental factors also mediate decline. Little is known about detrimental and protective genetic factors in HD. Studying prodromal progression can inform interventions by highlighting early prevention targets. This research leverages advanced multivariate techniques applied to legacy PREDICT-HD data to characterize brain structure, cognition, and motor functioning across prodromal HD and investigate genetic factors accounting for variability in these domains. Regarding brain structure, these experiments provide evidence for: regional co-occurrence in prodromal decline, early fronto-striatal degradation, dorso-ventral reduction gradients, and delayed atrophy in certain movement-related and subcortical regions. The genetic findings suggest a protective role of NTRK2 and identify NCOR1 and ADORA2B variants with early, CAG-independent detrimental effects on gray matter. Previously identified onset-delaying variants are also confirmed as CAG-independent modulators of brain structure and clinical functioning. Clinical findings highlight motor functioning as the best indicator of brain-structural integrity until the late prodrome and demonstrate that distinct regions coincide with cognitive compared to motor functioning; furthermore, regions that most align with clinical functioning vary at different prodromal stages

Intrinsic functional brain networks in health and disease

6 Introduction   6  6.1   Imaging  cognitive  processes  with  functional  magnetic  resonance  imaging   7  6.2   Imaging  the  brain’s  resting  state   8  6.3   Intrinsic  connectivity  networks  in  the  resting  state   9  6.4   Investigating  modulations  and  plasticity  of  intrinsic  connectivity  networks   12 7 Paper  1:   Towards  discovery  science  of  human  brain  function  (PNAS  2010)   14 8 Paper  2:   Repeated  pain  induces  adaptations  of  intrinsic  brain  activity  to  reflect  past  and  predict future pain  (Neuroimage  2011)   30 9 Paper  3:   Intrinsic  network  connectivity  reflects  consistency  of  synesthetic  experience

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial