Connectomics: A new paradigm for understanding brain disease

Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

Connectomics Sheds New Light on Alzheimer’s Disease

The human brain is a complex system able to continuously adapt. How and where brain activity is modulated by behavior can be studied with functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique with excellent spatial resolution and whole-brain coverage. FMRI scans of healthy adults completing a variety of behavioral tasks have greatly contributed to our understanding of the functional role of individual brain regions. However, by statistically analyzing each region independently, these studies ignore that brain regions act in concert rather than in unison. Thus, many studies since have instead examined how brain regions interact. Surprisingly, structured interactions between distinct brain regions not only occur during behavioral tasks but also while a subject rests quietly in the MRI scanner. Multiple groups of regions interact very strongly with each other and not only do these groups bear a striking resemblance to the sets of regions co-activated in tasks, but many of these interactions are also progressively disrupted in neurological diseases. This suggests that spontaneous fluctuations in activity can provide novel insights into fundamental organizing principles of the human brain in health and disease. Many techniques to date have segregated regions into spatially distinct networks, which ignores that any brain region can take part in multiple networks across time. A more natural view is to estimate dynamic brain networks that allow flexible functional interactions (or connectivity) over time. The estimation and analysis of such dynamic functional interactions is the subject of this dissertation. We take the perspective that dynamic brain networks evolve in a low-dimensional space and can be described by a small number of characteristic spatiotemporal patterns. Our proposed approaches are based on well-established statistical methods, such as principal component analysis (PCA), sparse matrix decompositions, temporal clustering, as well as a multiscale analysis by novel graph wavelet designs. We adapt and extend these methods to the analysis of dynamic brain networks. We show that PCA and its higher-order equivalent can identify co-varying functional interactions, which reveal disturbed dynamic properties in multiple sclerosis and which are related to the timing of stimuli for task studies, respectively. Further we show that sparse matrix decompositions provide a valid alternative approach to PCA and improve interpretability of the identified patterns. Finally, assuming an even simpler low-dimensional space and the exclusive temporal expression of individual patterns, we show that specific transient interactions of the medial prefrontal cortex are disturbed in aging and relate to impaired memory.

Many modern control systems become gradually more complicated and, consequently, the approach to control design approaches is both difficulty and complex. Moreover, if such a complex interconnected system is subjected to non‐smooth nonlinearities in the actuator, then unexpected difficulties, degradation or, even worse, instability will arise in the system performance. Therefore, a new decentralized sliding mode control (DSMC) approach for such a class of complex interconnected systems subjected to non‐smooth (deadzone) nonlinearities is proposed in this paper. Based on sliding mode control (SMC) theory, the proposed DSMC laws guarantee the global reaching condition of the sliding mode in uncertain complex interconnected systems with deadzone nonlinearities, that is, they can ensure that the sliding mode is reached in finite time and with prescribed transient behavior. In the sliding mode, the investigated uncertain complex interconnected system with deadzone nonlinearities in the actuator still are insensitive to system uncertainties and external disturbances. The proposed DSMC laws can work effectively for uncertain complex interconnected systems either with or without deadzone nonlinearities in the actuator. However, this cannot be guaranteed by the traditional DSMC design for systems without input deadzone nonlinearities. Furthermore, the sliding motion can be controlled to converge within a specified exponential speed. Finally, two illustrative examples with a series of computer simulations are presented to demonstrate the effectiveness of the proposed DSMC laws.

Simple SummaryCerebral perfusion provides important information about capillary-level hemodynamics of the brain parenchyma, especially in conditions such as an ischemic stroke, brain tumors, and inflammation. The assessment of brain perfusion in dogs has been performed using computed tomography (CT), magnetic resonance (MR), sonography, single-photon emission computed tomography (SPECT), and 2-fluoro-2-deoxy-D-glucose positron emission tomography. However, these methods, except for CT, have limitations and difficulties in obtaining quantitative values. In addition, some of them are cost-prohibitive, particularly SPECT and MR imaging. CT scanners are widely available in clinical hospitals, making CT-based perfusion techniques more accessible for studies. The purpose of this study was to investigate the normal range of perfusion determined via CT in the brains of healthy dogs and to compare values between white matter and gray matter, differences in aging, and each hemisphere. The results of our study showed that the cerebral blood volume and cerebral blood flow values of white matter in the frontal, temporal, parietal, and occipital lobes were significantly lower than gray matter values. No significant differences were observed in the age-related variations or between the left and right hemispheres in other brain regions. In addition, quantitative values of CT perfusion parameters in a specific brain region were presented. This information provided by our study might contribute to a better understanding of brain perfusion in healthy dogs, potentially leading to the earlier diagnosis and improved treatment of brain diseases.Brain computed tomography (CT) perfusion is a technique that allows for the fast evaluation of cerebral hemodynamics. However, quantitative studies of brain CT perfusion in veterinary medicine are lacking. The purpose of this study was to investigate the normal range of perfusion determined via CT in brains of healthy dogs and to compare values between white matter and gray matter, differences in aging, and each hemisphere. Nine intact male beagle dogs were prospectively examined using dynamic CT scanning and post-processing for brain perfusion. Regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), mean transit time, and time to peak were calculated. Tissue ROIs were drawn in the gray matter and white matter of the frontal, temporal, parietal, and occipital lobes; caudate nucleus; thalamus; piriform lobe; hippocampus; and cerebellum. Significant differences were observed between the white matter regions and gray matter regions for rCBV and rCBF (p < 0.05). However, no significant differences were identified between hemispheres and between young and old groups in brain regions. The findings obtained in this study involving healthy beagle dogs might serve as a reference for regional CT perfusion values in specific brain regions. These results may aid in the characterization of various brain diseases in dogs.

While astrocyte heterogeneity is an important feature of the healthy brain, less is understood about spatiotemporal heterogeneity of astrocytes in brain disease. Spinocerebellar ataxia type 1 (SCA1) is a progressive neurodegenerative disease caused by a CAG repeat expansion in the gene Ataxin1 (ATXN1). We characterized astrocytes across disease progression in the four clinically relevant brain regions, cerebellum, brainstem, hippocampus, and motor cortex, of Atxn1154Q/2Q mice, a knock-in mouse model of SCA1. We found brain region-specific changes in astrocyte density and GFAP expression and area, early in the disease and prior to neuronal loss. Expression of astrocytic core homeostatic genes was also altered in a brain region-specific manner and correlated with neuronal activity, indicating that astrocytes may compensate or exacerbate neuronal dysfunction. Late in disease, expression of astrocytic homeostatic genes was reduced in all four brain regions, indicating loss of astrocyte functions. We observed no obvious correlation between spatiotemporal changes in microglia and spatiotemporal astrocyte alterations, indicating a complex orchestration of glial phenotypes in disease. These results support spatiotemporal diversity of glial phenotypes as an important feature of the brain disease that may contribute to SCA1 pathogenesis in a brain region and disease stage-specific manner.

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Recent developments in magnetic resonance (MR) imaging have opened up new avenues in the investigation of cardiovascular physiology. Inherent signal intensity of any tissue on MR images depends largely on proton concentration as well as longitudinal (T1) and transverse (T2) relaxation times. Myocardial contrast can be manipulated by using specific MR pulse sequences which are selectively sensitive to differences in any one of these parameters. Paramagnetic metal complexes are used as contrast media in MR imaging to enhance the inherent contrast. Contrast media in MR imaging are not directly visible but change the magnetic properties of other nuclei in close proximity, such as those of the water hydrogen. The signal of water can be altered by the contrast medium in different ways, either by changing the relaxation times or through bulb susceptibility effects, or both. The role of MR contrast media for quantitative characterization of ischemic heart disease has advanced considerably in the past 10 years. Conventional MR imaging techniques following the administration of contrast media are useful for identifying and sizing myocardial infarctions and for distinguishing between occlusive and reperfused myocardial infarctions as well as reversible (stunned) and irreversible injuries. Recent results suggest that contrast-enhanced MR imaging can also be used to identify dead cells in reperfused ischemically injured myocardium. The recently developed fast MR imaging techniques, with the aid of MR contrast media as a perfusion indicator, may be useful in estimating regional myocardial perfusion and blood volume. The assessment of capillary circulation or myocardial perfusion may be used for evaluating the extent of hypoperfusion and treatment efficacy. Experimental and clinical perfusion studies indicate that perfusion-sensitive MR imaging detects compromised myocardium (area at risk). Combining myocardial perfusion imaging with the anatomic and functional information provided by other MR imaging sequences could make MR imaging a comprehensive noninvasive technique for the evaluation of ischemic heart disease.

While interconnected systems proliferate in nowadays computing and communications technology there are few reports investigating their security. In this paper, new efficient models are developed and evaluated for analyzing the security-related behavior of computing architectures and networks comprising complex interconnected communication systems adapted towards a generalized analysis. These models, based on Markov processes, allow calculation of critical security factors, related to intrusion detection, of such interconnected systems components and the evaluation of the associated security mechanisms. Although, at this step two interconnected systems are analyzed, the systematic model introduced allows for a generalized model of N interconnected systems. We herein show the principles of such an analysis. Security parameters calculation and Security mechanisms evaluation may support the risk analysis and the decision making process in resolving the trade-offs between security and quality of service characteristics corresponding to the complex interconnected computing and communication systems.

One of the major issues hindering a comprehensive connectivity model for the human brain is the difficulty in linking Magnetic Resonance Imaging (MRI) measurements to anatomical evidence produced by histological methods. In vivo and postmortem neuroimaging methodologies are still largely incompatible in terms of sample size, scale, and resolution. To help bridge the hiatus between different approaches we have established a program that characterizes the brain of individual subjects, combining MRI with postmortem neuroanatomy. The direct correlation of MRI and histological features is possible, because registered images from different modalities represent the same regions in the same brain. Comparisons are also facilitated by large-scale, digital microscopy techniques that afford images of the whole-brain sections at cellular resolution. The goal is to create a neuroimaging catalog representative of discrete age groups and specific neurological conditions. Individually, the datasets allow for investigating the relationship between different modalities; combined, they provide sufficient predictive power to inform analyses and interpretations made in the context of non-invasive studies of brain connectivity and disease.

For decades, magnetic resonance imaging has shown overwhelming utility in the diagnosis and monitoring of diseases and disorders. In the human brain, it has been used to assess changes in brain structure and function in both healthy and unhealthy individuals and across patient cohorts. Contrast in structural images has been derived from proton density, and relaxation based (T1, T2 and T2*) processes. Moreover, methods such as perfusion, diffusion and functional imaging have provided complementary information in the assessment of neurodegenerative diseases and disorder affecting the central nervous system. With the availability of ultra-high field imaging, new contrast mechanisms are able to provide additional information that can be derived from magnetic resonance imaging data. In particular, not only the magnitude of the magnetic resonance imaging signal contains important information, but also the signal phase, which is highly sensitive to changes in magnetic properties of tissues and the effect scales with field strength. In quantitative susceptibility mapping, a method that is still under development, phase images allow the derivation of susceptibility maps. These maps can contain vital information about iron deposition, calcification and microbleeds, and changes in tissue microstructure.nThe overall aim of my research was to improve the utility of the approach by analysing quantitative susceptibility maps derived from multi-echo gradient recalled echo magnetic resonance imaging signals. I made three significant advances. First, I used quantitative susceptibility mapping at ultra-high field and studied the temporal trends in magnetic susceptibility using signal compartmentalisation. I found the trends in magnetic susceptibility as a function of echo time are influenced by tissue microstructure differences. This information is potentially useful in identifying changes in tissue microstructure in the human brain.nSecond, I investigated how changes in temporal magnetic susceptibility change as a function of magnetic field strength of the MRI scanner. I found the processing pipeline and field strength to affect signal compartmentalisation, however consistent results can be generated provided a distinct processing pipeline is used. nThird, I studied how signal compartments could be used to parcellate cortical regions in the human brain. I examined the use of single and multiple orientation quantitative susceptibility mapping methods. I found cortical regions can potentially be parcellated using signal compartmentalisation of the multiple echo gradient recalled echo MRI signal. My work may lead to full parcellation of the human brain and help explore how parcellated brain regions play a role in brain development and how they change with brain diseases and disorders.nn

Neurovascular alterations are increasingly recognized as a key feature of many brain diseases. They can manifest as a reduction in resting cerebral blood flow or cerebrovascular reactivity (CVR) in the whole brain or in specific regions, depending on the underlying condition. Neurovascular impairment is observed in hypertension, Alzheimer’s disease, stroke, multiple sclerosis and cerebral small vessel disease. Magnetic resonance imaging (MRI)-derived CVR mapping is a reliable marker of vascular dysfunction and has been performed mainly at standard functional MRI (fMRI) resolutions of 2–3 mm using the blood oxygen level dependent (BOLD) contrast. However, vascular alterations may occur at a finer scale (i.e., in the capillary bed) which would be better characterized with smaller voxel sizes. Capillaries in gray matter deliver oxygen and glucose to neural tissue and are arranged in a mesh structure, with variable density across the cortical depth. Given that the human cortex is, on average, 2.5 mm thick, submillimetric voxel sizes are effective in increasing the spatial specificity of measurements of hemodynamic and metabolic changes. Novel MRI sequences offer the possibility to map physiological parameters at high resolution with relatively simple experimental setups. In particular, pairing the BOLD acquisition with a contrast sensitive to blood volume changes, while administering a mild hypercapnic challenge, allows for simultaneous mapping of CVR, cerebral metabolic rate of oxygen consumption and other relevant parameters at a high resolution and can be performed at the clinical field strength of 3 T. We propose that this approach will help provide crucial insights into vascular impairment.

Small-animal positron emission tomography as a tool for neuropharmacology

Scavenger receptor class B type I expression and elemental analysis in cerebellum and parietal cortex regions of the Alzheimer's disease brain

Engineering nanoprobes for magnetic resonance imaging of brain diseases