Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease

Integrated late onset Alzheimer's disease (LOAD) susceptibility genes: Cholesterol metabolism and trafficking perspectives

Late onset Alzheimer's disease (LOAD) is the most common type of dementia and is characterized by decreased amyloid-β (Aβ) clearance from the brain. Cholesterol regulates the production and clearance of Aβ. Genome-wide association study (GWAS) suggests that at least 20 genes are associated with LOAD. The genes APOE, CLU, SORL1, PICALM, and BIN1 have a relatively high LOAD susceptibility. Additional experimental and bioinformatic approaches to integrate data from genetics, epigenetics, and molecular networks may further increase our understanding of LOAD in relation to cholesterol metabolism and trafficking.

Hippocampal Volumetry as a Biomarker for Dementia in People with Low Education

Background/Aims

To evaluate the relationship between hippocampal volume and cognitive decline in patients with dementia due to probable Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) and education, and the possible relationship between cognitive reserve and education in this population.

Methods

From February 2013 to October 2015, 76 patients (25 men, 51 women) were classified according to the NIA-AA diagnostic criteria. We used two 3.0-tesla MRI scanners and performed manual hippocampal volumetry.

Results

Twenty-six patients were found to have AD, 20 aMCI and 30 had normal aging (NA). The mean normalized hippocampal volume in age-, sex- and education (years)-matched subjects was 2.38 ± 0.51 cm³ in AD (p < 0.001), 2.91 ± 0.78 cm³ in aMCI (p = 0.019) and 3.07 ± 0.76 cm³ in NA.

Conclusion

Psychometric test (MMSE and MoCA) scores had a good to strong positive correlation with statistically significant differences in the entire population and healthy subjects but not among dementia patients and lower educational level groups. The patients with low education had greater hippocampal volumes, which is in line with the cognitive reserve theory; lower-educated individuals can tolerate less neuropathology and will thus show less atrophy at a similar level of cognitive performance than higher-educated subjects.

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence

Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD.

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer's disease

PURPOSE

It has been suggested that neuronal energy metabolism may be involved in Alzheimer's disease (AD). In this view, the finding of increased cerebrospinal fluid (CSF) lactate levels in AD patients has been considered the result of energetic metabolism dysfunction. Here, we investigated the relationship between neuronal energy metabolism, as measured via CSF lactate levels, and cerebral glucose metabolism, as stated at the 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography ([18F]FDG PET) in AD patients.

METHODS

AD patients underwent lumbar puncture to measure CSF lactate levels and [18F]FDG PET to assess brain glucose metabolism. CSF and PET data were compared to controls. Since patients were studied at rest, we specifically investigated brain areas active in rest-condition owing to the Default Mode Network (DMN). We correlated the CSF lactate concentrations with the [18F]FDG PET data in brain areas owing to the DMN, using sex, age, disease duration, Mini Mental State Examination, and CSF levels of tau proteins and beta-amyloid as covariates.

RESULTS

AD patients (n = 32) showed a significant increase of CSF lactate levels compared to Control 1 group (n = 28). They also showed brain glucose hypometabolism in the DMN areas compared to Control 2 group (n = 30). Within the AD group we found the significant correlation between increased CSF lactate levels and glucose hypometabolism in Broadman areas (BA) owing to left medial prefrontal cortex (BA10, mPFC), left orbitofrontal cortex (BA11, OFC), and left parahippocampal gyrus (BA 35, PHG).

CONCLUSION

We found high CSF levels of lactate and glucose hypometabolism within the DMN in AD patients. Moreover, we found a relationship linking the increased CSF lactate and the reduced glucose consumption in the left mPFC, OFC and PHG, owing to the anterior hub of DMN. These findings could suggest that neural glucose hypometabolism may affect the DMN efficiency in AD, also proposing the possible role of damaged brain energetic machine in impairing DMN.

Brain Connectomics' Modification to Clarify Motor and Nonmotor Features of Myotonic Dystrophy Type 1

The adult form of myotonic dystrophy type 1 (DM1) presents with paradoxical inconsistencies between severity of brain damage, relative preservation of cognition, and failure in everyday life. This study, based on the assessment of brain connectivity and mechanisms of plasticity, aimed at reconciling these conflicting issues. Resting-state functional MRI and graph theoretical methods of analysis were used to assess brain topological features in a large cohort of patients with DM1. Patients, compared to controls, revealed reduced connectivity in a large frontoparietal network that correlated with their isolated impairment in visuospatial reasoning. Despite a global preservation of the topological properties, peculiar patterns of frontal disconnection and increased parietal-cerebellar connectivity were also identified in patients' brains. The balance between loss of connectivity and compensatory mechanisms in different brain networks might explain the paradoxical mismatch between structural brain damage and minimal cognitive deficits observed in these patients. This study provides a comprehensive assessment of brain abnormalities that fit well with both motor and nonmotor clinical features experienced by patients in their everyday life. The current findings suggest that measures of functional connectivity may offer the possibility of characterizing individual patients with the potential to become a clinical tool.

Structural and resting-state MRI detects regional brain differences in young and mid-age healthy APOE-e4 carriers compared with non-APOE-e4 carriers

The presence of the e4 allele of the apolipoprotein E (APOE) gene is the best-known genetic risk factor for Alzheimer's disease. In this study, we investigated the link between functional and behavioural differences and regional brain volume and cortical thickness differences in those who carry the e4 allele (e4+) and those who only carry the e3 allele (e3/e3). We studied these genotype populations in two age groups: a young group (average age, 21 years) and a mid-age group (average age, 50 years). High-resolution T1 -weighted MRI scans were analysed with Freesurfer to measure regional white matter brain volume and cortical thickness differences between genotype groups at each age. These data were correlated with behavioural findings in the same cohort. Resting-state MRI was also conducted to identify differences in underlying brain functional connectivity. We found that there was a positive correlation between the thickness of the parahippocampal cortex in young e4+ individuals and performance on an episodic memory task. Young e4+ individuals also showed a positive correlation between white matter volume in the left anterior cingulate and performance on a covert attention task. At mid-age, e4+ individuals had structural differences relative to e3/e3 individuals in these areas: the parahippocampal cortex was thicker and white matter volume in the left anterior cingulate was greater than in e3/e3 individuals. We discuss the possibility that an over-engagement with these regions by e4+ individuals in youth may have a neurogenic effect that is observable later in life. The cuneus appears to be an important region for APOE-driven differences in the brain, with greater functional connectivity among young e3/e3 individuals and greater white matter volume in young e4+ individuals.

Graph theoretical analysis of functional networks and its relationship to cognitive decline in patients with carotid stenosis

Significant carotid stenosis compromises hemodynamics and impairs cognitive functions. The interplay between these changes and brain connectivity has rarely been investigated. We aimed to discover the changes of functional connectivity and its relation to cognitive decline in carotid stenosis patients. Twenty-seven patients with unilateral carotid stenosis (≥60%) and 20 age- and sex-matched controls underwent neuropsychological tests and resting-state functional magnetic resonance imaging. The patients also received perfusion magnetic resonance imaging. The relationships between cognitive function and functional networks among the patients and controls were evaluated. Graph theory was applied on resting-state functional magnetic resonance imaging network analysis, which revealed that the hemispheres ipsilateral to the stenosis were significantly impaired in "degree" and "global efficiency." The neuropsychological performances were positively correlated with degree, clustering coefficient, local efficiency, and global efficiency, and negatively correlated with characteristic path length, modularity, and small-worldness in the patients, whereas these relationships were not observed in the controls. In this study, we identified the networks that were impaired in the affected hemispheres in patients with carotid stenosis. Specific indices (global efficiency, characteristic path length, and modularity) were highly correlated with neuropsychological performance in our patients. Analysis of brain connectivity may help to elucidate the relationship between hemodynamic impairment and cognitive decline.

Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.

Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

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We use machine learning classification to classify Alzheimer's disease.

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For classification we use anatomical MRI, diffusion MRI, and resting state fMRI.

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Grey matter density is most successful for single modality classification.

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Combining multiple modalities improves classification of Alzheimer's disease.

Functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies

Resting-state functional connectivity is a promising biomarker for Alzheimer's disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer's disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5-10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimer's disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer's disease.

Multimodality Imaging Approach in Alzheimer disease. Part I: Structural MRI, Functional MRI, Diffusion Tensor Imaging and Magnetization Transfer Imaging

The authors make a complete review of the potential clinical applications of traditional and novel magnetic resonance imaging (MRI) techniques in the evaluation of patients with Alzheimer's disease, including structural MRI, functional MRI, diffusion tension imaging and magnetization transfer imaging.

Assessment of brain cognitive functions in patients with vitamin B12 deficiency using resting state functional MRI: A longitudinal study

INTRODUCTION

The resting state functional MRI (rsfMRI) approach is useful to explore the brain's functional organization in health and disease conditions. In this study, using rsfMRI the alteration in brain due to vitamin B12 deficiency and reversibility of these alterations following therapy was studied.

METHODS

Thirteen patients with clinical and biochemical evidence of vitamin B12 deficiency were recruited in this study. Fifteen age and sex matched healthy controls were also included. Patients and controls were clinically evaluated using neuropsychological test (NPT). The analysis was carried out using regional homogeneity (ReHo) and low frequency oscillations (LFO) of BOLD signals in resting state. Six patients were also evaluated with rsfMRI and NPT after 6 weeks replacement therapy.

RESULTS

ReHo values in patients with vitamin B12 deficiency were significantly lower than controls in the entire cerebrum and the brain networks associated with cognition control, i.e., default mode, cingulo-opercular and fronto-parietal network. There was no significant difference using LFO and it did not show significant correlations with NPT scores. ReHo showed significant correlation with NPT scores. All the 6 patients showed increase in ReHo after replacement therapy.

CONCLUSION

We conclude that brain networks associated with cognition control are altered in patients with vitamin B12 deficiency, which partially recover following six weeks of replacement therapy. This is the first study to evaluate the rsfMRI in the light of clinical neuropsychological evaluation in patients. rsfMRI may be used as functional biomarker to assess therapeutic response in vitamin B12 deficiency patients.

Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer's disease

We present a test-retest dataset of resting-state fMRI data obtained in 80 cognitively normal elderly volunteers enrolled in the “Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease” (PREVENT-AD) Cohort. Subjects with a family history of Alzheimer's disease in first-degree relatives were recruited as part of an on-going double blind randomized clinical trial of Naproxen or placebo. Two pairs of scans were acquired ~3 months apart, allowing the assessment of both intra- and inter-session reliability, with the possible caveat of treatment effects as a source of inter-session variation. Using the NeuroImaging Analysis Kit (NIAK), we report on the standard quality of co-registration and motion parameters of the data, and assess their validity based on the spatial distribution of seed-based connectivity maps as well as intra- and inter-session reliability metrics in the default-mode network. This resource, released publicly as sample UM1 of the Consortium for Reliability and Reproducibility (CoRR), will benefit future studies focusing on the preclinical period preceding the appearance of dementia in Alzheimer's disease.

Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks

Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD-related brain changes and a prominent cortical association network called the default-mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45-PET, neuronal metabolism using FDG-PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region-of-interest and voxel-based analyses were used to assess disease stage- and modality-specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal-control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid- and atrophy-related profiles. Similar patterns of modality-dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology.

A Window into the Brain: Advances in Psychiatric fMRI

Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. It provides a means to assay differences in brain systems that underlie psychiatric illness, treatment response, and properties of brain structure and function that convey risk factor for mental diseases. Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. Drawing on concepts and findings from psychiatric fMRI, we propose that mental illness may not be associated with abnormalities in specific local regions but rather corresponds to variation in the overall organization of functional communication throughout the brain network. Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of psychiatric disorders.

[Diffusion formation and psychiatric diseases]

The basic principle behind diffusion is Brownian motion. The diffusion parameters obtained in a clinical association provide information on the spatial distribution of water molecule mobility and, therefore, evidence of the morphological integrity of the white and grey matters of the brain. In recent years functional magnetic resonance imaging (fMRI) could contribute to obtaining a detailed understanding of the cortical and subcortical cerebral networks. Diffusion tensor imaging (DTI) investigations can demonstrate the extent of anisotropy and the fiber pathways in so-called parametric images. For example, in Alzheimer's disease DTI reveals a reduced structural connectivity between the posterior cingulum and the hippocampus. This article shows examples of the application of diffusion-weighted imaging (DWI) in psychiatric disorders.

Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI

The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.

APOE Effects on Default Mode Network in Chinese Cognitive Normal Elderly: Relationship with Clinical Cognitive Performance

Background

Functional connectivity in default mode network (DMN) may be changed in Alzheimer’s disease (AD) patients and related risk populations, such as amnestic mild cognitive impairment (aMCI) patients and APOE ε4 carriers. Exploring DMN changes and related behavioral performance of APOE ε4 population might provide valuable evidence for better understanding the development of AD.

Methods

Subjects were enrolled from a population-based cohort established in a multi-center study in China. Forty-nine cognitive normal individuals were enrolled after standardized cognitive evaluations, MRI examination and APOE genotyping. Regions of interest (ROI)-based functional connectivity analyses were performed, and voxel-based analyses were used to validate these findings. The correlation between DMN functional connectivity and behavioral performance was further evaluated between APOE ε4ε3 and ε3ε3 carriers.

Results

Comparing to ε3ε3 carriers, functional connectivity between left parahippocampal gyrus and right superior frontal cortex (LPHC-R.Sup.F), left parahippocampal gyrus and medial prefrontal cortex (ventral) (LPHC-vMPFC) were significantly increased in ε4ε3 carriers, while connectivity between cerebellar tonsils and retrosplenial was decreased. LPHC-R.Sup.F connectivity was positively correlated with the changes of delay recall from baseline to follow-up (r = 0.768, p = 0.009), while LPHC-vMPFC connectivity had a positive correlation with MMSE at baseline (r = 0.356, p = 0.018), and a negative correlation with long-delayed recognition at follow-up (r = -0.677, p = 0.031). Significantly increased functional connectivity in vMPFC was confirmed in voxel-based analyses by taking LPHC as seed region.

Conclusion

APOE ε4 carriers present both increased and decreased functional connectivity in DMN, which is correlated with clinical cognitive performances. DMN changes might be an early sign for cognitive decline.

Default mode network links to visual hallucinations: A comparison between Parkinson's disease and multiple system atrophy

BACKGROUND

Studying default mode network activity or connectivity in different parkinsonisms, with or without visual hallucinations, could highlight its roles in clinical phenotypes' expression. Multiple system atrophy is the archetype of parkinsonism without visual hallucinations, variably appearing instead in Parkinson's disease (PD). We aimed to evaluate default mode network functions in multiple system atrophy in comparison with PD.

METHODS

Functional magnetic resonance imaging evaluated default mode network activity and connectivity in 15 multiple system atrophy patients, 15 healthy controls, 15 early PD patients matched for disease duration, 30 severe PD patients (15 with and 15 without visual hallucinations), matched with multiple system atrophy for disease severity. Cortical thickness and neuropsychological evaluations were also performed.

RESULTS

Multiple system atrophy had reduced default mode network activity compared with controls and PD with hallucinations, and no differences with PD (early or severe) without hallucinations. In PD with visual hallucinations, activity and connectivity was preserved compared with controls and higher than in other groups. In early PD, connectivity was lower than in controls but higher than in multiple system atrophy and severe PD without hallucinations. Cortical thickness was reduced in severe PD, with and without hallucinations, and correlated only with disease duration. Higher anxiety scores were found in patients without hallucinations.

CONCLUSIONS

Default mode network activity and connectivity was higher in PD with visual hallucinations and reduced in multiple system atrophy and PD without visual hallucinations. Cortical thickness comparisons suggest that functional, rather than structural, changes underlie the activity and connectivity differences.

The default mode network as a biomarker for monitoring the therapeutic effects of meditation

The default mode network (DMN) is a group of anatomically separate regions in the brain found to have synchronized patterns of activation in functional magnetic resonance imaging (fMRI). Mentation associated with the DMN includes processes such as mind wandering, autobiographical memory, self-reflective thought, envisioning the future, and considering the perspective of others. Abnormalities in the DMN have been linked to symptom severity in a variety of mental disorders indicating that the DMN could be used as a biomarker for diagnosis. These correlations have also led to the use of DMN modulation as a biomarker for assessing pharmacological treatments. Concurrent research investigating the neural correlates of meditation, have associated DMN modulation with practice. Furthermore, meditative practice is increasingly understood to have a beneficial role in the treatment of mental disorders. Therefore we propose the use of DMN measures as a biomarker for monitoring the therapeutic effects of meditation practices in mental disorders. Recent findings support this perspective, and indicate the utility of DMN monitoring in understanding and developing meditative treatments for these debilitating conditions.

Progressive changes in hippocampal resting-state connectivity across cognitive impairment: a cross-sectional study from normal to Alzheimer disease

We investigate the changes in functional connectivity of the left and right hippocampus by comparing the resting-state low-frequency fluctuations in the blood oxygen level-dependent signal from these regions with relation to Alzheimer disease (AD) progression. AD patients were divided into subgroups based on the clinical dementia rating (CDR) scores. Patients with amnestic mild cognitive impairment (aMCI) were also analyzed as an intermediate stage between normal controls and AD. We found that the total functional connectivity of both the right and left hippocampus was maintained during aMCI and the early stages of AD and that it decreased in the later stages of AD. However, when total functional connectivity was broken down into specific regions of the brain, we observed increased or decreased connectivity to specific regions beginning with aMCI. Direct correlation analysis in seeding the left hippocampus revealed a significant decrease in the functional connectivity with the posterior cingulate cortex region and lateral parietal areas, and an increase in functional connectivity in and the anterior cingulate cortex beginning with aMCI. In this study, we were able to quantify the deterioration of resting-state hippocampal connectivity with disease severity and formation of compensatory recruitment in the early stages of AD.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory

OBJECTIVE

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation.

METHOD

In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD.

RESULTS

Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.

CONCLUSION

Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.

SIGNIFICANCE

Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease.

Independent component analysis-based identification of covariance patterns of microstructural white matter damage in Alzheimer's disease

The existing DTI studies have suggested that white matter damage constitutes an important part of the neurodegenerative changes in Alzheimer’s disease (AD). The present study aimed to identify the regional covariance patterns of microstructural white matter changes associated with AD. In this study, we applied a multivariate analysis approach, independent component analysis (ICA), to identify covariance patterns of microstructural white matter damage based on fractional anisotropy (FA) skeletonised images from DTI data in 39 AD patients and 41 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative database. The multivariate ICA decomposed the subject-dimension concatenated FA data into a mixing coefficient matrix and a source matrix. Twenty-eight independent components (ICs) were extracted, and a two sample t-test on each column of the corresponding mixing coefficient matrix revealed significant AD/HC differences in ICA weights for 7 ICs. The covariant FA changes primarily involved the bilateral corona radiata, the superior longitudinal fasciculus, the cingulum, the hippocampal commissure, and the corpus callosum in AD patients compared to HCs. Our findings identified covariant white matter damage associated with AD based on DTI in combination with multivariate ICA, potentially expanding our understanding of the neuropathological mechanisms of AD.

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD.

Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI

Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating healthy subjects from subjects with amnesic mild cognitive impairment and 97% accuracy disambiguating amnesic mild cognitive impairment subjects from those with Alzheimer's disease, accuracies are estimated using a held-out test set. Both results are significant at the 1% level.

Longitudinal alteration of amygdalar functional connectivity in mild cognitive impairment subjects revealed by resting-state FMRI

Mild cognitive impairment (MCI) is considered to be the prodromal stage of Alzheimer's disease. The amygdala, which is considered to be a hub, has been shown to have widespread brain connections with many cortical regions. Longitudinal alterations in the functional connectivity of the amygdala remain unclear in MCI. We hypothesized that the impairment in the amygdala-cortical loop would be more severe in a follow-up MCI group than in a baseline MCI group and that these alterations would be related to the disease processes. To test this hypothesis, we used resting-state functional magnetic resonance imaging to investigate alterations in amygdalar connectivity patterns based on longitudinal data from 13 MCI subjects (8 males and 5 females). Compared to the baseline, decreases in functional connectivity were mainly found located between the amygdala and regions at the conjunction of the temporal-occipital system and the regions included in the default mode network in the follow-up MCI individuals. The alterations in the functional connectivity of the identified regions were validated in an independent dataset. Specifically, reduced amygdalar connectivity was significantly correlated with cognitive abilities. These findings indicate that impairments in the functional connectivity of the amygdala may be potential biomarkers of the progression of MCI.

Metabolic resting-state brain networks in health and disease

The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone.

When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality – an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

Genetics pathway-based imaging approaches in Chinese Han population with Alzheimer's disease risk

The tau hypothesis has been raised with regard to the pathophysiology of Alzheimer's disease (AD). Mild cognitive impairment (MCI) is associated with a high risk for developing AD. However, no study has directly examined the brain topological alterations based on combined effects of tau protein pathway genes in MCI population. Forty-three patients with MCI and 30 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) in Chinese Han, and a tau protein pathway-based imaging approaches (7 candidate genes: 17 SNPs) were used to investigate changes in the topological organisation of brain activation associated with MCI. Impaired regional activation is related to tau protein pathway genes (5/7 candidate genes) in patients with MCI and likely in topologically convergent and divergent functional alterations patterns associated with genes, and combined effects of tau protein pathway genes disrupt the topological architecture of cortico-cerebellar loops. The associations between the loops and behaviours further suggest that tau protein pathway genes do play a significant role in non-episodic memory impairment. Tau pathway-based imaging approaches might strengthen the credibility in imaging genetic associations and generate pathway frameworks that might provide powerful new insights into the neural mechanisms that underlie MCI.

Advances in MRI biomarkers for the diagnosis of Alzheimer's disease

With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.

Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease

Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.

A comprehensive assessment of resting state networks: bidirectional modification of functional integrity in cerebro-cerebellar networks in dementia

In resting state fMRI (rs-fMRI), only functional connectivity (FC) reductions in the default mode network (DMN) are normally reported as a biomarker for Alzheimer's disease (AD). In this investigation we have developed a comprehensive strategy to characterize the FC changes occurring in multiple networks and applied it in a pilot study of subjects with AD and Mild Cognitive Impairment (MCI), compared to healthy controls (HC). Resting state networks (RSNs) were studied in 14 AD (70 ± 6 years), 12 MCI (74 ± 6 years), and 16 HC (69 ± 5 years). RSN alterations were present in almost all the 15 recognized RSNs; overall, 474 voxels presented a reduced FC in MCI and 1244 in AD while 1627 voxels showed an increased FC in MCI and 1711 in AD. The RSNs were then ranked according to the magnitude and extension of FC changes (gFC), putting in evidence 6 RSNs with prominent changes: DMN, frontal cortical network (FCN), lateral visual network (LVN), basal ganglia network (BGN), cerebellar network (CBLN), and the anterior insula network (AIN). Nodes, or hubs, showing alterations common to more than one RSN were mostly localized within the prefrontal cortex and the mesial-temporal cortex. The cerebellum showed a unique behavior where voxels of decreased gFC were only found in AD while a significant gFC increase was only found in MCI. The gFC alterations showed strong correlations (p < 0.001) with psychological scores, in particular Mini-Mental State Examination (MMSE) and attention/memory tasks. In conclusion, this analysis revealed that the DMN was affected by remarkable FC increases, that FC alterations extended over several RSNs, that derangement of functional relationships between multiple areas occurred already in the early stages of dementia. These results warrant future work to verify whether these represent compensatory mechanisms that exploit a pre-existing neural reserve through plasticity, which evolve in a state of lack of connectivity between different networks with the worsening of the pathology.

Abnormal vibrissa-related behavior and loss of barrel field inhibitory neurons in 5xFAD transgenics

A recent study reported lower anxiety in the 5xFAD transgenic mouse model of Alzheimer's disease, as measured by reduced time on the open arms of an elevated plus maze. This is important because all behaviors in experimental animals must be interpreted in light of basal anxiety and response to novel environments. We conducted a comprehensive anxiety battery in the 5xFAD transgenics and replicated the plus-maze phenotype. However, we found that it did not reflect reduced anxiety, but rather abnormal avoidance of the closed arms on the part of transgenics and within-session habituation to the closed arms on the part of wild-type controls. We noticed that the 5xFAD transgenics did not engage in the whisker-barbering behavior typical of mice of this background strain. This is suggestive of abnormal social behavior, and we suspected it might be related to their avoidance of the closed arms on the plus maze. Indeed, transgenic mice exhibited excessive home-cage social behavior and impaired social recognition, and did not permit barbering by wild-type mice when pair-housed. When their whiskers were snipped the 5xFAD transgenics no longer avoided the closed arms on the plus maze. Examination of parvalbumin (PV) staining showed a 28.9% reduction in PV+ inhibitory interneurons in the barrel fields of 5xFAD mice, and loss of PV+ fibers in layers IV and V. This loss of vibrissal inhibition suggests a putatively aversive overstimulation that may be responsible for the transgenics' avoidance of the closed arms in the plus maze.

Ontology driven decision support for the diagnosis of mild cognitive impairment

In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.

Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease

The search for an Alzheimer's disease (AD) biomarker is one of the most relevant contemporary research topics due to the high prevalence and social costs of the disease. Functional connectivity (FC) of the default mode network (DMN) is a plausible candidate for such a biomarker. We evaluated 22 patients with mild AD and 26 age- and gender-matched healthy controls. All subjects underwent resting functional magnetic resonance imaging (fMRI) in a 3.0 T scanner. To identify the DMN, seed-based FC of the posterior cingulate was calculated. We also measured the sensitivity/specificity of the method, and verified a correlation with cognitive performance. We found a significant difference between patients with mild AD and controls in average z-scores: DMN, whole cortical positive (WCP) and absolute values. DMN individual values showed a sensitivity of 77.3% and specificity of 70%. DMN and WCP values were correlated to global cognition and episodic memory performance. We showed that individual measures of DMN connectivity could be considered a promising method to differentiate AD, even at an early phase, from normal aging. Further studies with larger numbers of participants, as well as validation of normal values, are needed for more definitive conclusions.