scholarly journals Brain network remodelling reflects tau-related pathology prior to memory deficits in Thy-Tau22 mice

Brain ◽  
2020 ◽  
Author(s):  
Laetitia Degiorgis ◽  
Meltem Karatas ◽  
Marion Sourty ◽  
Emilie Faivre ◽  
Julien Lamy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Dorsal Hippocampus ◽  
Brain Network ◽  
Prodromal Phase ◽  
Compensatory Response ◽  
Major Mechanism ◽  
Brain Connectome

Abstract In Alzheimer’s disease, the tauopathy is known as a major mechanism responsible for the development of cognitive deficits. Early biomarkers of such affectations for diagnosis/stratification are crucial in Alzheimer’s disease research, and brain connectome studies increasingly show their potential establishing pathology fingerprints at the network level. In this context, we conducted an in vivo multimodal MRI study on young Thy-Tau22 transgenic mice expressing tauopathy, performing resting state functional MRI and structural brain imaging to identify early connectome signatures of the pathology, relating with histological and behavioural investigations. In the prodromal phase of tauopathy, before the emergence of cognitive impairments, Thy-Tau22 mice displayed selective modifications of brain functional connectivity involving three main centres: hippocampus (HIP), amygdala (AMG) and the isocortical areas, notably the somatosensory (SS) cortex. Each of these regions showed differential histopathological profiles. Disrupted ventral HIP-AMG functional pathway and altered dynamic functional connectivity were consistent with high pathological tau deposition and astrogliosis in both hippocampus and amygdala, and significant microglial reactivity in amygdalar nuclei. These patterns were concurrent with widespread functional hyperconnectivity of memory-related circuits of dorsal hippocampus—encompassing dorsal HIP-SS communication—in the absence of significant cortical histopathological markers. These findings suggest the coexistence of two intermingled mechanisms of response at the functional connectome level in the early phases of pathology: a maladaptive and a likely compensatory response. Captured in the connectivity patterns, such first responses to pathology could further be used in translational investigations as a lead toward an early biomarker of tauopathy as well as new targets for future treatments.

scholarly journals In vivo mapping of brainstem nuclei functional connectivity disruption in Alzheimer's disease

2018 ◽  
Vol 72 ◽  
pp. 72-82 ◽  
Author(s):  
Laura Serra ◽  
Marcello D'Amelio ◽  
Carlotta Di Domenico ◽  
Ottavia Dipasquale ◽  
Camillo Marra ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Brainstem Nuclei

scholarly journals The trend of disruption in the functional brain network topology of Alzheimer’s disease

2021 ◽  
Author(s):  
Alireza Fathian ◽  
Yousef Jamali ◽  
Mohammad Reza Raoufy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Small World ◽  
Brain Network ◽  
Cognitively Normal ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Network ◽  
Progressive Disorder

Abstract Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study analyzed the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression, and these trends behaved differently at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, The methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

scholarly journals Approach to atypical Alzheimer’s disease and case studies of the major subtypes

CNS Spectrums ◽  
2017 ◽  
Vol 22 (6) ◽  
pp. 439-449 ◽  
Author(s):  
Bradford C. Dickerson ◽  
Scott M. McGinnis ◽  
Chenjie Xia ◽  
Bruce H. Price ◽  
Alireza Atri ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuropsychiatric Symptoms ◽  
Current Approach ◽  
Molecular Biomarkers ◽  
Diagnostic Tools ◽  
Prodromal Phase ◽  
Atypical Form ◽  
Atypical Symptoms

Alzheimer’s disease (AD) has long been recognized as a heterogeneous illness, with a common clinical presentation of progressive amnesia and less common “atypical” clinical presentations, including syndromes dominated by visual, aphasic, “frontal,” or apraxic symptoms. Our knowledge of atypical clinical phenotypes of AD comes from clinicopathologic studies, but with the growing use of in vivo molecular biomarkers of amyloid and tau pathology, we are beginning to recognize that these syndromes may not be as rare as once thought. When a clinician is evaluating a patient whose clinical phenotype is dominated by progressive aphasia, complex visual impairment, or other neuropsychiatric symptoms with relative sparing of memory, the differential diagnosis may be broader and a confident diagnosis of an atypical form of AD may require the use of molecular biomarkers. Despite the evolving sophistication in our diagnostic tools, and the acknowledgment of atypical AD syndromes in the 2011 revised diagnostic criteria for AD, the assessment of such patients still poses substantial challenges. We use a case-based approach to review the clinical and imaging phenotypes of a series of patients with typical and atypical AD, and discuss our current approach to their evaluation. One day, we hope that regardless of whether a patient exhibits typical or atypical symptoms of AD pathology, we will be able to identify the condition at a prodromal phase and institute a combination of symptomatic and disease-modifying therapies to support cognitive processes, function, and behavior, and slow or halt progression to dementia.

scholarly journals Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Miao Zhang ◽  
Wanqing Sun ◽  
Ziyun Guan ◽  
Jialin Hu ◽  
Binyin Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Glucose Metabolism ◽  
Clustering Algorithm ◽  
Brain Network ◽  
Resting State Functional Connectivity ◽  
Metabolic Pattern ◽  
Cognitively Normal ◽  
Functional Connectivity Strength

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer’s disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

scholarly journals Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

2021 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Zening Fu ◽  
Jingyu Liu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Cognitive Control ◽  
Disease Progression ◽  
Brain Network ◽  
Middle Frontal Gyrus ◽  
Functional Network ◽  
Control Network ◽  
Cognitive Control Network

AbstractBackgroundAlzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.MethodsGroup independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression.ResultsProgression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.ConclusionOur results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.Impact StatementBy assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.

scholarly journals Functional Connectivity between the Resting-State Olfactory Network and the Hippocampus in Alzheimer’s Disease

2019 ◽  
Vol 9 (12) ◽  
pp. 338 ◽  
Author(s):  
Lu ◽  
Testa ◽  
Jordan ◽  
Elyan ◽  
Kanekar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Gray Matter ◽  
Resting State ◽  
Early Stage ◽  
Gray Matter Volume

Olfactory impairment is associated with prodromal Alzheimer’s disease (AD) and is a risk factor for the development of dementia. AD pathology is known to disrupt brain regions instrumental in olfactory information processing, such as the primary olfactory cortex (POC), the hippocampus, and other temporal lobe structures. This selective vulnerability suggests that the functional connectivity (FC) between the olfactory network (ON), consisting of the POC, insula and orbital frontal cortex (OFC) (Tobia et al., 2016), and the hippocampus may be impaired in early stage AD. Yet, the development trajectory of this potential FC impairment remains unclear. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to investigate FC changes between the ON and hippocampus in four groups: aged-matched cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. FC was calculated using low frequency fMRI signal fluctuations in the ON and hippocampus (Tobia et al., 2016). We found that the FC between the ON and the right hippocampus became progressively disrupted across disease states, with significant differences between EMCI and LMCI groups. Additionally, there were no significant differences in gray matter hippocampal volumes between EMCI and LMCI groups. Lastly, the FC between the ON and hippocampus was significantly correlated with neuropsychological test scores, suggesting that it is related to cognition in a meaningful way. These findings provide the first in vivo evidence for the involvement of FC between the ON and hippocampus in AD pathology. Results suggest that functional connectivity (FC) between the olfactory network (ON) and hippocampus may be a sensitive marker for Alzheimer’s disease (AD) progression, preceding gray matter volume loss.

Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph

2020 ◽  
Vol 17 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Zhongke Gao ◽  
Yanhua Feng ◽  
Chao Ma ◽  
Kai Ma ◽  
Qing Cai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Complex Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Visibility Graph ◽  
Time Dependent ◽  
Biological Information ◽  
Local Complex

Background: Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD. Methods: We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences. Results: According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD. Conclusion: These results would be helpful in revealing the underlying pathological mechanism of the disease.

scholarly journals Diagnosis of Alzheimer’s Disease Using Brain Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Ramesh Kumar Lama ◽  
Goo-Rak Kwon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Feature Selection Method ◽  
Brain Network ◽  
Classification Performance ◽  
Support Vector ◽  
Early Event ◽  
Learning Machine ◽  
The Brain

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson’s correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.

scholarly journals Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 475 ◽  
Author(s):  
Eufemia Lella ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Angela Lombardi ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Brain Network ◽  
Data Set ◽  
Brain Connectome ◽  
Subcortical Brain ◽  
Classification Framework ◽  
Subcortical Regions ◽  
Cortical Regions

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.

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