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

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.

Download Full-text

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

10.21203/rs.3.rs-440491/v1 ◽

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.

Download Full-text

Resting State Functional Connectivity Signature Differentiates Cognitively Normal from Individuals Who Convert to Symptomatic Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-191039 ◽

2020 ◽

Vol 74 (4) ◽

pp. 1085-1095 ◽

Cited By ~ 1

Author(s):

Julie K. Wisch ◽

Catherine M. Roe ◽

Ganesh M. Babulal ◽

Suzanne E. Schindler ◽

Anne M. Fagan ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Functional Connectivity ◽

Cognitively Normal

Download Full-text

Faculty Opinions recommendation of Relationships between tau and glucose metabolism reflect alzheimer's disease pathology in cognitively normal older adults.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.733457144.793547250 ◽

2018 ◽

Author(s):

Daniela Perani

Keyword(s):

Alzheimer’S Disease ◽

Older Adults ◽

Alzheimer's Disease ◽

Glucose Metabolism ◽

Cognitively Normal

Download Full-text

O5-03-01: Decreased resting-state functional connectivity of posterior cingulate cortex in amnestic MCI-to-Alzheimer's disease converters and non-converters

Alzheimer s & Dementia ◽

10.1016/j.jalz.2013.04.481 ◽

2013 ◽

Vol 9 ◽

pp. P832-P833

Author(s):

Ying Han

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Cingulate Cortex ◽

Posterior Cingulate Cortex ◽

Resting State Functional Connectivity ◽

Posterior Cingulate ◽

Amnestic Mci

Download Full-text

Resting State Functional Connectivity in Preclinical Alzheimer’s Disease

Biological Psychiatry ◽

10.1016/j.biopsych.2012.11.028 ◽

2013 ◽

Vol 74 (5) ◽

pp. 340-347 ◽

Cited By ~ 243

Author(s):

Yvette I. Sheline ◽

Marcus E. Raichle

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Functional Connectivity ◽

Preclinical Alzheimer’S Disease

Download Full-text

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's Disease and Mild Cognitive Impairment

Clinical Neurophysiology ◽

10.1016/j.clinph.2021.06.036 ◽

2021 ◽

Author(s):

Mohammadmahdi Rahimiasl ◽

Nasrollah Moghadam Charkari ◽

Foad Ghaderi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Random Walks ◽

Resting State ◽

Resting State Functional Connectivity

Download Full-text

Predicting dysfunctional age-related task activations from resting-state network alterations

10.1101/678086 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ravi D. Mill ◽

Brian A. Gordon ◽

David A. Balota ◽

Jeffrey M. Zacks ◽

Michael W. Cole

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

At Risk ◽

Functional Connectivity ◽

Resting State ◽

Cognitive Task ◽

Low Risk ◽

Flow Modeling ◽

Resting State Functional Connectivity ◽

Age Related

AbstractAlzheimer’s disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimer’s disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimer’s-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.Significance StatementDeveloping analytic approaches that can reliably predict features of Alzheimer’s disease is a major goal for cognitive and clinical neuroscience, with particular emphasis on identifying such diagnostic features early in the timeline of disease. We demonstrate the utility of an activity flow modeling approach, which predicts fMRI cognitive task activations in subjects identified as at-risk for Alzheimer’s disease. The approach makes activation predictions by transforming a healthy aged activation template via the at-risk subjects’ individual pattern of fMRI resting-state functional connectivity (restFC). The observed prediction accuracy supports activity flow as a mechanism linking age-related alterations in restFC and task activations, thereby providing a theoretical basis for incorporating restFC into imaging biomarker and personalized medicine interventions.

Download Full-text