Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?

Soman, Shania Mereen; Raghavan, Sheelakumari; Rajesh, P.; Mohanan, Nandini; Kesavadas, C.; Menon, Ramshekhar

doi:10.1016/j.jns.2020.117093

Cited by 35 publications

(30 citation statements)

References 38 publications

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“…In recent years, functional connectivity or its network analog functional network connectivity (FNC), including dynamic (dFC/dFNC) and static (sFC/sFNC), achieved from rs-fMRI time series has uncovered a great deal of knowledge about the brain dysconnectivity in various neurological disorder including schizophrenia (Abrol et al, 2017;Sendi et al, 2020), major depression disorder (Zhi et al, 2018), autism (Cerliani et al, 2015;de Lacy et al, 2017;Mash et al, 2019), ADHD (Wang et al, 2018), and AD (Brier et al, 2014). In particular for AD, previous studies reported a reduction in the default-mode network FC in AD compared with mild cognitive impairment (MCI) patients and healthy subjects (Soman et al, 2020). Another study reported a difference in the FC of sensorimotor network (SMN), visual network (VSN), and default mode network of healthy control (HC) subjects and AD patients (Zheng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Sendi

Zendehrouh

Miller

et al. 2021

Front. Neural Circuits

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BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

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Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Sendi

Zendehrouh

Miller

et al. 2021

Front. Neural Circuits

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“…These results point to an important role of ongoing activity in the human brain. In line with this insight are close associations between abnormalities in these resting-state networks and many psychiatric disorders [143][144][145][146][147][148][149]. However, humans may be unique in this trait and results from humans may not generalize to other animals.…”

Section: Ongoing Activity In the Absence Of Inputmentioning

confidence: 90%

The brain as a dynamically active organ

Brembs¹

2020

Preprint

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Nervous systems are typically described as a static network passively responding to external stimuli (i.e., the ‘sensorimotor hypothesis’). However, for more than a century now, evidence has been accumulating that this passive-static perspective is wrong. Instead, evidence suggests that nervous systems dynamically change their connectivity and actively generate behavior in order to control their sensory feedback. This review provides a brief overview of the different historical perspectives on general brain function and details some select modern examples falsifying the sensorimotor hypothesis.

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“…Moreover, several studies supported the significant change in the connections to/ from DMN ROIs due to AD progression such as Precuneus_L, Precuneus_R, Rectus_R, Frontal_Sup_Medial_R, Frontal_Sup_L, Frontal_Med_Orb_L, Temporal_Mid_R, Cingulum_Ant_R, and ’Precentral_R, reported in Table 7 [ 5 , 10 – 13 , 17 , 18 , 49 , 52 ]. Furthermore, recent studies indicated that the sensorimotor cortex ROIsinclude Insula_L, Parietal_Sup_R, Temporal_Sup_L, Temporal_Sup_R, Postcentral_L, Heschl_R, and SupraMarginal_L are also affected in the early progression of AD, which is also listed in Table 7 [ 5 , 11 , 47 , 53 – 56 ].…”

Section: Discussionmentioning

confidence: 99%

Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging

2022

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Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.

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Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?

Cited by 35 publications

References 38 publications

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

The brain as a dynamically active organ

Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging

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