Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer’s disease

Schumacher, Julia; Peraza, Luis R.; Firbank, Michael; Thomas, Alan; Kaiser, Marcus; Gallagher, Peter; O’Brien, John; Blamire, Andrew M.; Taylor, John‐Paul

doi:10.1101/374538

Cited by 21 publications

(40 citation statements)

References 51 publications

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“…This finding suggests that the aberrant AD-related destabilization of restingstate posterior DMN activation may not be directly linked to cognitive impairment. This lack of conclusive correlation with the cognitive profile of AD patients was also observed in a rsfMRI study of dynamic functional integration 59 . A specific hypothesis for this is further developed below.…”

Section: Discussionmentioning

confidence: 63%

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum: a combined MEG-PET/MR approach

Puttaert

Coquelet

Wens

et al. 2020

Preprint

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Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD.We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with Subjective Cognitive Decline (SCD), 10 patients with amnestic Mild Cognitive Impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. HMM patterns were related to participants' cognitivetest scores, whole hippocampal grey matter volume and regional brain glucose metabolism.The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores. However, it correlated positively with the whole hippocampal volume and regional glucose consumption in the right temporo-parietal junctions and dorsolateral prefrontal cortex, and negatively with glucose consumption in the cerebellum.In AD patients, alterations of posterior DMN power activation dynamics at rest correlate with structural and neurometabolic abnormalities. These findings represent an additional electrophysiological correlate of AD-related synaptic and neural dysfunction.

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Section: Discussionmentioning

confidence: 63%

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum: a combined MEG-PET/MR approach

Puttaert

Coquelet

Wens

et al. 2020

Preprint

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“…Patients with Alzheimer's disease (AD) showed higher and lower dwell time in dFC state with strong the anterior and posterior DMN, respectively (Jones et al, 2012). Patients with AD also show alteration in whole-brain dFC (Cordova-Palomera et al, 2017;Schumacher et al, 2019) and spend more time in the weakly-connected dFC state (Schumacher et al, 2019). Patients with AD show both common and distinct dFC patterns with patients who have subcortical ischemic vascular disease (SIVD), while the clinical features and symptoms between these two disorders can sometimes be difficult to distinguish (Fu et al, 2019a).…”

Section: Dynamic Functional Connectivity (Time-varying Functional Patmentioning

confidence: 99%

“…Meta-state analysis (see Window-based approaches (WBAs) for definition) also show reduced dynamism in CI-MS patients (d'Ambrosio et al, 2019). Atypical dFC patterns present in other cohorts and brain disorders, including migraine (Tu et al, 2019), stroke , epilepsy (Douw et al, 2015;Liu et al, 2017;Klugah-Brown et al, 2019), attention deficit hyperactivity disorder (ADHD) (Ou et al, 2014;de Lacy and Calhoun, 2019), post-traumatic stress disorder Jin et al, 2017), frontotemporal dementia (Premi et al, 2019), and Lewy body dementia (Schumacher et al, 2019).…”

Section: Dynamic Functional Connectivity (Time-varying Functional Patmentioning

confidence: 99%

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Iraji¹,

Faghiri²,

Lewis³

et al. 2020

Preprint

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Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

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“…In this study, dFNC analysis was used to explore the differences of dynamic functional network connectivity in PD patients with or without LID and on/off levodopa treatments, focusing on temporal properties (fractional windows, dwell time and number of transitions [15], schizophrenia [16], and Alzheimer's disease [17]. In particular, for PD, Kim et al proposed for the rst time that PD patients occurred more frequently and dwelled longer in strongly between-network connected state compared to healthy controls [12].…”

Section: Discussionmentioning

confidence: 99%

“…3 Nevertheless, recent studies have proposed that FC is dynamic or uctuating within seconds to minutes, which highlights the need for detailed inspection of FC along discrete time windows [9][10][11]. Emerging data suggested the utility of dynamic functional network connectivity (dFNC) with sliding-window analysis for understanding functional neurodevelopment as well as PD pathogenesis [12][13][14][15][16][17]. Time-varying FC may re ect implied spontaneous changes in underlying networks, which might improve our knowledge of how neural systems exibly coordinate to support cognitive and behavioral function.…”

Section: Introductionmentioning

confidence: 99%

Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

Si¹,

Yuan²,

Gan³

et al. 2020

Preprint

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Background Traditional measures of static functional connectivity may not completely reflect the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). This study was aimed to investigate the dynamic changes of large-scale functional network connectivity in the temporal domain in PD patients with and without LID. Methods Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group), on and off their levodopa medications. Group spatial independent component analysis, sliding-window approach and k-means clusters were employed. Results In OFF phase, we found no differences between PD subgroups in temporal properties. In ON phase, compared than No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong connections between visual network (VIS) and other networks. When switching from OFF to ON phase, LID group occurred more frequently and dwelled longer in State 2 and occurred less frequently and dwelled shorter in State 3 (both states were strongly connected), while No-LID group occurred more frequently and dwelled longer in State 5 (weakly connected). Additionally, correlation analysis further demonstrated that the severity of dyskinesia was only associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN), strongly connecting with sensorimotor network (SMN) and VIS. Conclusions Using dFNC analysis, we found that compared to those without LID, PD patients with LID may be involved in the superexcitation of VIS, as well as interconnections between CEN and SMN, VIS, having impact on inhibition of motor circuits. The dFNC analysis might provide new insights into the neural mechanisms of LID in PD.

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Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer’s disease

Cited by 21 publications

References 51 publications

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum: a combined MEG-PET/MR approach

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum: a combined MEG-PET/MR approach

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

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