Early detection of dementia with default-mode network effective connectivity

Ereira, Sam; Waters, Sheena; Razi, Adeel; Marshall, Charles R.

doi:10.1038/s44220-024-00259-5

Cited by 3 publications

(2 citation statements)

References 81 publications

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“…Here, we showed that effective connectivity parameters related to group differences were helpful in predicting group membership. These results align with a recent study showing that resting-state DMN effective connectivity can predict dementia incidences before disease-onset in a relatively larger sample 47 , which supports the clinical usefulness of DMN effective connectivity in neurodegenerative disorders.…”

Section: Predicting Group Membership and Cognitive Performancesupporting

confidence: 91%

“…The groups showed a mixed pattern of excitatory and inhibitory connections within DMN. However, outgoing connections from HPC to other DMN regions were inhibitory across all groups, while interhemispheric HPC connections were excitatory, which is in line with a recent study investigating effective connectivity in healthy elderly and demented patients 47 . Missing entries correspond to redundant connections that have been removed following Bayesian model reduction (that did not differ from their prior expectation).…”

Section: Group Meanssupporting

confidence: 90%

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Neural mechanisms of disease pathology and cognition in young-onset Alzheimer’s Disease variants

Sacu,

Slattery,

Friston

et al. 2024

Preprint

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Young-onset Alzheimer's Disease(AD) is a rare form of AD characterized by early symptom onset (< 65 years) and heterogeneous clinical phenotypes. Previous studies have consistently shown that patients with late-onset AD exhibit alterations in the default mode network-a large-scale brain network associated with self-related processing and autobiographical memory. However, the functional organization of the default-mode network is far less clear in young-onset AD. Here, we assessed default-mode network effective connectivity in two common young-onset AD variants (i.e., typical amnestic variant and posterior cortical atrophy) and healthy participants to identify disease- and variant-specific differences in the default-mode network. This case-control study was conducted with thirty-nine young-onset AD patients, including typical amnestic (n = 26, 15 females, mean age = 61) and posterior cortical atrophy (n = 13; 8 females, mean age = 61.8), and 24 age-matched healthy participants (13 females, mean age=60.1). All participants underwent resting-state functional MRI and extensive neuropsychological testing. Spectral dynamic causal modelling was performed to quantify resting-state effective connectivity between default-mode network regions. Parametric empirical Bayes analysis was then performed to characterise group differences in effective connectivity. Our results showed that patients with typical AD variant showed increased connectivity from medial prefrontal cortex to posterior default-mode network nodes as well as reduced inhibitory connectivity from hippocampus to other default-mode network nodes, relative to healthy controls. Patients with posterior cortical atrophy exhibited decreased connectivity from posterior cingulate cortex to medial prefrontal cortex and bilateral angular gyrus and reduced inhibitory connectivity from left hippocampus to other default-mode network nodes compared to healthy controls. Right hippocampus connectivity differentiated the two patient groups. Patients with typical AD variant had lower inhibitory connectivity from right hippocampus to other default-mode network nodes than the patients with posterior cortical atrophy. Our findings suggest that resting-state default-mode network connectivity is a physiological phenotype of young-onset AD that could contribute to a new understanding offunctional integration in this condition.

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Section: Predicting Group Membership and Cognitive Performancesupporting

confidence: 91%

Section: Group Meanssupporting

confidence: 90%

Neural mechanisms of disease pathology and cognition in young-onset Alzheimer’s Disease variants

Sacu,

Slattery,

Friston

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Behavior- and circuit-specific cortico-striatal decoupling during the early development of Parkinson’s disease-like syndrome

Yao,

Liu,

Zheng

et al. 2024

Preprint

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Despite that cortico-striatal decoupling has been widely reported in individuals diagnosed with Parkinson's Disease (PD), its onset, circuit specificity and underlying mechanism remain largely unclear. To investigate these questions, dual fiber photometry is established for the first time to evaluate cortico-striatal coupling during varied motor behaviors, whose cell-type resolution was provided by the usage of Cre transgenic mouse lines. Contralateral turning, digging and licking show distinct coupling patterns, among which digging induces the strongest coupling. Striatal D1R-expressed medium spiny neurons (dMSNs) and D2R-expressed MSNs (iMSNs) similarly contribute to the cortical-striatal coupling during turning and licking but not digging, with much tighter coupling between the dMSNs and the M1 cortex. In PD-like mouse model induced via intra-striatal injection of synthetic mouse wildtype α-synuclein preformed fibril (PFF), digging-associated cortical-striatal decoupling emerges as early as 1-month post induction (Mpi), which becomes significant since 2 Mpi and correlates with later-onset behavioral deficit. Notably, impaired dMSNs but not iMSNs mediate this decoupling, which can be rescued by activation of D1 but not D2 receptor. Mechanistically, we found an inverted U-shape decline in striatal dopamine level along the disease development in PFF-injected mice. Supplement with L-DOPA alleviates the decoupling and motor deficit, suggesting that early dopamine deficiency directly contributes to the cortical-striatal decoupling and the associated motor deficit. These findings provide new insights into the temporal profile and mechanisms underlying the PD-associated cortico-striatal decoupling, which has been implicated as functional biomarker for early diagnosis of PD.

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Detection of Alzheimer’s Disease Using Hybrid Meta-ROI of MRI Structural Images

Zheng

2024

Diagnostics

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Background: The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer’s disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD. Methods: The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification. Results: An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%. Conclusions: Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer’s disease.

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Early detection of dementia with default-mode network effective connectivity

Cited by 3 publications

References 81 publications

Neural mechanisms of disease pathology and cognition in young-onset Alzheimer’s Disease variants

Neural mechanisms of disease pathology and cognition in young-onset Alzheimer’s Disease variants

Behavior- and circuit-specific cortico-striatal decoupling during the early development of Parkinson’s disease-like syndrome

Detection of Alzheimer’s Disease Using Hybrid Meta-ROI of MRI Structural Images

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