Neurodegenerative disease of the brain: a survey of interdisciplinary approaches

Journal of The Royal Society Interface, Journal Year: 2023, Volume and Issue: 20(198)

Published: Jan. 1, 2023

Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over last decade. Interdisciplinary research is improving understanding these this article reviews such approaches, particular emphasis on tools techniques drawn from physics, chemistry, artificial intelligence psychology.

Language: Английский

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Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

EBioMedicine, Journal Year: 2023, Volume and Issue: 90, P. 104540 - 104540

Published: March 25, 2023

Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, non-harmonised pipelines.We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied raw (unpreprocessed) 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, healthy controls; both male female as self-reported by participants). We tested our results demographically matched unmatched discard possible biases performed multiple out-of-sample validations.Robust classification across all groups were achieved 3T North, which also generalised Latin America. Moreover, non-standardised, routine 1.5T clinical images These generalisations robust heterogenous recordings not confounded demographics (i.e., samples, when incorporating variables multifeatured model). Model interpretability analysis occlusion sensitivity evidenced core pathophysiological regions for each (mainly hippocampus AD, insula bvFTD) demonstrating biological specificity plausibility.The generalisable approach outlined here could be used future aid clinician decision-making samples.The specific funding this article is provided acknowledgements section.

Language: Английский

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Source space connectomics of neurodegeneration: One-metric approach does not fit all

Neurobiology of Disease, Journal Year: 2023, Volume and Issue: 179, P. 106047 - 106047

Published: Feb. 23, 2023

Brain functional connectivity in dementia has been assessed with dissimilar EEG metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different may allow for a more comprehensive characterization of brain interactions subtypes. To test hypothesis, resting-state electroencephalogram (rsEEG) was recorded individuals Alzheimer's Disease (AD), behavioral variant frontotemporal (bvFTD), healthy controls (HCs). Whole-brain estimated the source space using 101 types connectivity, capturing linear nonlinear both time frequency-domains. Multivariate machine learning progressive feature elimination run to discriminate AD HCs, bvFTD based on i) frequency bands, ii) complementary frequency-domain (e.g., instantaneous, lagged, total connectivity), iii) time-domain linearity assumption Pearson correlation coefficient mutual information). <10% all possible connections were responsible differences between patients controls, atypical never captured by >1/4 measures. Joint revealed patterns hypoconnectivity (patientsHCs) groups mainly identified regions. These atypicalities differently frequency- metrics, bandwidth-specific fashion. The multi-metric representation whole-brain evidenced inadequacy single-metric approaches, resulted valid alternative selection problem connectivity. reveal interdependence that are overlooked single contributing reliable interpretable description neurodegeneration.

Language: Английский

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Functional connectome through the human life span

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 13, 2023

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years 132 global sites. We report critical inflection points in nonlinear curves mean variance connectome, peaking late fourth third decades life, respectively. After constructing a fine-grained, lifespan-wide suite system-level brain atlases, show distinct maturation timelines for segregation within different systems. Lifespan regional connectivity is organized along primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities networks patients with autism spectrum disorder, major depressive Alzheimer's disease. findings elucidate evolution can serve as reference quantifying variation development, aging, neuropsychiatric disorders.

Language: Английский

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Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer’s disease

Cerebral Cortex, Journal Year: 2024, Volume and Issue: 34(3)

Published: March 1, 2024

Abstract Alzheimer’s disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies 20 AD studies. patients, compared controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior (PCC), right temporal lobes, left angular gyrus increased between DMN inferior gyrus. within mPFC/ACC precuneus/PCC occipital dorsolateral cortex. Conjunction analysis shared precuneus/PCC. Compared MCI, had lobes. share likely underpinning episodic memory deficits neuropsychiatric symptoms, differ alterations related impairments other domains such as language, vision, execution. This may throw light on neuropathological mechanisms two stages dementia.

Language: Английский

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Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer’s disease

Cell Reports, Journal Year: 2024, Volume and Issue: 43(2), P. 113691 - 113691

Published: Jan. 21, 2024

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ pathologies than others, gene expression may play a role. We study the association between brain-wide profiles regional vulnerability (gene-to-Aβ associations) (gene-to-tau by leveraging two large independent AD cohorts. identify susceptibility genes modules co-expression network with specifically related AD. In addition, we biochemical pathways associated gene-to-Aβ gene-to-tau associations. These findings explain discordance pathologies. Finally, propose an analytic framework, linking identified gene-to-pathology associations cognitive dysfunction at individual level, suggesting potential clinical implication of

Language: Английский

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The emergence of multiscale connectomics-based approaches in stroke recovery

Trends in Neurosciences, Journal Year: 2024, Volume and Issue: 47(4), P. 303 - 318

Published: Feb. 23, 2024

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments readout technologies progress network modeling have revolutionized current understanding the effects on at macroscale, reorganization smaller scale remains incompletely understood. In this review, we use conceptual framework graph theory to define from nano- macroscales. Highlighting stroke-related connectivity studies multiple scales, argue that multiscale connectomics-based approaches may provide new routes better evaluate structural functional remapping after during recovery.

Language: Английский

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NMNAT2 supports vesicular glycolysis via NAD homeostasis to fuel fast axonal transport

Molecular Neurodegeneration, Journal Year: 2024, Volume and Issue: 19(1)

Published: Jan. 29, 2024

Abstract Background Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by mononucleotide adenylyl transferase 2 (NMNAT2) CNS neurons. NMNAT2 mRNA levels reduced brains Alzheimer’s, Parkinson’s, Huntington’s disease. Here we addressed whether required axonal health cortical glutamatergic neurons, whose long-projecting axons vulnerable neurodegenerative conditions. We also tested if maintains ensuring ATP transport, critical function. Methods generated mouse cultured neuron models to determine impact loss from neurons on energetic morphological integrity. In addition, determined exogenous NAD supplementation or inhibiting a hydrolase, sterile alpha TIR motif-containing protein 1 (SARM1), prevented deficits caused loss. This study used combination techniques, including genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, anti-sense oligos. Results provide vivo evidence that survival. Using vitro studies, demonstrate NAD-redox potential “on-board” via glycolysis vesicular cargos distal axons. Exogenous + KO restores resumes fast transport. Finally, both reducing activity SARM1, degradation enzyme, can reduce transport suppress axon degeneration Conclusion ensures maintaining redox ensure efficient

Language: Английский

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Functional and effective EEG connectivity patterns in Alzheimer’s disease and mild cognitive impairment: a systematic review

Frontiers in Aging Neuroscience, Journal Year: 2025, Volume and Issue: 17

Published: Feb. 12, 2025

Background Alzheimer’s disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may largely attributable to disrupted communication between brain regions, rather than deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships signals in different regions. research on AD mild cognitive impairment (MCI), often considered prodromal phase AD, has produced mixed results yet synthesized for comprehensive review. Thus, we performed systematic review MCI participants compared cognitively healthy older adult controls. Methods We searched PsycINFO, PubMed, Web Science peer-reviewed studies English EEG, connectivity, MCI/AD relative Of 1,344 initial matches, 124 articles were ultimately included Results The primarily analyzed coherence, phase-locked, graph theory metrics. influence factors demographics, design, approach was integrated discussed. An overarching pattern emerged lower both controls, which most prominent alpha band, consistent AD. In minority reporting greater theta band commonly implicated MCI, followed by alpha. overall prevalence effects indicate its potential provide insight into nuanced changes associated AD-related networks, caveat during resting state where dominant frequency. When reported it task engagement, suggesting compensatory resources employed. common rest, engagement already exhausted. Conclusion highlighted powerful tool advance understanding communication. address need including demographic methodological details, using source space extending this work adults risk toward advancing early detection intervention.

Language: Английский

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Alzheimer’s Disease Prediction via Brain Structural-Functional Deep Fusing Network

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2023, Volume and Issue: 31, P. 4601 - 4612

Published: Jan. 1, 2023

Fusing structural-functional images of the brain has shown great potential to analyze deterioration Alzheimer's disease (AD). However, it is a big challenge effectively fuse correlated and complementary information from multimodal neuroimages. In this work, novel model termed cross-modal transformer generative adversarial network (CT-GAN) proposed functional structural contained in magnetic resonance imaging (fMRI) diffusion tensor (DTI). The CT-GAN can learn topological features generate connectivity data an efficient end-to-end manner. Moreover, swapping bi-attention mechanism designed gradually align common enhance between modalities. By analyzing generated features, identify AD-related connections. Evaluations on public ADNI dataset show that dramatically improve prediction performance detect regions effectively. also provides new insights into detecting abnormal neural circuits.

Language: Английский

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