Breakdown of specific functional brain networks in clinical variants of Alzheimer’s disease

Pini, Lorenzo; Wennberg, Alexandra; Salvalaggio, Alessandro; Vallesi, Antonino; Pievani, Michela; Corbetta, Maurizio

doi:10.1016/j.arr.2021.101482

Cited by 27 publications

(16 citation statements)

References 179 publications

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“…Alzheimer's disease (AD) represents the most common type of neurodegenerative disease [1]. Due to its multifactorial nature, the gradual cognitive decline is associated with the interaction of multiple pathological alterations, including brain atrophy, amyloid plaques deposition, and neurofibrillary tangles formation, which ultimately lead to a clinical diagnosis of dementia [2][3][4][5][6]. Despite abnormal amyloid markers being recognized as an early indicator or risk factor of AD, it has been suggested to refrain from giving an AD diagnosis to patients with abnormal protein builds-up but normal cognitive functioning [7].…”

Section: Introductionmentioning

confidence: 99%

Transcranial magnetic stimulation treatment in Alzheimer’s disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

et al. 2022

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Alzheimer’s disease (AD) represents the most common type of neurodegenerative disorder. Although our knowledge on the causes of AD remains limited and no curative treatments are available, several interventions have been proposed in trying to improve patients’ symptomatology. Among those, transcranial magnetic stimulation (TMS) has been shown a promising, safe and noninvasive intervention to improve global cognitive functioning. Nevertheless, we currently lack agreement between research studies on the optimal stimulation protocol yielding the highest efficacy in these patients. To answer this query, we conducted a systematic literature search in PubMed, PsycINFO and Scopus databases and meta-analysis of studies published in the last 10 years (2010–2021) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Differently from prior published meta-analytic work, we investigated whether protocols that considered participants-specific neuroimaging scans for the selection of individualized stimulation targets held more successful outcomes compared to those relying on a generalized targeting selection criteria. We then compared the effect sizes of subsets of studies based on additional protocol characteristics (frequency, duration of intervention, number of stimulation sites, use of concomitant cognitive training and patients’ educational level). Our results confirm TMS efficacy in improving global cognitive functioning in mild-to-moderate AD patients, but also highlight the flaws of current protocols characteristics, including a possible lack of sufficient personalization in stimulation protocols.

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Section: Introductionmentioning

confidence: 99%

Transcranial magnetic stimulation treatment in Alzheimer’s disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

et al. 2022

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“…These variants show different weight of the amyloid cascade and increasing weight of stochastic factors (e.g., environmental, risk genes) ( 13 ), rather than considering Aβ as a sufficient and necessary causative agent. Additionally, AD is characterized by different clinical phenotypes, such as the “language” variant (primary progressive aphasia), the “visual” variant (posterior cortical atrophy), and the “behavioral” variant ( 25, 26 ). To date, it is still unclear whether different spatio-temporal Aβ trajectories are linked to these different variants, in contrast to τ PET ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of misfolded proteins spreading in Alzheimer’s disease using machine learning

Gherardini¹,

Pestka²,

Pini

et al. 2022

Preprint

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The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight two specific misfolded proteins in its development: Amyloid-beta and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on a machine learning approach. The proposed method applies an autoregressive model, constrained by structural connectivity, to predict concentrations of Amyloid-beta two years after the provided baseline. In experiments, the autoregressive model generally outperformed the state-of-art models yielding the lowest average prediction error (mean squared-error 0.0062). Moreover, we assess its effectiveness and suitability for real case scenarios, for which we provide a web service for physicians and researchers. Despite predicting amyloid pathology alone is not sufficient to clinical outcome, its prediction can be helpful to further plan therapies and other cures.

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“…In typical AD, breakdown of DMN is linked with core symptoms, i.e., impaired episodic memory [ 13 ]. By contrast, bvFTD manifests reduced FC of the VAN (also referred to as salience network) that is linked with clinical severity [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A low-dimensional cognitive-network space in Alzheimer’s disease and frontotemporal dementia

et al. 2022

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Background Alzheimer’s disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. Methods We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses. Results Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). Conclusions A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.

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Breakdown of specific functional brain networks in clinical variants of Alzheimer’s disease

Cited by 27 publications

References 179 publications

Transcranial magnetic stimulation treatment in Alzheimer’s disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

Transcranial magnetic stimulation treatment in Alzheimer’s disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

Prediction of misfolded proteins spreading in Alzheimer’s disease using machine learning

A low-dimensional cognitive-network space in Alzheimer’s disease and frontotemporal dementia

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