scholarly journals Functional Imaging to Guide Network-Based TMS Treatments: Toward a Tailored Medicine Approach in Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Chiara Bagattini ◽  
Debora Brignani ◽  
Sonia Bonnì ◽  
Giulia Quattrini ◽  
Roberto Gasparotti ◽  
...  

A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer’s disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.

2020 ◽  
Author(s):  
Chiara Bagattini ◽  
Debora Brignani ◽  
Sonia Bonnì ◽  
Roberto Gasparotti ◽  
Michela Pievani

AbstractINTRODUCTIONTranscranial magnetic stimulation (TMS) has gained increasing attention as a potential therapeutic strategy in Alzheimer’s disease (AD). Among factors determining a clinical response, the choice of the stimulation site represents a key point. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability.METHODSSingle-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality.RESULTSThe distance between individual fMRI-derived coordinates and traditionally-defined targets was significant for a focality <12mm, but not for >20mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets.DISCUSSIONThe proposed network-based fMRI-guided TMS approach allows targeting disorder-specific networks meanwhile accounting for inter-individual functional variability in Alzheimer’s disease. This approach might represent a step toward tailored TMS interventions for AD.


2017 ◽  
Vol 38 (2) ◽  
pp. 304-316 ◽  
Author(s):  
Felix Winter ◽  
Catrin Bludszuweit-Philipp ◽  
Olaf Wolkenhauer

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer’s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia-driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.


NeuroImage ◽  
2002 ◽  
Vol 17 (3) ◽  
pp. 1403-1414 ◽  
Author(s):  
D. Prvulovic ◽  
D. Hubl ◽  
A.T. Sack ◽  
L. Melillo ◽  
K. Maurer ◽  
...  

Marine Drugs ◽  
2018 ◽  
Vol 16 (10) ◽  
pp. 386 ◽  
Author(s):  
Laura Llorach-Pares ◽  
Alfons Nonell-Canals ◽  
Conxita Avila ◽  
Melchor Sanchez-Martinez

Alzheimer’s disease (AD) is becoming one of the most disturbing health and socioeconomic problems nowadays, as it is a neurodegenerative pathology with no treatment, which is expected to grow further due to population ageing. Actual treatments for AD produce only a modest amelioration of symptoms, although there is a constant ongoing research of new therapeutic strategies oriented to improve the amelioration of the symptoms, and even to completely cure the disease. A principal feature of AD is the presence of neurofibrillary tangles (NFT) induced by the aberrant phosphorylation of the microtubule-associated protein tau in the brains of affected individuals. Glycogen synthetase kinase-3 beta (GSK3β), casein kinase 1 delta (CK1δ), dual-specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) and dual-specificity kinase cdc2-like kinase 1 (CLK1) have been identified as the principal proteins involved in this process. Due to this, the inhibition of these kinases has been proposed as a plausible therapeutic strategy to fight AD. In this study, we tested in silico the inhibitory activity of different marine natural compounds, as well as newly-designed molecules from some of them, over the mentioned protein kinases, finding some new possible inhibitors with potential therapeutic application.


Author(s):  
Reisa A. Sperling ◽  
Thomas A. Sandson ◽  
Keith A. Johnson

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.


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