scholarly journals On the feasibility of concurrent human TMS-EEG-fMRI measurements

2013 ◽  
Vol 109 (4) ◽  
pp. 1214-1227 ◽  
Author(s):  
Judith C. Peters ◽  
Joel Reithler ◽  
Teresa Schuhmann ◽  
Tom de Graaf ◽  
Kâmil Uludağ ◽  
...  
Keyword(s):  
Functional Mri ◽  
Multimodal Imaging ◽  
Added Value ◽  
Steady Increase ◽  
Experimental Session ◽  
Temperature Changes ◽  
Complementary Approach ◽  
Complementary Assets ◽  
Functional Relevance ◽  
Comparable Image Quality

Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware.

scholarly journals tDCS-Induced Memory Reconsolidation Effects and Its Associations With Structural and Functional MRI Substrates in Subjective Cognitive Decline

2021 ◽  
Vol 13 ◽  
Author(s):  
Lídia Vaqué-Alcázar ◽  
Lídia Mulet-Pons ◽  
Kilian Abellaneda-Pérez ◽  
Cristina Solé-Padullés ◽  
María Cabello-Toscano ◽  
...  
Keyword(s):  
At Risk ◽  
Cognitive Decline ◽  
Functional Mri ◽  
Memory Performance ◽  
Verbal Learning ◽  
Learning Task ◽  
Memory Reconsolidation ◽  
Subjective Cognitive Decline ◽  
Experimental Session ◽  
Double Blind

Previous evidence suggests that transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (l-DLPFC) can enhance episodic memory in subjects with subjective cognitive decline (SCD), known to be at risk of dementia. Our main goal was to replicate such findings in an independent sample and elucidate if baseline magnetic resonance imaging (MRI) characteristics predicted putative memory improvement. Thirty-eight participants with SCD (aged: 60–65 years) were randomly assigned to receive active (N = 19) or sham (N = 19) tDCS in a double-blind design. They underwent a verbal learning task with 15 words (DAY-1), and 24 h later (DAY-2) stimulation was applied for 15 min at 1.5 mA targeting the l-DLPFC after offering a contextual reminder. Delayed recall and recognition were measured 1 day after the stimulation session (DAY-3), and at 1-month follow-up (DAY-30). Before the experimental session, structural and functional MRI were acquired. We identified a group∗time interaction in recognition memory, being the active tDCS group able to maintain stable memory performance between DAY-3 and DAY-30. MRI results revealed that individuals with superior tDCS-induced effects on memory reconsolidation exhibited higher left temporal lobe thickness and greater intrinsic FC within the default-mode network. Present findings confirm that tDCS, through the modulation of memory reconsolidation, is capable of enhancing performance in people with self-perceived cognitive complaints. Results suggest that SCD subjects with more preserved structural and functional integrity might benefit from these interventions, promoting maintenance of cognitive function in a population at risk to develop dementia.

scholarly journals Network degeneration in Parkinson’s disease: multimodal imaging of nigro-striato-cortical dysfunction

Brain ◽  
2020 ◽  
Vol 143 (3) ◽  
pp. 944-959 ◽  
Author(s):  
Marina C Ruppert ◽  
Andrea Greuel ◽  
Masoud Tahmasian ◽  
Frank Schwartz ◽  
Sophie Stürmer ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Fdg Pet ◽  
Multimodal Imaging ◽  
Substantia Nigra Pars Compacta ◽  
Dopamine Depletion ◽  
18F Fdg

Abstract The spreading hypothesis of neurodegeneration assumes an expansion of neural pathologies along existing neural pathways. Multimodal neuroimaging studies have demonstrated distinct topographic patterns of cerebral pathologies in neurodegeneration. For Parkinson’s disease the hypothesis so far rests largely on histopathological evidence of α-synuclein spreading in a characteristic pattern and progressive nigrostriatal dopamine depletion. Functional consequences of nigrostriatal dysfunction on cortical activity remain to be elucidated. Our goal was to investigate multimodal imaging correlates of degenerative processes in Parkinson’s disease by assessing dopamine depletion and its potential effect on striatocortical connectivity networks and cortical metabolism in relation to parkinsonian symptoms. We combined 18F-DOPA-PET, 18F-fluorodeoxyglucose (FDG)-PET and resting state functional MRI to multimodally characterize network alterations in Parkinson’s disease. Forty-two patients with mild-to-moderate stage Parkinson’s disease and 14 age-matched healthy control subjects underwent a multimodal imaging protocol and comprehensive clinical examination. A voxel-wise group comparison of 18F-DOPA uptake identified the exact location and extent of putaminal dopamine depletion in patients. Resulting clusters were defined as seeds for a seed-to-voxel functional connectivity analysis. 18F-FDG metabolism was compared between groups at a whole-brain level and uptake values were extracted from regions with reduced putaminal connectivity. To unravel associations between dopaminergic activity, striatocortical connectivity, glucose metabolism and symptom severity, correlations between normalized uptake values, seed-to-cluster β-values and clinical parameters were tested while controlling for age and dopaminergic medication. Aside from cortical hypometabolism, 18F-FDG-PET data for the first time revealed a hypometabolic midbrain cluster in patients with Parkinson’s disease that comprised caudal parts of the bilateral substantia nigra pars compacta. Putaminal dopamine synthesis capacity was significantly reduced in the bilateral posterior putamen and correlated with ipsilateral nigral 18F-FDG uptake. Resting state functional MRI data indicated significantly reduced functional connectivity between the dopamine depleted putaminal seed and cortical areas primarily belonging to the sensorimotor network in patients with Parkinson’s disease. In the inferior parietal cortex, hypoconnectivity in patients was significantly correlated with lower metabolism (left P = 0.021, right P = 0.018). Of note, unilateral network alterations quantified with different modalities corresponded with contralateral motor impairments. In conclusion, our results support the hypothesis that degeneration of nigrostriatal fibres functionally impairs distinct striatocortical connections, disturbing the efficient interplay between motor processing areas and impairing motor control in patients with Parkinson’s disease. The present study is the first to reveal trimodal evidence for network-dependent degeneration in Parkinson’s disease by outlining the impact of functional nigrostriatal pathway impairment on striatocortical functional connectivity networks and cortical metabolism.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Targeting Default Mode Network Dysfunction in Persons at Risk of Alzheimer’s Disease with Transcranial Magnetic Stimulation (NEST4AD): Rationale and Study Design

2021 ◽  
Vol 83 (4) ◽  
pp. 1877-1889
Author(s):  
Michela Pievani ◽  
Anna Mega ◽  
Giulia Quattrini ◽  
Giacomo Guidali ◽  
Clarissa Ferrari ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
At Risk ◽  
Transcranial Magnetic Stimulation ◽  
Functional Mri ◽  
Default Mode Network ◽  
Resting State ◽  
Multimodal Imaging ◽  
Magnetic Stimulation ◽  
Default Mode ◽  
E4 Allele

Background: Default mode network (DMN) dysfunction is well established in Alzheimer’s disease (AD) and documented in both preclinical stages and at-risk subjects, thus representing a potential disease target. Multi-sessions of repetitive transcranial magnetic stimulation (rTMS) seem capable of modulating DMN dynamics and memory in healthy individuals and AD patients; however, the potential of this approach in at-risk subjects has yet to be tested. Objective: This study will test the effect of rTMS on the DMN in healthy older individuals carrying the strongest genetic risk factor for AD, the Apolipoprotein E (APOE) ɛ4 allele. Methods: We will recruit 64 older participants without cognitive deficits, 32 APOE ɛ4 allele carriers and 32 non-carriers as a reference group. Participants will undergo four rTMS sessions of active (high frequency) or sham DMN stimulation. Multimodal imaging exam (including structural, resting-state, and task functional MRI, and diffusion tensor imaging), TMS with concurrent electroencephalography (TMS-EEG), and cognitive assessment will be performed at baseline and after the stimulation sessions. Results: We will assess changes in DMN connectivity with resting-state functional MRI and TMS-EEG, as well as changes in memory performance in APOE ɛ4 carriers. We will also investigate the mechanisms underlying DMN modulation through the assessment of correlations with measures of neuronal activity, excitability, and structural connectivity with multimodal imaging. Conclusion: The results of this study will inform on the physiological and cognitive outcomes of DMN stimulation in subjects at risk for AD and on the possible mechanisms. These results may outline the design of future non-pharmacological preventive interventions for AD.

Analysis of surface temperature changes in the foothill zone of South Russia on the example of Kislovodsk

2020 ◽  
Vol 20 (3) ◽  
pp. 69-78
Author(s):  
A.A. Tashilova ◽  
Keyword(s):  
Hurst Exponent ◽  
Statistical Significance ◽  
Surface Air Temperature ◽  
Steady Increase ◽  
Instrumental Data ◽  
Temperature Changes ◽  
Distribution Shape ◽  
Southern Russia ◽  
The Stability ◽  
Selection Of

Based on the series of surface air temperature (average, absolute maxima, absolute minima) for 1961-2018, obtained from instrumental data from the Kislovodsk station, an analysis of changes in seasonal and annual temperatures is carried out. We obtained averaged values in the base (1961-1990, normal) and modern (1991-2018) periods, the results of the t-test for determining the statistical equality/inequality of temperatures for two sub-periods, as well as characteristics of the distribution shape (asymmetry, kurtosis), the rate of temperature change with the criterion of statistical significance (F-test), the value of the Hurst exponent H for determining the stability of the series. Based on the results of statistical and fractal analysis, it can be concluded about a steady increase in temperatures (with the selection of summer averages, maximum and minimum) in the foothill zone of southern Russia (Kislovodsk).

scholarly journals P11.13 Radiotherapy combined with a multimodal imaging approach in a glioblastoma preclinical model

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii45-iii45
Author(s):  
D Salvatore ◽  
G Shaw ◽  
J Wright ◽  
I Teh ◽  
J Koch-Paszkowski ◽  
...  
Keyword(s):  
Treatment Response ◽  
Brain Tissue ◽  
Cell Biology ◽  
Multimodal Imaging ◽  
Fold Increase ◽  
Transport Systems ◽  
Steady Increase ◽  
Preclinical Model ◽  
Cellular Transport ◽  
Time Activity

Abstract BACKGROUND Glioblastoma multiforme (GBM) carries a poor prognosis, partly due to biological and anatomical heterogeneity. Although radiotherapy (RT) is effective, high doses damage surrounding healthy tissues. Multimodal imaging with Magnetic Resonance (MRI) and Positron Emission Tomography (PET) may represent a useful approach for identifying GBM heterogeneity and visualising metabolic tumour properties. PET radiotracer [18F]-fluciclovine is preferentially accumulated in gliomas compared to healthy brain tissue via the cellular transport systems, LAT1 and ASCT2. In this study the effect of fractionated RT using multimodal imaging including [18F]-fluciclovine uptake and immunohistochemistry (IHC) in a GBM preclinical model will be validated. MATERIAL AND METHODS Two C57BL/6J mice cohorts were injected intracranially (i.c.) with murine CT2A-luc cells and subsequently submitted to multiparametric MRI and [18F]-fluciclovine PET imaging during hemi-brain RT (3Gy on 2 days/each week) for maximum 25 days after i.c. injection. Brains were collected for IHC characterization including LAT1 and ASCT2 staining. RESULTS Preliminary data showed that both MRI and PET were effective modalities to track tumour growth in this model. PET data revealed up to greater than 3-fold increase in SUVmax from regions of interest around the tumour site compared to healthy brain tissue. Time activity curves showed a steady increase in tumour uptake over 90 minutes. MRI showed a 25% increase in T2 values in tumours relative to unaffected contralateral regions. Confirmation of treatment response through matched imaging and IHC are ongoing, from which changes in glioma cell biology as well as amino acid transporter protein levels will be analysed. CONCLUSION These preliminary results show that multimodal imaging presents novel data in the assessment of treatment response in this model and will permit parallel IHC analyses to better define GBM tumour heterogeneity aligned with imaging changes. These data will also inform an on-going clinical study using the same imaging modalities. Work at authors’ labs are supported by an Investigator initiated project from Blue Earth Diagnostics (AS, SCS) and a University of Leeds Biswas studentship (SCS, DS). Daniela Salvatore is also supported by a Scholarship provided by Molecular and Translational Medicine Doctorate School of University of Milan (Italy).

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Limitations: The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals COMPLEMENTARITY BETWEEN <i>IN SITU</i> STUDIES AND PHOTOGRAMMETRY: METHODOLOGICAL FEEDBACK FROM A ROMAN SHIPWRECK IN CAESAREA, ISRAEL

2019 ◽  
Vol XLII-2/W10 ◽  
pp. 77-83 ◽  
Author(s):  
B. Derenne ◽  
E. Nantet ◽  
G. Verly ◽  
M. Boone
Keyword(s):  
Error Rate ◽  
Three Dimensional ◽  
Added Value ◽  
Scientific Model ◽  
Production Methods ◽  
Complementary Approach ◽  
Indispensable Tool ◽  
Excavation Site

<p><strong>Abstract.</strong> As a quick and effective way to archive the different stages of an excavation - notably to prepare the post-excavation phase and to document the production methods – photogrammetry has become an indispensable tool. Indeed, it offers a valid scientific model, usable by any member of the team and at any moment, without the need to return to the excavation site. Photogrammetry can also complement other archaeological tools such as manual surveys. The interaction between the complementary approach of the interpretative drawing measurements (IDM) and the photogrammetric model measurements (PMM) enables us to apprehend the error rate of the interpretative measurements <i>in situ</i>. It appears thus that the measurements taken flat have an error rate inferior to 2% whereas the distances that are either too long or taken on a three-dimensional support have an error rate that can exceed 10%. The input of photogrammetry is therefore an added value whether it be during the excavation phase or during the post-excavation studies.</p>

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Intersubject consistent dynamic connectivity during natural vision revealed by functional MRI

2019 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Functional Mri ◽  
Brain Regions ◽  
List Type ◽  
Regional Activity ◽  
Natural Vision ◽  
Complementary Approach ◽  
Window Technique ◽  
Time Courses ◽  
Task Conditions ◽  
Dynamic Connectivity

AbstractThe functional communications between brain regions are thought to be dynamic. However, it is usually difficult to elucidate whether the observed dynamic connectivity is functionally meaningful or simply due to noise during unconstrained task conditions such as resting-state. During naturalistic conditions, such as watching a movie, it has been shown that local brain activities, e.g. in the visual cortex, are consistent across subjects. Following similar logic, we propose to study intersubject correlations of the time courses of dynamic connectivity during naturalistic conditions to extract functionally meaningful dynamic connectivity patterns. We analyzed a functional MRI (fMRI) dataset when the subjects watched a short animated movie. We calculated dynamic connectivity by using sliding window technique, and quantified the intersubject correlations of the time courses of dynamic connectivity. Although the time courses of dynamic connectivity are thought to be noisier than the original signals, we found similar level of intersubject correlations of dynamic connectivity to those of regional activity. Most importantly, highly consistent dynamic connectivity could occur between regions that did not show high intersubject correlations of regional activity, and between regions with little stable functional connectivity. The analysis highlighted higher order brain regions such as the default mode network that dynamically interacted with posterior visual regions during the movie watching, which may be associated with the understanding of the movie.HighlightsIntersubject consistency may provide a complementary approach to study brain dynamic connectivityWidespread brain regions showed highly consistent dynamic connectivity during movie watching, while these regions themselves did not show highly consistent regional activityConsistent dynamic connectivity often occurred between regions from different functional systems

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