Scholarly Publications - UMRAM

Permanent URI for this collectionhttps://hdl.handle.net/11693/115674

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Open Access
Minimizing electric fields and increasing peripheral nervestimulation thresholds using a body gradient array coil
(John Wiley & Sons, Inc., 2024-04-16) Babaloo, Reza; Atalar, Ergin
**Purpose:** To demonstrate the performance of gradient array coils in minimizing switched-gradient-induced electric fields (E-fields) and improving peripheral nerve stimulation (PNS) thresholds while generating gradient fields with adjustable linearity across customizable regions of linearity (ROLs). **Methods:** A body gradient array coil is used to reduce the induced E-fields on the surface of a body model by modulating applied currents. This is achieved by performing an optimization problem with the peak E-field as the objective function and current amplitudes as unknown variables. Coil dimensions and winding patterns are fixed throughout the optimization, whereas other engineering metrics remain adjustable. Various scenarios are explored by manipulating adjustable parameters. **Results:** The array design consistently yields lower E-fields and higher PNS thresholds across all scenarios compared with a conventional coil. When the gradient array coil generates target gradient fields within a 44-cm-diameter spherical ROL, the maximum E-field is reduced by 10%, 18%, and 61% for the X, Y, and Z gradients, respectively. Transitioning to a smaller ROL (24 cm) and relaxing the gradient linearity error results in further E-field reductions. In oblique gradients, the array coil demonstrates the most substantial reduction of 40% in the Z–Y direction. Among the investigated scenarios, the most significant increase of 4.3-fold is observed in the PNS thresholds. **Conclusion:** Our study demonstrated that gradient array coils offer a promising pathway toward achieving high-performance gradient coils regarding gradient strength, slew rate, and PNS thresholds, especially in scenarios in which linear magnetic fields are required within specific target regions.
Open Access
Comparison of angiographic outcomes of woven endobridge and balloon‐assisted coiling for the treatment of ruptured wide‐necked aneurysms: a multicentric study
(John Wiley & Sons, Inc., 2024-05) Rodriguez-Calienes, Aaron; Vivanco-Suarez, Juan; Galecio-Castillo, Milagros; Dibas, Mahmoud; Gross, Bradley; Farooqui, Mudassir; Algın, Oktay; Kılıç, Türker; Güneş, Yasin Celal; Feigen, Chaim; Samaniego, Edgar A.; Altschul, David J.; Ortega-Gutierrez, Santiago
BACKGROUND: The optimal endovascular approach for acutely ruptured wide-neck intracranial aneurysms remains uncertain, and the use of stent-assisted coiling or flow diversion is controversial due to antiplatelet therapy requirements and potential risks. Various techniques have been developed to address these challenges, including balloon-assisted coiling (BAC) and intrasaccular flow-disruption. The Woven EndoBridge (WEB) is an intrasaccular device that has shown a favorable efficacy and safety profile for ruptured aneurysms with minimal rebleeding rates. We aimed to compare the clinical and radiological outcomes between WEB and BAC in a cohort of patients with ruptured wide-necked intracranial aneurysms. METHODS: In this international multicenter cohort study, we included consecutive patients treated for ruptured wide-neck intracranial aneurysms with either WEB or BAC at 4 neurovascular centers. The primary effectiveness outcome was complete aneurysm occlusion at the final imaging follow-up using the Raymond–Roy scale. Secondary outcomes included a composite of periprocedural hemorrhagic/ischemia-related complications and favorable functional outcome. RESULTS: The study included 104 patients treated with WEB and 107 patients treated with BAC. Of the patients, 60.5% in the WEB group and 53% in the BAC group achieved complete occlusion, with no significant difference between the 2 groups after adjusting for covariates (adjusted odds ratio [OR] = 1.02; 95% CI 0.46–2.25; P = 0.964). The odds of favorable functional outcome did not significantly differ between the WEB (74.8%) and BAC groups (77.4%, adjusted OR = 1.45; 95% CI 0.65– 3.24; P = 0.368). Procedure-related complications were similar in both groups (WEB: 9.6%, BAC: 10.3%, P = 0.872), with no significant difference observed in the rates of ischemic events (WEB: 6.7% versus BAC: 2.8%; P = 0.180) and hemorrhagic events (WEB: 3.8% versus BAC: 7.5%; P = 0.255) between the 2 groups. CONCLUSION: In conclusion, both WEB and BAC techniques showed similar effectiveness and safety outcomes in treating ruptured wide-neck intracranial aneurysms. Further prospective comparative studies are needed to better guide treatment decisions for this patient population.
Open Access
Exploring cognitive enhancements and default mode network connectivity in relapsing-remitting multiple sclerosis: insights from a prospective study investigating the mind diet
(Sage Publications Ltd., 2024-09) Demirel, Mert; Daşgın, Hacer; Acar, Nazire Pınar; Özçelik Erğlu, Elçin; Atabilen, Büşra; Ertuğrul Aygün; Akdevelioğlu, Yasemin; Oğuz, Kader Karlı; Tuncer, Meryem Aslı
Open Access
VISPool: enhancing transformer encoders with vector visibility graph neural networks
(Association for Computational Linguistics, 2024-08-16) Alikaşifoğlu, Tuna; Aras, Arda Can; Koç, Aykut
The emergence of transformers has revolutionized natural language processing (NLP), as evidenced in various NLP tasks. While graph neural networks (GNNs) show recent promise in NLP, they are not standalone replacements for transformers. Rather, recent research explores combining transformers and GNNs. Existing GNN-based approaches rely on static graph construction methods requiring excessive text processing, and most of them are not scalable with the increasing document and word counts. We address these limitations by proposing a novel dynamic graph construction method for text documents based on vector visibility graphs (VVGs) generated from transformer output. Then, we introduce visibility pooler (VISPool), a scalable model architecture that seamlessly integrates VVG convolutional networks into transformer pipelines. We evaluate the proposed model on the General Language Understanding Evaluation (GLUE) benchmark datasets. VISPool outperforms the baselines with less trainable parameters, demonstrating the viability of the visibility-based graph construction method for enhancing transformers with GNNs. © 2024 Association for Computational Linguistics.
Open Access
Corrigendum to “natural language processing for defining linguistic features in schizophrenia: a sample from Turkish speakers” [Schizophr. Res. 266 (2024) 183–189]
(Elsevier BV, 2024-12) Çabuk, Tuğçe; Sevim, Nurullah; Mutlu, Emre; Yağcıoğlu, A. Elif Anıl; Koç, Aykut; Toulopoulou, Timothea
Open Access
Neural correlates of dynamic lightness induction
(Association for Research in Vision and Ophthalmology, 2024-09) Malik, Amna; Boyacı, Hüseyin
The lightness of a surface depends not only on the amount of light reflected off, it but also on the context in which it is embedded. Despite a long history of research, neural correlates of context-dependent lightness perception remain a topic of ongoing debate. Here, we seek to expand on the existing literature by measuring functional magnetic resonance imaging (fMRI) responses to lightness variations induced by the context. During the fMRI experiment, we presented 10 participants with a dynamic stimulus in which either the luminance of a disk or its surround is modulated at four different frequencies ranging from 1 to 8 Hz. Behaviorally, when the surround luminance is modulated at low frequencies, participants perceive an illusory change in the lightness of the disk (lightness induction). In contrast, they perceive little or no induction at higher frequencies. Using this frequency dependence and controlling for long-range responses to border contrast and luminance changes, we found that activity in the primary visual cortex (V1) correlates with lightness induction, providing further evidence for the involvement of V1 in the processing of context-dependent lightness.
Open Access
Graph fractional Fourier transform: a unified theory
(IEEE, 2024) Alikaşifoğlu, Tuna; Kartal, Bünyamin; Koç, Aykut
The fractional Fourier transform (FRFT) parametrically generalizes the Fourier transform (FT) by a transform order, representing signals in intermediate time-frequency domains. The FRFT has multiple but equivalent definitions, including the fractional power of FT, time-frequency plane rotation, hyper-differential operator, and many others, each offering benefits like derivational ease and computational efficiency. Concurrently, graph signal processing (GSP) extends traditional signal processing to data on irregular graph structures, enabling concepts like sampling, filtering, and Fourier transform for graph signals. The graph fractional Fourier transform (GFRFT) is recently extended to the GSP domain. However, this extension only generalizes the fractional power definition of FRFT based on specific graph structures with limited transform order range. Ideally, the GFRFT extension should be consistent with as many alternative definitions as possible. This paper first provides a rigorous fractional power-based GFRFT definition that supports any graph structure and transform order. Then, we introduce the novel hyper-differential operator-based GFRFT definition, allowing faster forward and inverse transform matrix computations on large graphs. Through the proposed definition, we derive a novel approach to select the transform order by learning the optimal value from data. Furthermore, we provide treatments of the core GSP concepts, such as bandlimitedness, filters, and relations to the other transforms in the context of GFRFT. Finally, with comprehensive experiments, including denoising, classification, and sampling tasks, we demonstrate the equivalence of parallel definitions of GFRFT, learnability of the transform order, and the benefits of GFRFT over GFT and other GSP methods.¹¹ The codebase is available at https://github.com/koc-lab/gfrft-unified.
Open Access
Graph receptive transformer encoder for text classification
(IEEE, 2024) Aras, Arda Can; Alikaşifoğlu, Tuna; Koç, Aykut
By employing attention mechanisms, transformers have made great improvements in nearly all NLP tasks, including text classification. However, the context of the transformer's attention mechanism is limited to single sequences, and their fine-tuning stage can utilize only inductive learning. Focusing on broader contexts by representing texts as graphs, previous works have generalized transformer models to graph domains to employ attention mechanisms beyond single sequences. However, these approaches either require exhaustive pre-training stages, learn only transductively, or can learn inductively without utilizing pre-trained models. To address these problems simultaneously, we propose the Graph Receptive Transformer Encoder (GRTE), which combines graph neural networks (GNNs) with large-scale pre-trained models for text classification in both inductive and transductive fashions. By constructing heterogeneous and homogeneous graphs over given corpora and not requiring a pre-training stage, GRTE can utilize information from both large-scale pre-trained models and graph-structured relations. Our proposed method retrieves global and contextual information in documents and generates word embeddings as a by-product of inductive inference. We compared the proposed GRTE with a wide range of baseline models through comprehensive experiments. Compared to the state-of-the-art, we demonstrated that GRTE improves model performances and offers computational savings up to ˜100×.
Open Access
Automated parameter selection for accelerated mri reconstruction via low-rank modeling of local k-space neighborhoods
(Elsevier GmbH, 2025-02) Ilıcak, Efe; Sarıtaş, Emine Ülkü; Çukur, Tolga
The publisher regrets that the declaration of competing interest statement was not included in the original article. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The publisher would like to apologise for any inconve nience caused.
Open Access
Boosting viscosity sensitivity of magnetic particle imaging using selection field gradients
(AIP Publishing LLC) Topçu, Atakan; Alpman, Aslı; Utkur, Mustafa; Sarıtaş, Emine Ülkü
In magnetic particle imaging (MPI), selection field (SF) gradients are utilized to form a field-free point (FFP) in space, such that only the magnetic nanoparticles (MNPs) in the vicinity of the FFP respond to the applied drive field (DF) and contribute to the received signal. While the relaxation behavior of MNPs adversely affects image quality by reducing signal intensity and causing blurring, it also provides MPI with functional imaging capabilities, such as viscosity and temperature mapping. This work investigates the effects of SF gradients on the relaxation behavior of the MNPs using an in-house magnetic particle spectrometer (MPS) setup equipped with an additional DC electromagnet SF coil, which switches the MPS setup into an MPI system. The results reveal that the presence of SF gradients boosts the viscosity sensitivity of MPI, and that the MPI signal can be sensitized to viscosity even at high DF frequencies and amplitudes if sufficiently large SF gradients are applied.
Open Access
DreaMR: diffusion driven counterfactual explanation for functional MRI
(IEEE, 2024-11-27) Bedel, Hasan Atakan; Çukur, Tolga
Deep learning analyses have offered sensitivity leaps in detection of cognition-related variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models perform hierarchical nonlinear transformations on fMRI data, interpreting the association between individual brain regions and the detected variables is challenging. Among explanation approaches for deep fMRI classifiers, attribution methods show poor specificity and perturbation methods show limited sensitivity. While counterfactual generation promises to address these limitations, previous counterfactual methods based on variational or adversarial priors can yield suboptimal sample fidelity. Here, we introduce the first diffusion-driven counterfactual method, DreaMR, to enable fMRI interpretation with high fidelity. DreaMR performs diffusion-based resampling of an input fMRI sample to alter the decision of a downstream classifier, and then computes the difference between the original sample and the counterfactual sample for explanation. Unlike conventional diffusion methods, DreaMR leverages a novel fractional multi-phase-distilled diffusion prior to improve inference efficiency without compromising fidelity, and it employs a transformer architecture to account for long-range spatiotemporal context in fMRI scans. Comprehensive experiments on neuroimaging datasets demonstrate the superior fidelity and efficiency of DreaMR in sample generation over state-of-the-art counterfactual methods for fMRI explanation.
Open Access
DEQ-MPI: a deep equilibrium reconstruction with learned consistency for magnetic particle imaging
(IEEE, 2024-01) Güngör, Alper; Askin, Baris; Soydan, Damla Alptekin; Top, Can Baris; Sarıtaş, Emine Ülkü; Çukur, Tolga
Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that is used to reconstruct data from subsequent scans. The ill-posed reconstruction problem can be solved by simultaneously enforcing data consistency based on the SM and regularizing the solution based on an image prior. Traditional hand-crafted priors cannot capture the complex attributes of MPI images, whereas recent MPI methods based on learned priors can suffer from extensive inference times or limited generalization performance. Here, we introduce a novel physics-driven method for MPI reconstruction based on a deep equilibrium model with learned data consistency (DEQ-MPI). DEQ-MPI reconstructs images by augmenting neural networks into an iterative optimization, as inspired by unrolling methods in deep learning. Yet, conventional unrolling methods are computationally restricted to few iterations resulting in non-convergent solutions, and they use hand-crafted consistency measures that can yield suboptimal capture of the data distribution. DEQ-MPI instead trains an implicit mapping to maximize the quality of a convergent solution, and it incorporates a learned consistency measure to better account for the data distribution. Demonstrations on simulated and experimental data indicate that DEQ-MPI achieves superior image quality and competitive inference time to state-of-the-art MPI reconstruction methods.
Open Access
CalibFPA: a focal plane array imaging system based on online deep learning calibration
(2024) Gungor, Alper; Bahceci, M. Umut; Ergen, Yasin; Sozak, Ahmet; Ekiz, O. Oner; Yelboga, Tolga; Çukur, Tolga
Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is often attained via electronically controllable spatial light modulators (SLM). To capture system non-idealities such as optical aberrations, a system matrix is measured via additional offline scans, where the system response is recorded for a point source at each spatial location on the imaging grid. An HR image can then be reconstructed by solving an inverse problem that involves encoded measurements and the calibration matrix. However, this offline calibration framework faces limitations due to challenges in encoding single HR grid locations with a fixed coded aperture, lengthy calibration scans repeated to account for system drifts, and computational burden of reconstructions based on dense system matrices. Here, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). To acquire multiplexed measurements, we devise an optical setup where a piezo-stage locomotes a pre-printed fixed coded aperture. We introduce a physics-driven deep-learning method to correct for the influences of optical aberrations in multiplexed measurements without the need for offline calibration scans. The corrected measurement matrix is of block-diagonal form, so it can be processed efficiently to recover HR images with a user-preferred reconstruction algorithm including least-squares, plug-and-play, or unrolled techniques. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity.
Open Access
Intrasaccular flow disruptor (woven endobridge) assisted embolization of vertebral arteriovenous fistulas
(Korean Soc Interventional Neuroradiology - KSIN, 2024-03) Algın, Oktay
Open Access
The use of macrocyclic contrast agents and woven endobridge-assisted embolization for direct carotid-cavernous fistulas due to aneurysm rupture
(Korean Soc Interventional Neuroradiology - KSIN, 2024-11) Algın, Oktay
Embargo
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
(ELSEVIER, 2024-05) Dalmaz, Onat; Mirza, Muhammad Usama; Elmas, Gökberk; Özbey, Muzaffer; Dar, Salman UI Hassan; Ceyani, Emir; Karlı Oğuz, Kader; Avestimehr, Salman; Çukur, Tolga
Curation of large, diverse MRI datasets via multi-institutional collaborations can help improve learningof generalizable synthesis models that reliably translate source- onto target-contrast images. To facilitatecollaborations, federated learning (FL) adopts decentralized model training while mitigating privacy concernsby avoiding sharing of imaging data. However, conventional FL methods can be impaired by the inherentheterogeneity in the data distribution, with domain shifts evident within and across imaging sites. Here weintroduce the first personalized FL method for MRI Synthesis (pFLSynth) that improves reliability against dataheterogeneity via model specialization to individual sites and synthesis tasks (i.e., source-target contrasts).To do this, pFLSynth leverages an adversarial model equipped with novel personalization blocks that controlthe statistics of generated feature maps across the spatial/channel dimensions, given latent variables specificto sites and tasks. To further promote communication efficiency and site specialization, partial networkaggregation is employed over later generator stages while earlier generator stages and the discriminatorare trained locally. As such, pFLSynth enables multi-task training of multi-site synthesis models with highgeneralization performance across sites and tasks. Comprehensive experiments demonstrate the superiorperformance and reliability of pFLSynth in MRI synthesis against prior federated methods
Open Access
The aesthetic experience of interior spaces with curvilinear boundaries and various space properties in immersive and desktop-based virtual environments
(American Psychological Association, 2024-12-05) Elver Boz, Tuğce; Demirkan, Halime; Ürgen, Burcu Ayşen
The study aims to investigate participants' aesthetic experience in response to environments with curvilinear boundaries that are presented in two different virtual environments (VEs), namely immersive (IVE) and desktop-based virtual environments (DTVE). To this end, 60 participants were presented with 360 degrees 32 VE visualizations that had either horizontal or vertical curvilinear boundaries and possessed various architectural properties (size/light/texture/color) using a head-mounted display and a desktop computer. The aesthetic experience in response to these visualizations was measured in terms of the three key dimensions identified in a previous study (Elver Boz et al., 2022): familiarity, excitement, and fascination. In addition, participants' sense of presence in the two different environments was measured. The results show that familiarity and excitement dimensions were significantly higher in IVE than in DTVE, whereas the two environments did not significantly differ from each other in terms of the fascination dimension. As for the boundary types, the familiarity dimension was significantly higher in horizontal curvilinear boundaries than in vertical ones. In contrast, excitement and fascination dimensions were significantly higher in vertical curvilinear boundaries than in horizontal ones. The only dimension that showed an interaction between boundary types and the type of VE was excitement. Finally, IVE induced a higher presence feeling than DTVE. Overall, results suggest that people's aesthetic experiences toward built environments change as a function of the boundary types and the medium they are presented with these environments and that different dimensions of the aesthetic experience are affected differently by these variables.
Embargo
Biodegradation by cancer cells of magnetite nanoflowers with and without encapsulation in ps-b-paa block copolymer micelles
(American Chemical Society, 2024-06-29) Benassai, Emilia; Daffe, Nieli; Aygun, Elif; Geeverding, Audrey; Ülkü Sarıtaş, Emine; Wilhelm, Claire; Abou-Hassan, Ali
Magnetomicelles were produced by the self-assembly of magnetite iron oxide nanoflowers and the amphiphilic poly(styrene)-b-poly(acrylic acid) block copolymer to deliver a multifunctional theranostic agent. Their bioprocessing by cancer cells was investigated in a three-dimensional spheroid model over a 13-day period and compared with nonencapsulated magnetic nanoflowers. A degradation process was identified and monitored at various scales, exploiting different physicochemical fingerprints. At a collective level, measurements were conducted using magnetic, photothermal, and magnetic resonance imaging techniques. At the nanoscale, transmission electron microscopy was employed to identify the morphological integrity of the structures, and X-ray absorption spectroscopy was used to analyze the degradation at the crystalline phase and chemical levels. All of these measurements converge to demonstrate that the encapsulation of magnetic nanoparticles in micelles effectively mitigates their degradation compared to individual nonencapsulated magnetic nanoflowers. This protective effect consequently resulted in better maintenance of their therapeutic photothermal potential. The structural degradation of magnetomicelles occurred through the formation of an oxidized iron phase in ferritin from the magnetic nanoparticles, leaving behind empty spherical polymeric ghost shells. These results underscore the significance of encapsulation of iron oxides in micelles in preserving nanomaterial integrity and regulating degradation, even under challenging physicochemical conditions within cancer cells.
Open Access
Charting brain GABA and glutamate levels across psychiatric disorders by quantitative analysis of 121 1H-MRS studies
(Cambridge University Press, 2024-11-20) Zhang, Jiayuan; Toulopoulou, Timothea; Li, Qian; Niu, Lijing; Peng, Lanxin; Dai, Haowei; Chen, Keyin; Wang, Xingqin; Huang, Ruiwang; Wei, Xinhua; Zhang, Ruibin
###### Background: Psychiatric diagnosis is based on categorical diagnostic classification, yet similarities in genetics and clinical features across disorders suggest that these classifications share commonalities in neurobiology, particularly regarding neurotransmitters. Glutamate (Glu) and gamma-aminobutyric acid (GABA), the brain's primary excitatory and inhibitory neurotransmitters, play critical roles in brain function and physiological processes. ###### Methods: We examined the levels of Glu, combined glutamate and glutamine (Glx), and GABA across psychiatric disorders by pooling data from 121 H-1-MRS studies and further divided the sample based on Axis I disorders. ###### Results: Statistically significant differences in GABA levels were found in the combined psychiatric group compared with healthy controls (Hedge's g = -0.112, p = 0.008). Further analyses based on brain regions showed that brain GABA levels significantly differed across Axis I disorders and controls in the parieto-occipital cortex (Hedge's g = 0.277, p = 0.019). Furthermore, GABA levels were reduced in affective disorders in the occipital cortex (Hedge's g = -0.468, p = 0.043). Reductions in Glx levels were found in neurodevelopmental disorders (Hedge's g = -0.287, p = 0.022). Analysis focusing on brain regions suggested that Glx levels decreased in the frontal cortex (Hedge's g = -0.226, p = 0.025), and the reduction of Glu levels in patients with affective disorders in the frontal cortex is marginally significant (Hedge's g = -0.172, p = 0.052). When analyzing the anterior cingulate cortex and prefrontal cortex separately, reductions were only found in GABA levels in the former (Hedge's g = - 0.191, p = 0.009) across all disorders. ###### Conclusions: Altered glutamatergic and GABAergic metabolites were found across psychiatric disorders, indicating shared dysfunction. We found reduced GABA levels across psychiatric disorders and lower Glu levels in affective disorders. These results highlight the significance of GABA and Glu in psychiatric etiology and partially support rethinking current diagnostic categories.
Open Access
Two distinct networks for encoding goals and forms of action: an effective connectivity study
(National Academy of Sciences, 2024-06-17) Di Cesare, Giuseppe; Lombardi, Giada; Zeidman, Peter; Ürgen, Burcu Ayşen; Sciutti, Alessandra; Friston, Karl J.; Rizzolatti, Giacomo
Goal- directed actions are characterized by two main features: the content (i.e., the action goal) and the form, called vitality forms (VF) (i.e., how actions are executed). It is well another's action are mediated by a network formed by a set of parietal and frontal brain areas. In contrast, the neural bases of action forms (e.g., gentle or rude actions) have not been characterized. However, there are now studies showing that the observation and execution of actions endowed with VF activate, in addition to the parieto- frontal network, the dorso- central insula (DCI). In the present study, we established-using dynamic causal modeling (DCM)-the direction of information flow during observation and execution of actions endowed with gentle and rude VF in the human brain. Based on previous fMRI studies, the selected nodes for the DCM comprised the posterior superior temporal sulcus (pSTS), the inferior parietal lobule (IPL), the premotor cortex (PM), and the DCI. Bayesian model comparison showed that, during action observation, two streams arose from pSTS: one toward IPL, concerning the action goal, and one toward DCI, concerning the action vitality forms. During action execution, two streams arose from PM: one toward IPL, concerning the action goal and one toward DCI concerning action vitality forms. This last finding opens an interesting question concerning the possibility to elicit VF in two distinct ways: cognitively (from PM to DCI) and affectively (from DCI to PM).