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BRAIN

CONNECTIVITY

GALLERY OF PROJECT OUTPUTS

This gallery includes selected results, screenshots, movies and developed apps (more to come)

DKI Compartment Modelling Diffusion Kurtosis Imaging: implementation of the multi-compartment model for water diffusion based on (Jensen and Helpern, 2010) and considering and considering scalar diffusion coefficients (DC), i.e assuming isotropic diffusion, which is acceptable for short diffusion periods. Developed by David Naves Sousa, Rafael Neto Henriques, and Hugo Alexandre Ferreira	DKI Monte-Carlo Simulation Diffusion Kurtosis Imaging: Monte-Carlo simulation of water diffusion (may run on CrowdProcess) Developed by David Naves Sousa and Hugo Alexandre Ferreira	DKI Tractography Diffusion Kurtosis Imaging -based Tractography based on the work by Rafael Neto-Henriques et al. As far as we know, we were the second group in the world to have developed DKI-based tractography!
DKI Tractography for Surgery Clinical translation: DKI tractography improves on the delineation of the Cortical Spinal Tract in patients with space-occupying lesions. As far as we know, we are the first in the world to look at this! Study by João Leote et al.	United DKI United DKI: software toolbox in MATLAB for processing DKI data Developed by Rafael Neto-Henriques et al. and to be released soon in link below. (Tract reconstruction by Ricardo Loução)	fMRI Functional Connectivity Clinical translation: task-based fMRI with left and right hand-grasping (red and blue, respectively) and language paradigms. Resting-state fMRI data processing enables the correct depiction of the Sensorimotor network. Resting-state fMRI could be useful especially for non-cooperative patients. Study by Hugo Alexandre Ferreira, Joana Ramalho and Luís Cerqueira
Automatic RSN Identification This MATLAB protocol enables the automatic identification of Resting-State Networks (RSNs) from a dataset of multiple components resulting from Independent Component Analysis (ICA) of fMRI data. Developed by Francisco Fernandes and Hugo Alexandre Ferreira	Epileptogenic focus detection This method was developed in MATLAB and combines Temporal Clustering Analysis, Detrended Fluctuation Analysis and Multi-Scale Entropy Analysis for usage in the detection of the epileptogenic foci using fMRI data. This method is also suitable for analysing the dynamic behaviour of task- and resting-state networks. Developed by Vânia Tavares et al.	Phase locking fMRI A method was developed in MATLAB for the characterization of the dynamics of functional connectivity based on the phase locking of fMRI signals from distinct brain regions. This method enables the study of brain cronometry. Developed by Joana Brito et al.
Dynamic Connectivity Connectivity matrices over time representing the dynamics of resting-state. By Ricardo Maximiano et al.	MIBCA toolbox The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox: an all-in-one toolbox for processing MRI and PET data and analysis of multimodal brain-connectivity using graph-theory. Distinguished with Magna Cum Laude poster presentation at the ISMRM2014 (see link below for poster) and with the Cover page of PeerJ of the 14 July 2014. Developed by André Santos Ribeiro, Luís Miguel Lacerda et al. Please find links for the poster, honors and paper in the next images.	MIBCA: Brain Connectivity Matrix One of the outputs of MIBCA is the brain connectivity matrix, which can be computed and combined from anatomical, structural, functional and metabolic connectivity matrices (shown above).
MIBCA: Brain Graphs Brain Graphs are also obtained from MIBCA. The brain graph on the left represents what is common between structural (DTI) and funcional (fMRI) connectivity matrices, whilst the brain graph on the right represents what is different. Check in the link below the poster 3003!	MIBCA: Connectogram MIBCA also produce connectograms, which are quite comprehensive visual representations. Here for example the outer ring represent right (lighter grey) and left (darker grey) cortical regions. The inner rings can represent both imaging and graph-theory derived connectivity metrics such as cortical thickness, mean diffusivity, standard uptake values, and structural connectivity node degree and clustering coefficient, for example. Please find the link to the paper below.	MIBCA: Alzheimer's Disease MIBCA: This connectogram represents the whole-brain statistical analysis (Mann-Whitney U-test) between cognitive normal and Alzheimer's Disease patients. This was our first approach to testing MIBCA in patient data (ADNI). Study by André Santos Ribeiro et al. You can find the paper in the link below.
MIBCA: Brain Tumors Here we studied the brain connectivity of brain tumor patient and we found out that changes to brain connectivity are not only local but can also be felt in regions distant to the tumor. As far as we know, we are the first in the world to look at this! Study by Ana Carina Mendes et al.	MIBCA: DKI-based Connectivity Here we structural connectivity using DKI-based tractography. We found out that we are able to observe larger number of interhemispherical connections using DKI vs using DTI, as was expected given the superior capability of DKI in resolving crossing-fibers. As far as we know, we were also the first to look at this! Study by Ricardo Loução et al.	Brain Connectivity Leap 1.0 Here is the version 1.0 of Brain Connectivity Leap. This is an application developed in Unity3D which enables the user to navigate inside the brain and the brain connectome using Gesture Recognition (Leap Motion) and Virtual Reality goggles (Oculus Rift). This app has been feature in Leap Motion's Blog! Developed by Filipe Sobreira Rodrigues, Ricardo Ribeiro and Hugo Alexandre Ferreira. By now you have already noticed that the welcoming video is about this great app!
Brain Connectivity Leap 2.0 This is the version 2.0 of the Brain Connectivity Leap with a number of improved features. Developed by Filipe Sobreira Rodrigues, Ricardo Ribeiro and Hugo Alexandre Ferreira. Check the video and the news section!	Tractography in BCL Tractrography visualization is now integrated into the Brain Connectivity Leap application. Developed by Filipe Sobreira Rodrigues	Brain AR/VR This is an Augmented Reality and Virtual Reality application developed with Metaio in Android environment. It enables the user to visualised a reconstructed brain, including regions-of-interest superimposed over video. Developed by José Soeiro et al. Ckeck the video below!

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