Podeu consultar les dades primàries associades a l'article a: http://hdl.handle.net/2445/136617, The assessment of task-independent functional connectivity (FC) after a lesion causing hemianopia remains an uncovered topic and represents a crucial point to better understand the neural basis of blindsight (i.e. unconscious visually triggered behavior) and visual awareness. In this light, we evaluated functional connectivity (FC) in 10 hemianopic patients and 10 healthy controls in a resting state paradigm. The main aim of this study is twofold: first of all we focused on the description and assessment of density and intensity of functional connectivity and network topology with and without a lesion affecting the visual pathway, and then we extracted and statistically compared network metrics, focusing on functional segregation, integration and specialization. Moreover, a study of 3-cycle triangles with prominent connectivity was conducted to analyze functional segregation calculated as the area of each triangle created connecting three neighboring nodes. To achieve these purposes we applied a graph theory-based approach, starting from Pearson correlation coefficients extracted from pairs of regions of interest. In these analyses we focused on the FC extracted by the whole brain as well as by four resting state networks: The Visual (VN), Salience (SN), Attention (AN) and Default Mode Network (DMN), to assess brain functional reorganization following the injury. The results showed a general decrease in density and intensity of functional connections, that leads to a less compact structure characterized by decrease in functional integration, segregation and in the number of interconnected hubs in both the Visual Network and the whole brain, despite an increase in long-range inter-modules connections (occipito-frontal connections). Indeed, the VN was the most affected network, characterized by a decrease in intra- and inter-network connections and by a less compact topology, with less interconnected nodes. Surprisingly, we observed a higher functional integration in the DMN and in the AN regardless of the lesion extent, that may indicate a functional reorganization of the brain following the injury, trying to compensate for the general reduced connectivity. Finally we observed an increase in functional specialization (lower between-network connectivity) and in inter-networks functional segregation, which is reflected in a less compact network topology, highly organized in functional clusters. These descriptive findings provide new insight on the spontaneous brain activity in hemianopic patients by showing an alteration in the intrinsic architecture of a large-scale brain system that goes beyond the impairment of a single RSN.

There are twenty zip files each one corresponding to a participant. The acronym PT stands for patients (from 01 to 10) and HC stands for healthy controls (from 01 to 10). Once unzipped, structural and functional data of each patient and healthy control are located in the folder indicated by the specific acronym and number of the participant (i.e. PT01). Thus, each folder contains the original functional data of the resting state sequence (fmri.nii.gz), the preprocessed functional data (fmri_res.nii.gz), the original structural T1-weighted image (T1.nii.gz) and the structural T1-weighted image after applying the brain extraction with the Brain Extraction Tool of fsl (T1_brain.nii.gz) and used to perform the co-registration with the functional data. All images are in NIfTI (Neuroimaging Computer Technology Initiative) format, as a gzip compressed file, normally used by different software as the software library FMRIB (FSL). We also provide the NIfTI file of the AAL Atlas used to parcellate the brain during the analysis (AAL_Contract_90_3MM.nii) and the corresponding text file indicating the list of regions composing the atlas (AAL_LabelID_90.txt)., Podeu consultar el document publicat a: http://hdl.handle.net/2445/161359, Dades primàries associades a un article publicat a la revista PLoS One, 2020, vol. 15, num. 1, p. e0226816 [https://doi.org/10.1371/journal.pone.0226816], We tested 10 hemianopic patients and 10 healthy participants. They were scanned in a 1.5 Tesla Philips scanner at the Borgo Roma Hospital in Verona (Italy). We collected a whole brain high-resolution (1x1x1 mm3) 3D T1-weighted image with magnetization-prepared rapid acquisition gradient echo (MPRAGE) and one hundred sixty resting state volumes with TR=2500ms, Echo Time=50ms; 30 slices with slice thickness of 4mm, Field of View = 224x224mm, time duration = 6.47m and echo train length = 39. The entire sample was were scanned during the period of time going from February 2017 until July 2018.

Podeu consultar les dades primàries associades a l'article a: http://hdl.handle.net/2445/136617, The assessment of task-independent functional connectivity (FC) after a lesion causing hemianopia remains an uncovered topic and represents a crucial point to better understand the neural basis of blindsight (i.e. unconscious visually triggered behavior) and visual awareness. In this light, we evaluated functional connectivity (FC) in 10 hemianopic patients and 10 healthy controls in a resting state paradigm. The main aim of this study is twofold: first of all we focused on the description and assessment of density and intensity of functional connectivity and network topology with and without a lesion affecting the visual pathway, and then we extracted and statistically compared network metrics, focusing on functional segregation, integration and specialization. Moreover, a study of 3-cycle triangles with prominent connectivity was conducted to analyze functional segregation calculated as the area of each triangle created connecting three neighboring nodes. To achieve these purposes we applied a graph theory-based approach, starting from Pearson correlation coefficients extracted from pairs of regions of interest. In these analyses we focused on the FC extracted by the whole brain as well as by four resting state networks: The Visual (VN), Salience (SN), Attention (AN) and Default Mode Network (DMN), to assess brain functional reorganization following the injury. The results showed a general decrease in density and intensity of functional connections, that leads to a less compact structure characterized by decrease in functional integration, segregation and in the number of interconnected hubs in both the Visual Network and the whole brain, despite an increase in long-range inter-modules connections (occipito-frontal connections). Indeed, the VN was the most affected network, characterized by a decrease in intra- and inter-network connections and by a less compact topology, with less interconnected nodes. Surprisingly, we observed a higher functional integration in the DMN and in the AN regardless of the lesion extent, that may indicate a functional reorganization of the brain following the injury, trying to compensate for the general reduced connectivity. Finally we observed an increase in functional specialization (lower between-network connectivity) and in inter-networks functional segregation, which is reflected in a less compact network topology, highly organized in functional clusters. These descriptive findings provide new insight on the spontaneous brain activity in hemianopic patients by showing an alteration in the intrinsic architecture of a large-scale brain system that goes beyond the impairment of a single RSN.

There are twenty zip files each one corresponding to a participant. The acronym PT stands for patients (from 01 to 10) and HC stands for healthy controls (from 01 to 10). Once unzipped, structural and functional data of each patient and healthy control are located in the folder indicated by the specific acronym and number of the participant (i.e. PT01). Thus, each folder contains the original functional data of the resting state sequence (fmri.nii.gz), the preprocessed functional data (fmri_res.nii.gz), the original structural T1-weighted image (T1.nii.gz) and the structural T1-weighted image after applying the brain extraction with the Brain Extraction Tool of fsl (T1_brain.nii.gz) and used to perform the co-registration with the functional data. All images are in NIfTI (Neuroimaging Computer Technology Initiative) format, as a gzip compressed file, normally used by different software as the software library FMRIB (FSL). We also provide the NIfTI file of the AAL Atlas used to parcellate the brain during the analysis (AAL_Contract_90_3MM.nii) and the corresponding text file indicating the list of regions composing the atlas (AAL_LabelID_90.txt)., Podeu consultar el document publicat a: http://hdl.handle.net/2445/161359, Dades primàries associades a un article publicat a la revista PLoS One, 2020, vol. 15, num. 1, p. e0226816 [https://doi.org/10.1371/journal.pone.0226816], We tested 10 hemianopic patients and 10 healthy participants. They were scanned in a 1.5 Tesla Philips scanner at the Borgo Roma Hospital in Verona (Italy). We collected a whole brain high-resolution (1x1x1 mm3) 3D T1-weighted image with magnetization-prepared rapid acquisition gradient echo (MPRAGE) and one hundred sixty resting state volumes with TR=2500ms, Echo Time=50ms; 30 slices with slice thickness of 4mm, Field of View = 224x224mm, time duration = 6.47m and echo train length = 39. The entire sample was were scanned during the period of time going from February 2017 until July 2018.