Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Authors: Cesar F. Caiafa, Olaf Sporns, Andrew Saykin, Franco Pestilli
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | in section 5 we present experimental results using high resolution in vivo datasets; Here, we validate our theoretical findings by using d MRI data from subjects in a public source (the Stanford dataset [32]). |
| Researcher Affiliation | Academia | Cesar F. Caiafa Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA IAR CCT La Plata, CONICET / CIC-PBA (1894) V. Elisa, ARGENTINA ccaiafa@gmail.com Olaf Sporns Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA osporns@indiana.edu Andrew J. Saykin Department of Radiology Indiana University School of Medicine. (46202) Indianapolis, IN, USA asaykin@iupui.edu Franco Pestilli Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA franpest@indiana.edu |
| Pseudocode | Yes | Algorithm 1 : y = M_times_w(Φ,D,w); Algorithm 2 : w = Mtransp_times_y(Φ,D,y) |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology described, nor does it state that the code is available in supplementary materials or upon request. |
| Open Datasets | Yes | Here, we validate our theoretical findings by using d MRI data from subjects in a public source (the Stanford dataset [32]). The Human Connectome Project dataset [45] |
| Dataset Splits | No | The paper mentions using 'd MRI data from subjects in a public source (the Stanford dataset [32])' and 'Human Connectome Project dataset [45]' but does not provide specific details on how these datasets were split into training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like MATLAB [19] and a NNLS algorithm [22], but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | We performed tractography using these data and both, probabilistic and deterministic methods, in combination with Constrained Spherical Deconvolution (CSD) and the diffusion tensor model (DTI) [41, 17, 5]. We generated candidate connectomes with Nf = 500, 000 fascicles per brain brain. ... Nθ = 96 (STN96, five subjects) and Nθ = 150 (STN150, one subject) directions with b-value b = 2, 000s/mm2. ... we fixed L = 360 and computed the model size for both, Li FE and Li FESD, as a function of the number of gradient directions Nθ (Fig. 4(c)) and fascicles Nf (Fig. 4(d)). |