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 | Venue 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 EMAIL Olaf Sporns Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA EMAIL Andrew J. Saykin Department of Radiology Indiana University School of Medicine. (46202) Indianapolis, IN, USA EMAIL Franco Pestilli Department of Psychological and Brain Sciences Indiana University (47405) Bloomington, IN, USA EMAIL |
| 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)). |