FastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields

in NeuroImage (2020)

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes ... [more ▼]

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method–FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable e-field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2s, ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications [less ▲]

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

in NeuroImage (2018)

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment ... [more ▼]

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field. [less ▲]

Understanding the functional neuroanatomy of acquired prosopagnosia

in NeuroImage (2007), 35(2), 836-852

One of the most remarkable disorders following brain damage is prosopagnosia, the inability to recognize faces. While a number of cases of prosopagnosia have been described at the behavioral level, the ... [more ▼]

One of the most remarkable disorders following brain damage is prosopagnosia, the inability to recognize faces. While a number of cases of prosopagnosia have been described at the behavioral level, the functional neuroanatomy of this face recognition impairment, and thus the brain regions critically involved in normal face recognition, has never been specified in great detail. Here, we used anatomical and functional magnetic resonance imaging (fMRI) to present the detailed functional neuroanatomy of a single case of acquired prosopagnosia (PS; Rossion, B., Caldara, R., Seghier, M., Schuller, A.-M., Lazeyras, F., Mayer, E., 2003a. A network of occipito-temporal face-sensitive areas besides the right middle fusiform gyrus is necessary for normal face processing. Brain 126, 2381-95; Rossion, B., Joyce, C.A., Cottrell, G.W., Tarr, M.J., 2003b. Early lateralization and orientation tuning for face, word, and object processing in the visual cortex. Neuroimage 20, 1609-24) with normal object recognition. First, we clarify the exact anatomical location and extent of PS' lesions in relation to (a) retinotopic cortex, (b) face-preferring regions, and (c) other classical visual regions. PS' main lesion - most likely causing her prosopagnosia - is localized in the posterior part of the right ventral occipitotemporal cortex. This lesion causes a left superior paracentral scotoma, as frequently observed in cases of prosopagnosia. While the borders of the early visual areas in the left hemisphere could be delineated well, the extensive posterior right-sided lesion hampered a full specification of the cortical representation of the left visual field. Using multiple scanning runs, face-preferring activation was detected within the right middle fusiform gyrus (MFG) in the so-called 'fusiform face area' ('FFA'), but also in the left inferior occipital gyrus (left 'OFA'), and in the right posterior superior temporal sulcus (STS). The dorsal part of the lateral occipital complex (LOC) and the human middle temporal cortex (hMT+/V5) were localized bilaterally. The color-preferring region V4/V8 was localized only in the left hemisphere. In the right hemisphere, the posterior lesion spared the ventral part of LOC, a region that may be critical for the preserved object recognition abilities of the patient, and the restriction of her deficit to the category of faces. The presumptive functions of both structurally damaged and preserved regions are discussed and new hypotheses regarding the impaired and preserved abilities of the patient during face and non-face object processing are derived. Fine-grained neurofunctional analyses of brain-damaged single cases with isolated recognition deficits may considerably improve our knowledge of the brain regions critically involved in specific visual functions, such as face recognition. [less ▲]

The functionally defined right occipital and fusiform "face areas" discriminate novel from visually familiar faces

in NeuroImage (2003), 19(3), 877-883

Neuroimaging (PET and fMRI) studies have identified a set of brain areas responding more to faces than to other object categories in the visual extrastriate cortex of humans. This network includes the ... [more ▼]

Neuroimaging (PET and fMRI) studies have identified a set of brain areas responding more to faces than to other object categories in the visual extrastriate cortex of humans. This network includes the middle lateral fusiform gyrus (the fusiform face area, or FFA) as well as the inferior occipital gyrus (occipital face area, OFA). The exact functions of these areas in face processing remain unclear although it has been argued that their primary function is to distinguish faces from nonface object categories-"face detection"-or also to discriminate among faces, irrespective of their visual familiarity to the observer. Here, we combined the data from two previous positron emission tomography (PET) studies to show that the functionally defined face areas are involved in the automatic discrimination between unfamiliar faces and familiar faces. Consistent with previous studies, a face localizer contrast (faces-objects) revealed bilateral activation in the middle lateral fusiform gyrus (FFA, BA37) and in the right inferior occipital cortex (OFA, BA19). Within all the regions of the right hemisphere, larger levels of activation were found for unfamiliar as compared to familiar faces. These results suggest that the very same areas involved in categorizing faces at the basic or individual level, play a role in differentiating familiar faces from new faces, showing an overlap between visual and presemantic mnesic representations of faces in the right hemisphere. [less ▲]

Neuronal mechanisms of perceptual learning: Changes in human brain activity with training in orientation discrimination

in NeuroImage (1999), 9(1), 46-62

Using 15O-water 3D positron emission tomography, regional cerebral blood flow was measured twice in six human subjects: before and after extensive training in orientation discrimination. In each session ... [more ▼]

Using 15O-water 3D positron emission tomography, regional cerebral blood flow was measured twice in six human subjects: before and after extensive training in orientation discrimination. In each session subjects performed two orientation discrimination tasks, during which they discriminated the orientation of a grating at either the trained or untrained reference orientation, and a control task, during which they detected a randomly textured pattern. By comparing the discrimination to the detection tasks, we observed a main effect of task bilaterally in the posterior occipital cortex, extending into the left posterior fusiform gyrus and the right inferior occipital gyrus, bilaterally in the intraparietal sulcus, as well as in the cerebellum, thalamus, and brainstem. When we compared the activation pattern before and after the training period, all the changes observed were activity decreases. The nonspecific changes, which were not related to the orientation used during the training, were situated in the cerebellum and bilaterally in the extrastriate visual cortex. The orientation-specific changes, on the other hand, were restricted to the striate and extrastriate visual cortex, more precisely the right calcarine sulcus, the left lingual gyrus, the left middle occipital, and the right inferior occipital gyrus. These findings confirm our hypothesis concerning the existence of learning related changes at early levels of visual processing in human adults and suggest that mechanisms resulting in neuronal activity decreases might be involved in the present kind of learning. [less ▲]