Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

Histological validation of high-resolution DTI in human post mortem tissue.

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.

Histological validation of DW-MRI tractography in human postmortem tissue.

Despite several previous attempts, histological validation of diffusion-weighted magnetic resonance imaging (DW-MRI)-based tractography as true axonal fiber pathways remains difficult. In the present study, we establish a method to compare histological and tractography data precisely enough for statements on the level of single tractography pathways. To this end, we used carbocyanine dyes to trace connections in human postmortem tissue and aligned them to high-resolution DW-MRI of the same tissue processed within the diffusion tensor imaging (DTI) formalism. We provide robust definitions of sensitivity (true positives) and specificity (true negatives) for DTI tractography and characterize tractography paths in terms of receiver operating characteristics. With sensitivity and specificity rates of approximately 80%, we could show a clear correspondence between histological and inferred tracts. Furthermore, we investigated the effect of fractional anisotropy (FA) thresholds for the tractography and identified FA values between 0.02 and 0.08 as optimal in our study. Last, we validated the course of entire tractography curves to move beyond correctness determination based on pairs of single points on a tract. Thus, histological techniques, in conjunction with alignment and processing tools, may serve as an important validation method of DW-MRI on the level of inferred tractography projections between brain areas.

Sensory modality of smoking cues modulates neural cue reactivity.

RATIONALE: Behavioral experiments have demonstrated that the sensory modality of presentation modulates drug cue reactivity. OBJECTIVES: The present study on nicotine addiction tested whether neural responses to smoking cues are modulated by the sensory modality of stimulus presentation. METHODS: We measured brain activation using functional magnetic resonance imaging (fMRI) in 15 smokers and 15 nonsmokers while they viewed images of smoking paraphernalia and control objects and while they touched the same objects without seeing them. RESULTS: Haptically presented, smoking-related stimuli induced more pronounced neural cue reactivity than visual cues in the left dorsal striatum in smokers compared to nonsmokers. The severity of nicotine dependence correlated positively with the preference for haptically explored smoking cues in the left inferior parietal lobule/somatosensory cortex, right fusiform gyrus/inferior temporal cortex/cerebellum, hippocampus/parahippocampal gyrus, posterior cingulate cortex, and supplementary motor area. CONCLUSIONS: These observations are in line with the hypothesized role of the dorsal striatum for the expression of drug habits and the well-established concept of drug-related automatized schemata, since haptic perception is more closely linked to the corresponding object-specific action pattern than visual perception. Moreover, our findings demonstrate that with the growing severity of nicotine dependence, brain regions involved in object perception, memory, self-processing, and motor control exhibit an increasing preference for haptic over visual smoking cues. This difference was not found for control stimuli. Considering the sensory modality of the presented cues could serve to develop more reliable fMRI-specific biomarkers, more ecologically valid experimental designs, and more effective cue-exposure therapies of addiction.

Smoking experience modulates the cortical integration of vision and haptics.

Human neuroplasticity of multisensory integration has been studied mainly in the context of natural or artificial training situations in healthy subjects. However, regular smokers also offer the opportunity to assess the impact of intensive daily multisensory interactions with smoking-related objects on the neural correlates of crossmodal object processing. The present functional magnetic resonance imaging study revealed that smokers show a comparable visuo-haptic integration pattern for both smoking paraphernalia and control objects in the left lateral occipital complex, a region playing a crucial role in crossmodal object recognition. Moreover, the degree of nicotine dependence correlated positively with the magnitude of visuo-haptic integration in the left lateral occipital complex (LOC) for smoking-associated but not for control objects. In contrast, in the left LOC non-smokers displayed a visuo-haptic integration pattern for control objects, but not for smoking paraphernalia. This suggests that prolonged smoking-related multisensory experiences in smokers facilitate the merging of visual and haptic inputs in the lateral occipital complex for the respective stimuli. Studying clinical populations who engage in compulsive activities may represent an ecologically valid approach to investigating the neuroplasticity of multisensory integration.