Individual Region Of Interest Research Articles

Natural scene reconstruction from fMRI signals using generative latent diffusion

In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called “Brain-Diffuser”. In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling “ROI-optimal” scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain–computer interface) and fundamental neuroscience.

Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications

The critical components for applying the correct amount of agrochemicals are fruit tree characteristics such as canopy height, canopy volume, and canopy coverage. An unmanned aerial vehicle (UAV)-based tree canopy characteristics measurement system was developed using image processing approaches. The UAV captured images using a high-resolution red-green-blue (RGB) camera. A digital surface model (DSM) and a digital terrain model (DTM) were generated from the captured images. A tree canopy height map was generated from the subtraction of DSM and DTM. A total of 24 apple trees were randomly targeted to measure the canopy characteristics. Region of interest (ROI) was generated across the boundary of each targeted tree. The height of all pixels within each ROI was computed separately. The pixel with maximum height was considered as the height of the respective tree. For computing canopy volume, the sum of all pixel heights from individual ROI was multiplied by the square of ground sample distance (GSD) of 5.69 mm·pixel−1. A segmentation method was employed to calculate the canopy coverage of the individual trees. The segmented canopy pixel area was divided by the total pixel area within the ROI. The results showed an average relative error of 0.2 m(6.64%) while comparing automatically measured tree height with ground measurements. For tree canopy volume, a mean absolute error of 0.25 m3 and a root mean square error of 0.33 m3 were achieved. The study estimated the possible agrochemical requirement for spraying the fruit trees, ranging from 0.1 to 0.32 l based on tree canopy volumes. The overall investigations suggest that the UAV-based tree canopy characteristics measurements could be a potential tool to calculate the pesticide requirement for precision spraying applications in tree fruit orchards.

Perturbed astrocyte Ca2+ signaling in a diet‐based mouse model of small cerebral vessel disease

AbstractBackgroundThe hyperhomocysteinemia (HHcy) diet model of small cerebral vessel disease exhibits vascular inflammation, cerebral hypoperfusion, impaired neurovascular coupling, synapse dysfunction, and cognitive decline. Pathological changes in astrocyte structural features have also been noted, but functional astrocyte phenotypes have not been characterized. Here, we assessed Ca2+ signaling in individual astrocytes and astrocyte networks in barrel cortex of mice, pretreated with control or HHcy diet. Investigations were performed in fully awake mice to avoid confounding interactions between anesthetics, neural circuit activity, and vasoactive factors.MethodAdult C57/BL6 mice were fed for 15 weeks with control chow, or chow deficient in vitamins B6/B12 and enriched in methionine (HHcy diet). At week 11‐12, mice were injected with AAV‐Gfa104‐lck‐GCaMP6f into barrel cortex, followed by craniotomies and installation of a glass cranial windows with headmounts. Three weeks later, fully awake mice were head‐mounted under a two photon microscope to assess GCaMP6/Ca2+ transient activity in barrel cortex astrocytes during a 10 minute baseline (spontaneous activity) and also before, during, and air puff stimulation of contralateral whiskers (10Hz, 10 sec) . Mean Ca2+ transient amplitudes, transient rise/decay times, and number of transients per region of interest (ROI) were measured and averaged across the field of view (FOV). Functional relationships between multiple ROIs within the FOV were assessed to evaluate astrocyte network activityResultSpontaneous Ca2+ transients were comparable in amplitude and rise/decay kinetics across diet conditions. However, the number of transients per ROI was reduced in HHcy diet mice. Whisker stimulation evoked a primary Ca2+ elevation per ROI followed 10‐15 seconds later by a smaller amplitude Ca2+‐“aftershock”. Evoked Ca2+ signals were highly coordinated across ROIs within the FOV. However, the number of active ROIs and the functional connectivity between ROIs were significantly reduced in HHcy vs. control diet mice. Despite these network deficits, evoked Ca2+ transient amplitudes and aftershocks in individual ROIs were significantly larger with faster activation/decay kinetics in HHcy mice.ConclusionThe results suggest that altered astrocyte Ca2+ signaling, as a result of vascular pathology, may play an important role in the dynamic interplay between cerebral blood flow and neuronal activity, leading to overall neural dysfunction and cognitive impairment.

Hyperspectral Imaging during Normothermic Machine Perfusion-A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks.

Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550–995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.

Combining region- and network-level brain-behavior relationships in a structural equation model

Brain-behavior associations in fMRI studies are typically restricted to a single level of analysis: either a circumscribed brain region-of-interest (ROI) or a larger network of brain regions. However, this common practice may not always account for the interdependencies among ROIs of the same network or potentially unique information at the ROI-level, respectively. To account for both sources of information, we combined measurement and structural components of structural equation modeling (SEM) approaches to empirically derive networks from ROI activity, and to assess the association of both individual ROIs and their respective whole-brain activation networks with task performance using three large task-fMRI datasets and two separate brain parcellation schemes. The results for working memory and relational tasks revealed that well-known ROI-performance associations are either non-significant or reversed when accounting for the ROI's common association with its corresponding network, and that the network as a whole is instead robustly associated with task performance. The results for the arithmetic task revealed that in certain cases, an ROI can be robustly associated with task performance, even when accounting for its associated network. The SEM framework described in this study provides researchers additional flexibility in testing brain-behavior relationships, as well as a principled way to combine ROI- and network-levels of analysis.

Panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics

Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics.

Age-dependent atypicalities in body- and face-sensitive activation of the EBA and FFA in individuals with ASD

Individuals with autism spectrum disorder (ASD) have difficuly in recognizing bodies and faces, which are more pronounced in children than adults. If such difficulties originate from dysfunction of the extrastriate body area (EBA) and the fusiform face area (FFA), activation in these regions might be more atypical in children than in adults. We preformed functional magnetic resonance imaging while children and adults with ASD and age-matched typically developed (TD) individuals observed face, body, car, and scene. To examine various aspects, we performed individual region of interest (ROI) analysis, as well as conventional random effect group analysis. At individual ROI analysis, we examined the ratio of participants showing a category-sensitive response, the size of regions, location and activation patterns among the four object categories. Adults with ASD showed no atypicalities in activation of the EBA and FFA, whereas children with ASD showed atypical activation in these regions. Specifically, a smaller percentage of children with ASD showed face-sensitive activation of the FFA than TD children. Moreover, the size of the EBA was smaller in children with ASD than in TD children. Our results revealed atypicalities in both the FFA and EBA in children with ASD but not in adults with ASD.

Decentralized content sharing among tourists in visiting hotspots

Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.

Real-time fMRI neurofeedback: application in patients with substance use disorder

One of the most prominent symptoms in substance use disorders is a strong desire to consume a particular substance or to show a certain behaviour (craving). The aim of the present project was to modify craving-related neuronal responses in patients with alcohol use disorder (AUD) using neurofeedback (real-time fMRI). Patients with AUD and healthy subjects participated in the study; neurofeedback was an add-on to other therapeutic interventions. Neutral and alcohol-associated pictures were presented during functional MRI measurements. Alcohol-associated BOLD-responses in the frontal cortex were used as a region of interest (ROI). During the neurofeedback sessions, participants were instructed to reduce their neuronal activity in the specific ROI during the presentation of alcohol-associated pictures. The results indicated decreased brain responses after real-time fMRI sessions in alcohol-associated brain areas, especially the anterior cingulate cortex, the insula, inferior temporal gyrus, medial frontal gyrus and cuneus. In addition, the neuronal activity was reduced in the individual ROIs after the neurofeedback training in patients. In future, real-time fMRI may offer a therapeutic strategy to be used in addition to other psychotherapeutic strategies for patients with psychiatric and psychosomatic disorders.

Real-time fMRI neurofeedback: application in patients with substance use disorder

One of the most prominent symptoms in substance use disorders is a strong desire to consume a particular substance or to show a certain behaviour (craving). The aim of the present project was to modify craving-related neuronal responses in patients with alcohol use disorder (AUD) using neurofeedback (real-time fMRI). Patients with AUD and healthy subjects participated in the study; neurofeedback was an add-on to other therapeutic interventions. Neutral and alcohol-associated pictures were presented during functional MRI measurements. Alcohol-associated BOLD-responses in the frontal cortex were used as a region of interest (ROI). During the neurofeedback sessions, participants were instructed to reduce their neuronal activity in the specific ROI during the presentation of alcohol-associated pictures. The results indicated decreased brain responses after real-time fMRI sessions in alcohol-associated brain areas, especially the anterior cingulate cortex, the insula, inferior temporal gyrus, medial frontal gyrus and cuneus. In addition, the neuronal activity was reduced in the individual ROIs after the neurofeedback training in patients. In future, real-time fMRI may offer a therapeutic strategy to be used in addition to other psychotherapeutic strategies for patients with psychiatric and psychosomatic disorders.

The repeatability of T2 relaxation time measurement of human knee articular cartilage.

To assess short- and long-term repeatability of T2 relaxation time measurements of the knee articular cartilage. The right knees of nine asymptomatic volunteers (age 30-38 years, five male, four female) were imaged at 1.5 T in three sessions 1 and 2 weeks apart. To observe short-term repeatability, the measurements were repeated three times within one of the three imaging sessions for each volunteer. T2 relaxation time was mapped using a multi-slice multi-echo spin echo sequence in axial and sagittal planes. Cartilage was manually segmented and repeatability, as measured by root-mean-square coefficient of variation (CVRMS) was evaluated both for the entire bulk cartilage of each joint surface in the slice and separately for each region of interest (ROI) at different topographical locations and separately for the superficial and deep half of each ROI. For bulk T2, the long-term repeatability was 3.2, 5.4, and 3.7%, and the short-term reproducibility was 3.9, 3.9, and 3.4% for bulk femoral, tibial, and patellar cartilage, respectively. There were no significant differences between long-term and short-term repeatability in superficial or deep cartilage when comparing CVRMS values (p = 0.338 and 0.700, respectively). For individual ROIs, the repeatability varied between 2.5 and 22.2% depending on the topographical location. The current results show mostly good repeatability. However, there were remarkable variations of T2 between bulk cartilage and different ROIs, bulk cartilage showing better repeatability. With careful patient positioning T2 can be accurately determined for different cartilage surfaces.

Two methods to generate templates for template-based partial volume effect correction: SPECT phantom experiments

In this paper, we explore the applicability of template-based compensation for the partial volume effect (PVE) for situations where (i) the image has multiple uptake sites (tumors and organs) but only one of them is treated as a region of interest (ROI) with the boundaries available from a high-resolution modality and (ii) no information regarding activities inside or outside this ROI is a priori available. We modeled this situation by performing SPECT acquisitions of phantoms containing 21 containers, which had different shapes and sizes and were filled with different levels of activity. In our analysis, each of these containers was treated as an individual ROI. We compared the performance of two methods of template construction. In method 1, the ROI template value was obtained from a conventionally reconstructed (without PVEC) image. In method 2, the ROI template value was directly (bypassing the PVE-affected conventional image) calculated from projections using region-based reconstruction. Our processing shows that method 1 resulted in consistent (activities for all 21 ROIs were improved) but relatively weak PVE compensation (errors of recovered total activities were equal to or lower than 10% for 5 ROIs only). Application of method 2 resulted in a selective (activities for 19 ROIs were improved) but considerably better compensation when compared to method 1 (errors of recovered total activities were equal to or lower than 10% for 10 ROIs).

How much are clinical fMRI reports influenced by standard postprocessing methods? An investigation of normalization and region of interest effects in the medial temporal lobe

Recent evidence has indicated that standard postprocessing methods such as template-based region of interest (ROI) definition and normalization of individual brains to a standard template may influence final outcome of functional magnetic resonance imaging investigations. Here, we provide the first comprehensive investigation into whether ROI definition and normalization may also change the clinical interpretation of patient data. A series of medial temporal lobe epilepsy patients were investigated with a clinical memory paradigm and individually delineated as well as template-based ROIs. Different metrics for activation quantification were applied. Results show that the application of template-based ROIs can significantly change the clinical interpretation of individual patient data. This relates to sensitivity for brain activation and hemispheric dominance. We conclude that individual ROIs should be defined on nontransformed functional data and that use of more than one metric for activation quantification is beneficial.

Evaluation of atlas-based segmentation of hippocampi in healthy humans

Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus. High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas. Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.

In vivo voxel-based relaxometry in amyotrophic lateral sclerosis

We applied voxel-based relaxometry (VBR) in 12 ALS patients and 12 matched healthy controls and compared the results with those of voxel-based morphometry (VBM). VBR revealed a reduced relaxation rate in the right precentral gyrus and ventral pons. Individual region of interest (ROI)-based analysis of R2 maps confirmed our VBR results. VBM depicted a reduction of white matter in the paracentral lobules and in the right middle cerebellar peduncle. Our data suggests that VBR and ROI analysis of R2 maps may have a higher sensitivity in detecting local tissue atrophy in the motor cortex and along the corticospinal tract than VBM. Larger prospective studies are necessary to evaluate the usefulness and relevance of VBR and ROI-based analysis of R2 maps in clinical practice.

SU‐GG‐I‐56: Internal Dosimetry of Iodine 131 in Patients with Thyroid Carcinoma in the Instituto Nacional De Cancerologia, Bogota‐Colombia

Purpose: with the purpose of determining the iodine 131 biodistribution in patients with thyroid cancer, a patient‐specific dosimetry protocol was developed and applied, using the administration of a tracer amount of this radionuclide and the methodology of the Medical Internal Radiation Dose. The dosimetry method consists in a determination of the maximum tolerated activity that will deliver 2 Gy to the blood, and the corresponding ablative lesion dose. Method and Materials: Fifteen patients with thyroid‐stimulating hormone (TSH) level of 30 mU/L and a suppressed iodine diet were orally administered with a tracer activity (111 – 148 MBq). The activity determination in whole body, lungs, stomach, thyroid and metastatic targets were done using a region‐of‐interest (ROI) technique. Individual ROIs were drawn on both anterior and posterior projections at 2, 4, 24, 48 and 72 hours. Calculations were based on the geometric mean of anterior and posterior counts, including corrections for photon scatter, attenuation, septal penetration, and other effects in the gamma camera. Results: the data were fitted to multicomponent exponential retention functions or in closed compartment models using the Organ Level Internal Dose Assessment (a software program OLINDA, Vanderbilt University), and, subsequently, the calculation of radiation absorbed doses to the different organs and tissues. Conclusion: The internal dosimetry method implemented has the advantage of being ambulatory and the tumor doses was prescribed for those cases in which tumor uptake give ablative lesion dose of 300 Gy. The internal dosimetry is useful in determine the optimal amount of administered activity in radioiodine therapy, so that the absorbed doses to the organs of interest turn out to be the optimal, without overcoming the maximum tolerated dose in red marrow and the maximum tolerated dose to the lungs.

Region of interest template for the human basal ganglia: Comparing EPI and standardized space approaches

Identifying task-related activation in the basal ganglia (BG) is an important area of interest in normal motor systems and cognitive neuroscience. The purpose of this study was to compare changes in brain activation in the BG using results obtained from two different masking methods: a mask drawn in standardized space from a T1-weighted anatomical image and individual region of interest (ROI) masks drawn from each subject’s echo-planar image (EPI) from different tasks with reference to the high resolution fast spin echo image of each subject. Two standardized masks were used: a mask developed in Talairach space (Basal Ganglia Human Area Template (BGHAT)) and a mask developed in Montreal Neurological Institute space (MNI mask). Ten subjects produced fingertip force pulses in five separate contraction tasks during fMRI scanning. ROIs were the left caudate, putamen, external and internal portions of the globus pallidus, and subthalamic nucleus. ANOVA revealed a similar average number of voxels in the EPI mask across tasks in each BG region. The percent signal change (PSC) was consistent within each region regardless of which mask was used. Linear regression analyses between PSC in BGHAT and EPI masks and MNI and EPI masks yielded r2 values between 0.74–0.99 and 0.70–0.99 across regions, respectively. In conclusion, PSC in different BG ROIs can be compared across studies using these different masking methods. The masking method used does not affect the overall interpretation of results with respect to the effect of task. Use of a mask drawn in standardized space is a valid and time saving method of identifying PSC in the small nuclei of the BG.

Detecting hippocampal hypometabolism in Mild Cognitive Impairment using automatic voxel-based approaches

While the hippocampus is constantly reported as the site of earliest and highest structural alteration in Alzheimer's disease (AD), findings regarding the metabolic status of this region are rather heterogeneous. It has been proposed that only a time-consuming individual region-of-interest (ROI) approach would allow the detection of hypometabolism in this complex and small area. Our main goal with this study is to assess whether more automatic and clinically useful methods would be sensitive enough when considering other methodological confounds. From a single PET data set collected in 28 patients with amnestic Mild Cognitive Impairment (aMCI) and 19 controls, we assessed the effects of partial volume effect (PVE) correction, scaling (using vermis or global means), and analysis method (individual ROI versus more automatic template-based ROI or voxel-based approaches) on hippocampal hypometabolism detection in aMCI. PVE correction and scaling both showed a significant effect on group comparison, while the analysis method (individual versus template-based ROI) surprisingly did not. Hippocampal metabolic decrease was significant in all vermis-scaled conditions, and more so after PVE correction. Our findings highlight the crucial relevance of using reference-region-based (instead of global) scaling, and the higher sensitivity of PVE-corrected PET measures, to detect hippocampal hypometabolism in aMCI. They also show that hippocampal metabolic decline can be detected using template-based ROI as well as voxel-based methods. These findings have clinical relevance since they support the validity of more automatic and time-saving approaches to assess hippocampal metabolism changes in aMCI and early AD.

Use of a neural mass model for the analysis of effective connectivity among cortical regions based on high resolution EEG recordings

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in cortical electroencephalogram (EEG) power spectral density, and investigate the possibility to derive connectivity circuits from EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in Wendling et al. (Eur J Neurosci 15:1499-1508, 2002). Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. The following main steps have been followed: (1) we analyzed how the power spectral density (PSD) is significantly modified by the kind of coupling hypothesized among the ROIs; (2) with the model, and using an automatic fitting procedure, we looked for a simple connectivity circuit able to reproduce PSD of cortical EEG in three ROIs during a finger-movement task. The estimated parameters represent the strength of connections among the ROIs in the different frequency bands. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with 96 electrodes on the scalp. The present study suggests that the model can be used as a simulation tool, able to mimic the effect of connectivity on EEG. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospectives in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps

The statistical inference on functional imaging data is severely complicated by the embedded multiple testing problem. Defining a region of interest (ROI) where the activation is hypothesized a priori helps to circumvent this problem, since in this case the inference is restricted to fewer simultaneous tests, rendering it more sensitive. Cytoarchitectonic maps obtained from postmortem brains provide objective, a priori ROIs that can be used to test anatomically specified hypotheses about the localization of functional activations. We here analyzed three methods for the definition of ROIs based on probabilistic cytoarchitectonic maps. (1) ROIs defined by the volume assigned to a cytoarchitectonic area in the summary map of all areas (maximum probability map, MPM), (2) ROIs based on thresholding the individual probabilistic maps and (3) spherical ROIs build around the cytoarchitectonic center of gravity. The quality with which the thus defined ROIs represented the respective cytoarchitectonic areas as well as their sensitivity for detecting functional activations was subsequently statistically evaluated. Our data showed that the MPM method yields ROIs, which reflect most adequately the underlying anatomical hypotheses. These maps also show a high degree of sensitivity in the statistical analysis. We thus propose the use of MPMs for the definition of ROIs. In combination with thresholding based on the Gaussian random field theory, these ROIs can then be applied to test anatomically specified hypotheses in functional neuroimaging studies.