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Olfactory perception depends not only on olfactory inputs but also on semantic context. Although multi-voxel activity patterns of the piriform cortex, a part of the primary olfactory cortex, have been shown to represent odor perception, it remains unclear whether semantic contexts modulate odor representation in this region. Here, we investigated whether multi-voxel activity patterns in the piriform cortex change when semantic context modulates odor perception and, if so, whether the modulated areas communicate with brain regions involved in semantic and memory processing beyond the piriform cortex. We also explored regional differences within the piriform cortex, which are influenced by olfactory input and semantic context. We used 2 × 2 combinations of word labels and odorants that were perceived as congruent and measured piriform activity with a 1-mm isotropic resolution using 7T MRI. We found that identical odorants labeled with different words were perceived differently. This labeling effect was observed in multi-voxel activity patterns in the piriform cortex, as the searchlight decoding analysis distinguished identical odors with different labels for half of the examined stimulus pairs. Significant functional connectivity was observed between parts of the piriform cortex that were modulated by labels and regions associated with semantic and memory processing. While the piriform multi-voxel patterns evoked by different olfactory inputs were also distinguishable, the decoding accuracy was significant for only one stimulus pair, preventing definitive conclusions regarding the locational differences between areas influenced by word labels and olfactory inputs. These results suggest that multi-voxel patterns of piriform activity can be modulated by semantic context, possibly due to communication between the piriform cortex and the semantic and memory regions., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

It is important to create a link between stereotaxic coordinates and head-surface-based positioning systems in order to share data between tomographic and transcranial brain mapping studies. In our previous studies, we established the probabilistic correspondence of the international 10-20 positions to the standard stereotaxic coordinate systems and made a reference database. However, its expansion required the physical marking of the 10-20 positions and the subsequent acquisition of MR images. To avoid such tedious procedures, we developed a virtual 10-20 measurement algorithm that can be applied to re-analyze any structural MR image that covers the whole head. As in the physical 10-20 measurements, with the reference points given, the algorithm automatically determines each 10-20 position step by step. Using the virtual 10-20 measurement method, we re-analyzed the MR images of 17 healthy subjects for whom we had determined 10-20 positions by physical marking in our previous study. The acquired 10-20 positions were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and compared with the positions previously determined by physical measurements. 10-20 positions determined using the virtual and physical methods were roughly consistent. Average standard deviations for virtual and physical methods were 7.7 mm and 9.0 mm, respectively. There was a systematic shift in the virtual method, likely due to the absence of hair interference. We corrected the shift with affine transformation. The virtual 10-20 measurement method proved to be an effective alternative to physical marking. This method will serve as an essential tool for expanding the reference database and will further strengthen the link between tomographic and transcranial brain mapping methods.

An intriguing application of neuroimaging is directly measuring actual human brain activities during daily living. To this end, we investigated cortical activation patterns during apple peeling. We first conducted a pilot study to assess the activation pattern of the whole lateral cortical surface during apple peeling by multichannel near-infrared spectroscopy (NIRS) and detected substantial activation in the prefrontal region in addition to expected activations extending over the motor, premotor and supplementary motor areas. We next examined cortical activation during mock apple peeling by simultaneous measurement using multichannel NIRS and functional magnetic resonance imaging (fMRI) in four subjects. We detected activations extending over the motor, premotor and supplementary motor areas, but not in the prefrontal cortex. Thus, we finally focused on the prefrontal cortex and examined its activation during apple peeling in 12 subjects using a multichannel NIRS. We subsequently found that regional concentrations of oxygenated hemoglobin significantly increased in the measured region, which encompassed portions of the dorsolateral, ventrolateral and frontopolar areas of the prefrontal cortex. The current study demonstrated that apple peeling as practiced in daily life recruited the prefrontal cortex but that such activation might not be detected for less laborious mock apple peeling that can be performed in an fMRI environment. We suggest the importance of cortical study of an everyday task as it is but not as a simplified form; we also suggest the validity of NIRS for this purpose. Studies on everyday tasks may serve as stepping stone toward understanding human activities in terms of cortical activations.

The recent advent of multichannel near-infrared spectroscopy (NIRS) has expanded its technical potential for human brain mapping. However, NIRS measurement has a technical drawback in that it measures cortical activities from the head surface without anatomical information of the object to be measured. This problem is also found in transcranial magnetic stimulation (TMS) that transcranially activates or inactivates the cortical surface. To overcome this drawback, we examined cranio-cerebral correlation using magnetic resonance imaging (MRI) via the guidance of the international 10-20 system for electrode placement, which had originally been developed for electroencephalography. We projected the 10-20 standard cranial positions over the cerebral cortical surface. After examining the cranio-cerebral correspondence for 17 healthy adults, we normalized the 10-20 cortical projection points of the subjects to the standard Montreal Neurological Institute (MNI) and Talairach stereotactic coordinates and obtained their probabilistic distributions. We also expressed the anatomical structures for the 10-20 cortical projection points probabilistically. Next, we examined the distance between the cortical surface and the head surface along the scalp and created a cortical surface depth map. We found that the locations of 10-20 cortical projection points in the standard MNI or Talairach space could be estimated with an average standard deviation of 8 mm. This study provided an initial step toward establishing a three-dimensional probabilistic anatomical platform that enables intra- and intermodal comparisons of NIRS and TMS brain imaging data.

Abstract: The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10–20 system without using the subject''s magnetic resonance image (MRI). In the current study, we demonstrate our method with a new statistical model to facilitate group studies and provide information on different components of variability. We adopt an analysis similar to the single-factor one-way classification analysis of variance based on random effects model to examine the variability involved in our improvised method of probabilistic registration of fNIRS data. We tested this method by registering head surface data of twelve subjects to seventeen reference MRI data sets and found that the standard deviation in probabilistic registration thus performed for given head surface points is approximately within the range of 4.7 to 7.0 mm. This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects'' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community. [Copyright &y& Elsevier]

Abstract: Recent advancements in two noninvasive transcranial neuroimaging techniques, near-infrared spectroscopy (NIRS) and transcranial magnetic stimulation (TMS), signify the increasing importance of establishing structural compatibility between transcranial methods and conventional tomographic methods, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). The transcranial data obtained from the head surface should be projected onto the cortical surface to present the transcranial brain-mapping data on the same platform as tomographic methods. Thus, we developed two transcranial projection algorithms that project given head-surface points onto the cortical surface in structural images, and computer programs based on them. The convex-hull algorithm features geometric handling of the cortical surface, while the balloon-inflation algorithm is faster, and better reflects the local cortical structure. The automatic cortical projection methods proved to be as effective as the manual projection method described in our previous study. These methods achieved perfect correspondence between any given point on the head surface or a related nearby point in space, and its cortical projection point. Moreover, we developed a neighbor-reference method that enables transcranial cortical projection of a given head-surface point in reference to three neighboring points and one additional standard point, even when no structural image of the subject is available. We also calculated an error factor associated with these probabilistic estimations. The current study presents a close topological link between transcranial and tomographic brain-mapping modalities, which could contribute to inter-modal data standardization. [Copyright &y& Elsevier]

Abstract: The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community. [Copyright &y& Elsevier]