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Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications., Competing Interests: Declaration of competing interest None, (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The white matter in the brain is composed of neural fibers that wire the cortical and subcortical brain systems. Considering the complexity in terms of interconnections of many neural populations within the narrow space surrounded by the folding walls of the gray matter, the brain requires a certain way of structured wiring. To explore the three-dimensional organization of wiring in an accessible manner, we focused on voxel-level wiring patterns in the white matter according to cortical distributions in which each voxel mediates the wiring. We constructed a voxel-wise connection distribution map from the whole white matter voxels to 360 cortical regions using probabilistic tractography of the 100 diffusion imaging data in the Human Connectome Project. We then explored the spatial organization of the fiber bundles at the white matter voxels in terms of the maximal and second maximal cortical connection labels and the local gradient and entropy of cortical connection density using the population connection distribution map. We presented dominant cortical connection labels, local gradient, and connection entropy for the most representative white matter regions, including the internal capsule, external capsule, corpus callosum, cingulum bundle, and uncinate fascicles, most of which were introduced in the current study. Those major tracts showed a gradient organization of connection distributions for individual voxels. This organized pattern, as reflected in the whole brain connection distribution map, could be established to optimize wiring in the entire brain within the physical space of the white matter., (Copyright © 2022. Published by Elsevier Inc.)

The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment., (Copyright © 2021. Published by Elsevier Inc.)

Common representations of functional networks of resting state fMRI time series, including covariance, precision, and cross-correlation matrices, belong to the family of symmetric positive definite (SPD) matrices forming a special mathematical structure called Riemannian manifold. Due to its geometric properties, the analysis and operation of functional connectivity matrices may well be performed on the Riemannian manifold of the SPD space. Analysis of functional networks on the SPD space takes account of all the pairwise interactions (edges) as a whole, which differs from the conventional rationale of considering edges as independent from each other. Despite its geometric characteristics, only a few studies have been conducted for functional network analysis on the SPD manifold and inference methods specialized for connectivity analysis on the SPD manifold are rarely found. The current study aims to show the significance of connectivity analysis on the SPD space and introduce inference algorithms on the SPD manifold, such as regression analysis of functional networks in association with behaviors, principal geodesic analysis, clustering, state transition analysis of dynamic functional networks and statistical tests for network equality on the SPD manifold. We applied the proposed methods to both simulated data and experimental resting state fMRI data from the human connectome project and argue the importance of analyzing functional networks under the SPD geometry. All the algorithms for numerical operations and inferences on the SPD manifold are implemented as a MATLAB library, called SPDtoolbox, for public use to expediate functional network analysis on the right geometry., (Copyright © 2020. Published by Elsevier Inc.)

The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx -/+ ;Dlx5/6 CRE-IRES-GFP ) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI., (Copyright © 2019 Elsevier Inc. All rights reserved.)

During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Although the relationship between resting-state functional connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or effective connectivity - and its behavioral concomitants - remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (A rest ) and task states (A task ), (ii) cluster phenotypes of task-related changes in effective connectivity (B task ) across participants, (iii) identify edges (B task ) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between B task and A rest in these edges. We found a strong correlation between A rest and A task over subjects but a marked difference between B task and A rest . We further observed a strong clustering of individuals in terms of B task , which was not apparent in A rest . The task-related effective connectivity B task varied highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between B task and A rest at the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling - from resting-state connectivity - is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The configuration of the human brain system at rest, which is in a transitory phase among multistable states, remains unknown. To investigate the dynamic systems properties of the human brain at rest, we constructed an energy landscape for the state dynamics of the subcortical brain network, a critical center that modulates whole brain states, using resting state fMRI. We evaluated alterations in energy landscapes following perturbation in network parameters, which revealed characteristics of the state dynamics in the subcortical brain system, such as maximal number of attractors, unequal temporal occupations, and readiness for reconfiguration of the system. Perturbation in the network parameters, even those as small as the ones in individual nodes or edges, caused a significant shift in the energy landscape of brain systems. The effect of the perturbation on the energy landscape depended on the network properties of the perturbed nodes and edges, with greater effects on hub nodes and hubs-connecting edges in the subcortical brain system. Two simultaneously perturbed nodes produced perturbation effects showing low sensitivity in the interhemispheric homologous nodes and strong dependency on the more primary node among the two. This study demonstrated that energy landscape analysis could be an important tool to investigate alterations in brain networks that may underlie certain brain diseases, or diverse brain functions that may emerge due to the reconfiguration of the default brain network at rest., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Recent understanding that the brain at rest does not remain in a single state but transiently visits multiple states emphasizes the importance of state changes embedded in the brain network. Due to the effectiveness of larger networks in characterizing brain states, there is an increasing need for a network-based change point detection method that is applicable to large-size networks, particularly those with longer time series. This paper presents a fast and efficient method for detecting change points in the large-size functional networks of resting-state fMRI. To detect change points, a statistic for the covariance change at each time point is tested by a local false discovery rate, estimated based on the empirical null principle using a semiparametric mixture model. We present simulations and empirical analyses of task-based and resting-state fMRI data sets with various network sizes up to 300 nodes selected from the Human Connectome Project database. We demonstrate that the proposed method is not only efficient in detecting change points in large samples of large-size networks but also is less sensitive to the window size selection and provides the consequent identification of the changed edges. The covariance-based change point detection method in this study would be very useful in exploring characteristics of dynamic states in long-term large-size resting-state brain networks., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification., (Copyright © 2014 Elsevier Inc. All rights reserved.)

In ordinary conversations, literal meanings of an utterance are often quite different from implicated meanings and the inference about implicated meanings is essentially required for successful comprehension of the speaker's utterances. Inference of finding implicated meanings is based on the listener's assumption that the conversational partner says only relevant matters according to the maxim of relevance in Grice's theory of conversational implicature. To investigate the neural correlates of comprehending implicated meanings under the maxim of relevance, a total of 23 participants underwent an fMRI task with a series of conversational pairs, each consisting of a question and an answer. The experimental paradigm was composed of three conditions: explicit answers, moderately implicit answers, and highly implicit answers. Participants were asked to decide whether the answer to the Yes/No question meant 'Yes' or 'No'. Longer reaction time was required for the highly implicit answers than for the moderately implicit answers without affecting the accuracy. The fMRI results show that the left anterior temporal lobe, left angular gyrus, and left posterior middle temporal gyrus had stronger activation in both moderately and highly implicit conditions than in the explicit condition. Comprehension of highly implicit answers had increased activations in additional regions including the left inferior frontal gyrus, left medial prefrontal cortex, left posterior cingulate cortex and right anterior temporal lobe. The activation results indicate involvement of these regions in the inference process to build coherence between literally irrelevant but pragmatically associated utterances under the maxim of relevance. Especially, the left anterior temporal lobe showed high sensitivity to the level of implicitness and showed increased activation for highly versus moderately implicit conditions, which imply its central role in inference such as semantic integration. The right hemisphere activation, uniquely found in the anterior temporal lobe for highly implicit utterances, suggests its competence for integrating distant concepts in implied utterances under the relevance principle., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Despite the widespread view of the brain as a large complex network, the dynamicity of the brain network over the course of a day has yet to be explored. To investigate whether the spontaneous human brain network maintains long-term stability throughout a day, we evaluated the intra-class correlation coefficient (ICC) of results from an independent component analysis (ICA), seed correlation analysis, and graph-theoretical analysis of resting state functional MRI, acquired from 12 young adults at three-hour intervals over 24 consecutive hours. According to the ICC of the usage strength of the independent network component defined by the root mean square of the temporal weights of the network components, the default mode network centered at the posterior cingulate cortex and precuneus, the superior parietal, and secondary motor networks showed a high temporal stability throughout the day (ICC>0.5). However, high intra-individual dynamicity was observed in the default mode network, including the anterior cingulate cortex and medial prefrontal cortex or posterior-anterior cingulate cortex, the hippocampal network, and the parietal and temporal networks. Seed correlation analysis showed a highly stable (ICC>0.5) extent of functionally connected regions from the posterior cingulate cortex, but poor stability from the hippocampus throughout the day. Graph-theoretical analysis using local and global network efficiency suggested that local brain networks are temporally stable but that long-range integration behaves dynamically in the course of a day. These results imply that dynamic network properties are a nature of the resting state brain network, which remains to be further researched., (Copyright © 2011 Elsevier Inc. All rights reserved.)

High b-valued diffusion-weighted images (DWI), which were designed to solve fiber-crossing problems, are susceptible to many artifacts and distortions. Since DWIs with different diffusion gradients produce dissimilar intensity contrasts, and since the distortion is nonlinear when multiple artifactual sources are intermixed, the mutual information-based affine registration may not be adequate for precise correction of distortions in DWIs, especially for images acquired with high b-values. To overcome these problems, we proposed an iterative image registration technique through which simulated DWIs are generated, driven from a diffusion tensor estimate, as targets for measured DWIs in the registration. Since simulated DWIs have similar intensity profiles to those of measured DWIs and the same geometric profiles as b(0)-images, an iterative procedure enables intensity-based nonlinear registration. As a pre-processing step, we also proposed a motion detection and sub-volume utilization for interleaved volumes. Performance evaluation with high b-valued DWIs for high angular resolution diffusion imaging and diffusion kurtosis imaging showed that the proposed method had a superior advantage over the conventional registration technique., (Copyright © 2011 Elsevier Inc. All rights reserved.)

To explore the morphological aspects of the functional reorganization of the blind's visual cortex, we analyzed the regional cortical thickness and cortical surface area in the congenitally blind subjects (CB) compared to the late-onset blind (LB) and sighted controls (SC). Cortical thickness was calculated from high-resolution T1-weighted magnetic resonance images of 21 young CB (blind from birth, mean age=27.1 yr), 12 LB, and 35 young SC. Analysis of covariance of cortical layer thickness with global thickness, age, and gender as covariates was done node-by-node on the entire cortical surface. Further analysis of mean thickness and surface area was performed for 33 automatically parceled cortical regions. Voxel-based morphometry was also conducted to compare results with cortical thickness and surface area. We found increased cortical thickness in the regions involved in vision and eye movement, such as the pericalcarine sulcus, cingulate cortex, and right frontal eye field, but cortical thinning in the left somatosensory cortex and right auditory cortex of CB compared to SC. CB had significantly reduced surface extent in the primary and associated visual areas, which explains volumetric atrophies in the visual cortex of CB despite increased cortical thickness. Conversely, LB tended to have cortical thinning in the primary visual cortex with a slight or no significant reduction in the surface extent. These morphological alterations in CB may indicate cortical reorganization at the visual cortex in connection with other sensory cortices.

The purposes of this study were to examine the effect of sensitivity encoding (SENSE) factors on cortical thickness measurements and to determine which SENSE factor to use to reliably measure cortical thickness in 3.0 T and 1.5 T T1-weighted MRI images. The 3D T1-TFE images were acquired from 11 healthy volunteers with 6 different SENSE acceleration factors from 1.0 (without SENSE acceleration) to 4.0 on a 1.5 T scanner, and 9 different SENSE factors from 1.0 to 6.0, plus a second-day 1.0 acquisition on a 3.0 T scanner. Cortical thickness was calculated for the entire cortical surface that was further subdivided into 33 regions. Repeated measures multivariate analysis of variance revealed that the main effect of SENSE factors (F=12.485, df=7, p=0.006) was a significant underestimation of cortical thickness at SENSE 5.0 (p=0.022) and 6.0 (p=0.011) at 3.0 T and at SENSE 4.0 (p<0.000) at 1.5 T. Repeated measures ANOVA showed that thickness measurements at the insula, superior temporal sulcus, the medial part of the superior frontal lobe, and cingulate cortex are highly affected by SENSE factors. SENSE factors affect thickness estimation more significantly at 1.5 T and thus 1.5 T imaging provides less reliable estimates using SENSE techniques. Faster imaging can be done without too much loss of reliability using a high SENSE factor, such as 3.0, at 3.0 T with acquisition time being inversely proportional to the SENSE factor.

Periventricular leukomalacia (PVL) due to hypoxic-ischemic insult to the immature brain, chorioamnionitis and maternal infection are the major etiological factors of spastic type cerebral palsy (CP). Despite advances in preventing and treating certain causes of CP, the number of patients has remained essentially unchanged and the pathophysiological mechanisms related to motor dysfunction remain poorly understood. In this study, statistical parametric mapping (SPM) analysis of cerebral gamma-aminobutyric acid (GABA) receptor PET imaging using [18F]-fluoroflumazenil showed increased GABA(A) receptor binding in the bilateral motor and visual cortices in spastic diplegia (SD) type CP patients (n = 20) compared with normal controls (n = 10). As GABA(A) receptor signaling modulates biological perception and production of movement, complex motor skills and use-dependent plasticity in the motor cortex, increased GABA(A) receptor binding in the motor cortex might play a important role in poor motor control. Decreased GABA(A) receptor binding was seen in the brain stem in SD CP patients, which appears to be related to spastic symptom.

The purpose of the study is to propose a new framework for surface-based statistical parametric mapping of PET images using MRI-based cortical surface analysis, including partial volume correction, intensity normalization and spatial normalization on the cortical surface. Maximum PET intensities along the path between inner and outer layer of the cortical gray matter are mapped onto the cortical surface to generate a metabolic activity surface map. For the partial volume correction, the metabolic activity surface map was divided by the partial volume effect map. The regional metabolic activity was normalized by the global activity iteratively calculated at the surface nodes, statistically independent of the group, as measured by F statistics. After surface-based spatial normalization, a statistical evaluation of both cortical thickness and cortical metabolic activity was conducted on the normalized surfaces of 16 patients with schizophrenia and 16 age- and gender-matched healthy controls. The patients with schizophrenia were found to have significant cortical thinning in the temporal and inferior frontal cortices. Accordingly, their PET imaging was significantly affected by the partial volume effect, indicating that partial volume correction could change the statistical results. After correction of the partial volume effects, the patients showed hyperactivity in the temporal cortex, whereas hypoactivity in the prefrontal cortex, predominantly in the left hemisphere. Our results demonstrate that anatomical factors affect an analysis for functional data from the PET, and therefore the importance of combining anatomy and function in the analysis of imaging data for schizophrenia should be considered.

Schizophrenia has been suggested to be the result of both macroscopic and microscopic abnormalities in the brain. Although no definitive clinico-pathological correlations have been found to reconcile the many facets inherent in this disorder, the recent development of the magnetic resonance diffusion tensor imaging (DTI) has allowed us to gather useful information regarding the microcircuitry of the brain. Specifically, the apparent diffusion coefficient (ADC) reflects the degree of diffusion barriers and heterosynaptic communication for the brain neurotransmitter. Nineteen patients with DSM-IV schizophrenia and 21 age- and sex-matched control subjects participated in DTI, and the severity of the patients' symptoms was evaluated according to the Positive and Negative Syndrome Scale (PANSS). The ADC values were determined and compared between patients and control subjects via voxel-based morphometry. The results show an increased ADC in the bilateral fronto-temporal regions of the schizophrenic patients, as compared with those of the control subjects. In addition, the ADC values in the area of the right insular were correlated with the negative syndromes from the PANSS. Our findings of increased water diffusivity in the fronto-temporal regions of schizophrenic patients and the correlation between negative symptom scales and the ADC in the right insular region indicate that damaged brain microcircuitry might contribute to the pathophysiology of schizophrenia. These findings contribute towards integrating micro and macrostructural abnormalities and syndromes of schizophrenia.

Hemisphere asymmetry was explored in normal healthy subjects and in patients with schizophrenia using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. Our voxel-based approach, which requires precise spatial normalization to remove the misalignment of fiber tracts, includes generating a symmetrical group average template of the diffusion tensor by applying nonlinear elastic warping of the demons algorithm. We then normalized all 32 diffusion tensor MRIs from healthy subjects and 23 from schizophrenic subjects to the symmetrical average template. For each brain, six channels of tensor component images and one T2-weighted image were used for registration to match tensor orientation and shape between images. A statistical evaluation of white matter asymmetry was then conducted on the normalized FA images and their flipped images. In controls, we found left-higher-than-right anisotropic asymmetry in the anterior part of the corpus callosum, cingulum bundle, the optic radiation, and the superior cerebellar peduncle, and right-higher-than-left anisotropic asymmetry in the anterior limb of the internal capsule and the anterior limb's prefrontal regions, in the uncinate fasciculus, and in the superior longitudinal fasciculus. In patients, the asymmetry was lower, although still present, in the cingulum bundle and the anterior corpus callosum, and not found in the anterior limb of the internal capsule, the uncinate fasciculus, and the superior cerebellar peduncle compared to healthy subjects. These findings of anisotropic asymmetry pattern differences between healthy controls and patients with schizophrenia are likely related to neurodevelopmental abnormalities in schizophrenia.

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia., (Copyright 2004 Elsevier Inc.)

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.

We investigated the characteristics of P300 generators in schizophrenics by using voxel-based statistical parametric mapping of current density images. P300 generators, produced by a rare target tone of 1500 Hz (15%) under a frequent nontarget tone of 1000 Hz (85%), were measured in 20 right-handed schizophrenics and 21 controls. Low-resolution electromagnetic tomography (LORETA), using a realistic head model of the boundary element method based on individual MRI, was applied to the 128-channel EEG. Three-dimensional current density images were reconstructed from the LORETA intensity maps that covered the whole cortical gray matter. Spatial normalization and intensity normalization of the smoothed current density images were used to reduce anatomical variance and subject-specific global activity and statistical parametric mapping (SPM) was applied for the statistical analysis. We found that the sources of P300 were consistently localized at the left superior parietal area in normal subjects, while those of schizophrenics were diversely distributed. Upon statistical comparison, schizophrenics, with globally reduced current densities, showed a significant P300 current density reduction in the left medial temporal area and in the left inferior parietal area, while both left prefrontal and right orbitofrontal areas were relatively activated. The left parietotemporal area was found to correlate negatively with Positive and Negative Syndrome Scale total scores of schizophrenic patients. In conclusion, the reduced and increased areas of current density in schizophrenic patients suggest that the medial temporal and frontal areas contribute to the pathophysiology of schizophrenia, the frontotemporal circuitry abnormality.