Schizophrenia has been increasingly conceptualized as a disorder of brain connectivity, in large part due to findings emerging from white matter and functional connectivity (FC) studies. This work has focused primarily on within-hemispheric connectivity, however some evidence has suggested abnormalities in callosal structure and interhemispheric interaction. Here we examined functional connectivity between homotopic points in the brain using a technique called voxel-mirrored homotopic connectivity (VMHC). We performed VMHC analyses on resting state fMRI data from 23 healthy controls and 25 patients with schizophrenia or schizoaffective disorder. We found highly significant reductions in VMHC in patients for a number of regions, particularly the occipital lobe, the thalamus, and the cerebellum. No regions of increased VMHC were detected in patients. VMHC in the postcentral gyrus extending into the precentral gyrus was correlated with PANSS Total scores. These results show substantial impairment of interhemispheric coordination in schizophrenia.
Schizophrenia; resting state; interhemispheric; MRI
The present study investigates the relationship between inter-individual differences in fearful face recognition and amygdala volume. Thirty normal adults were recruited and each completed two identical facial expression recognition tests offline and two magnetic resonance imaging (MRI) scans. Linear regression indicated that the left amygdala volume negatively correlated with the accuracy of recognizing fearful facial expressions and positively correlated with the probability of misrecognizing fear as surprise. Further exploratory analyses revealed that this relationship did not exist for any other subcortical or cortical regions. Nor did such a relationship exist between the left amygdala volume and performance recognizing the other five facial expressions. These mind-brain associations highlight the importance of the amygdala in recognizing fearful faces and provide insights regarding inter-individual differences in sensitivity toward fear-relevant stimuli.
BACKGROUND AND PURPOSE
CC is extensively involved in MS with interhemispheric dysfunction. The purpose of this study was to determine whether interhemispheric correlation is altered in MS by use of a recently developed RS-fMRI homotopy technique and whether these homotopic changes correlate with CC pathology.
MATERIALS AND METHODS
Twenty-four patients with relapsing-remitting MS and 24 age-matched healthy volunteers were studied with RS-fMRI and DTI acquired at 3T. The Pearson correlation of each pair of symmetric interhemispheric voxels of RS-fMRI time-series data was performed to compute VMHC, and z-transformed for subsequent group-level analysis. In addition, 5 CC segments in the midsagittal area and DTI-derived FA were measured to quantify interhemispheric microstructural changes and correlate with global and regional VMHC in MS.
Relative to control participants, patients with MS exhibited an abnormal homotopic pattern with decreased VMHC in the primary visual, somatosensory, and motor cortices and increased VMHC in several regions associated with sensory processing and motor control including the insula, thalamus, pallidum, and cerebellum. The global VMHC correlates moderately with the average FA of the entire CC for all participants in both groups (r = 0.3; P = .03).
Our data provide preliminary evidence of the potential usefulness of VMHC analyses for the detection of abnormalities of interhemispheric coordination in MS. We demonstrated that the whole-brain homotopic RS-fMRI pattern was altered in patients with MS, which was partially associated with the underlying structural degenerative changes of CC measured with FA.
In this paper, a Bregman iteration based total variation image restoration algorithm is proposed. Based on the Bregman iteration, the algorithm splits the original total variation problem into sub-problems that are easy to solve. Moreover, non-local regularization is introduced into the proposed algorithm, and a method to choose the non-local filter parameter locally and adaptively is proposed. Experiment results show that the proposed algorithms outperform some other regularization methods.
Spontaneous brain activity or off-line activity after memory encoding is associated with memory consolidation. A few recent resting-state functional magnetic resonance imaging (RS-fMRI) studies indicate that the RS-fMRI could map off-line memory consolidation effects. However, the gene effects on memory consolidation process remain largely unknown. Here we collected two RS-fMRI sessions, one before and another after an episodic memory encoding task, from two groups of healthy young adults, one with apolipoprotein E (APOE) ε2/ε3 and the other with APOE ε3/ε4. The ratio of regional homogeneity (ReHo), a measure of local synchronization of spontaneous RS-fMRI signal, of the two sessions was used as an index of memory-consolidation. APOE ε3/ε4 group showed greater ReHo ratio within the medial temporal lobe (MTL). The ReHo ratio in MTL was significantly correlated with the recognition memory performance in the APOE ε3/ε4 group but not in ε2/ε3 group. Additionally, APOE ε3/ε4 group showed lower ReHo ratio in the occipital and parietal picture-encoding areas. Our results indicate that APOE ε3/ε4 group may have a different off-line memory consolidation process compared to ε2/ε3 group. These results may help generate future hypotheses that the off-line memory consolidation might be impaired in Alzheimer’s disease.
A common registration problem for the application of consumer device is to align all the acquired image sequences into a complete scene. Image alignment requires a registration algorithm that will compensate as much as possible for geometric variability among images. However, images captured views from a real scene usually produce different distortions. Some are derived from the optic characteristics of image sensors, and others are caused by the specific scenes and objects.
An image registration algorithm considering the perspective projection is proposed for the application of consumer devices in this study. It exploits a multiresolution wavelet-based method to extract significant features. An analytic differential approach is then proposed to achieve fast convergence of point matching. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based modified Levenberg-Marquardt method. Due to its feature-based and nonlinear characteristic, it converges considerably faster than most other methods. In addition, vignette compensation and color difference adjustment are also performed to further improve the quality of registration results.
The performance of the proposed method is evaluated by testing the synthetic and real images acquired by a hand-held digital still camera and in comparison with two registration techniques in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is promising in registration accuracy and quality, which are statistically significantly better than other two approaches.
Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel’s BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites.
functional connectivity; functional data analysis; model selection; quantile regression; resting state; seed region
Models of Autism Spectrum Disorders (ASD) as neural dysconnection syndromes have been predominantly supported by examinations of abnormalities in cortico-cortical networks in adults with autism. A broader body of research implicates subcortical structures, particularly the striatum, in the physiopathology of autism. Resting state fMRI has revealed detailed maps of striatal circuitry in healthy and psychiatric populations, and vividly captured maturational changes in striatal circuitry during typical development.
Using resting state fMRI, we examined striatal functional connectivity in 20 children with ASD and 20 typically developing children (TDC) between the age of 7.6 and 13.5 years. Whole-brain voxel-wise statistical maps quantified within-group striatal FC and between-group differences for three caudate and three putamen seeds, for each hemisphere.
Children with ASD mostly exhibited prominent patterns of ectopic striatal functional connectivity (i.e., functional connectivity present in ASD but not in TDC), with increased functional connectivity between nearly all striatal subregions and heteromodal associative and limbic cortex previously implicated in the physiopathology of ASD (e.g., insular and right superior temporal gyrus). Additionally, we found striatal functional hyperconnectivity with the pons, thus expanding the scope of functional alterations implicated in ASD. Secondary analyses revealed ASD-related hyperconnectivity between the pons and insular cortex.
Examination of functional connectivity of striatal networks in children with ASD revealed abnormalities in circuits involving early developing areas such as the brainstem and insula, with a pattern of increased functional connectivity in ectopic circuits that likely reflects developmental derangement rather than immaturity of functional circuits.
Autism; Striatum; Functional Connectivity; Brainstem; Insula; Development
Models of cocaine addiction emphasize the role of disrupted frontal circuitry supporting cognitive control processes. Yet, addiction-related alterations in functional interactions among brain regions, especially between the cerebral hemispheres, are rarely examined directly. Resting state fMRI approaches, which reveal patterns of coherent spontaneous fluctuations in the fMRI signal, offer a means to directly quantify functional interactions between the hemispheres. We examined interhemispheric resting state functional connectivity (RSFC) in cocaine dependence using a recently validated approach named “voxel-mirrored homotopic connectivity.”
We compared interhemispheric RSFC between 25 adults (aged 35.0±8.8) meeting DSM-IV criteria for cocaine dependence within the past 12 months, but currently abstaining (>2 weeks) from cocaine, and 24 healthy comparisons (35.1±7.5), group-matched on age, sex, education and employment status.
We observed reduced prefrontal interhemispheric RSFC in cocaine dependent participants relative to controls. Further analyses demonstrated a striking cocaine-dependence-related reduction in interhemispheric RSFC among nodes of the dorsal attention network (DAN), comprising bilateral lateral frontal, medial premotor and posterior parietal areas. Further, within the cocaine-dependent group, RSFC within the DAN was associated with self-reported lapses of attention.
Our findings provide further evidence of an association between chronic exposure to cocaine and disruptions within large-scale brain circuitry supporting cognitive control. We did not detect group differences in DTI measures, suggesting that alterations in the brain’s functional architecture associated with cocaine exposure can be observed in the absence of detectable abnormalities in the white matter microstructure supporting that architecture.
cocaine; resting state functional connectivity; interhemispheric; fMRI; prefrontal; cognitive control
The brain’s energy economy excessively favors intrinsic, spontaneous neural activity over extrinsic, evoked activity, presumably to maintain its internal organization. Emerging hypotheses capable of explaining such an investment posit that the brain’s intrinsic functional architecture encodes a blueprint for its repertoire of responses to the external world. Yet, there is little evidence directly linking intrinsic and extrinsic activity in the brain. Here we relate differences among individuals in the magnitude of task-evoked activity during performance of an Eriksen flanker task, to spontaneous oscillatory phenomena observed during rest. Specifically, we focused on the amplitude of low-frequency oscillations (LFO, 0.01–0.1Hz) present in the BOLD signal. LFO amplitude measures obtained during rest successfully predicted the magnitude of task-evoked activity in a variety of regions that were all activated during performance of the flanker task. In these regions, higher LFO amplitude at rest predicted higher task-evoked activity. LFO amplitude measures obtained during rest were also found to have robust predictive value for behavior. In midline cingulate regions, LFO amplitudes not only predicted the speed and consistency of performance, but also the magnitude of the behavioral congruency effect embedded in the flanker task. These results support the emerging hypothesis that the brain’s repertoire of responses to the external world are represented and updated in the brain’s intrinsic functional architecture.
resting state; intrinsic; extrinsic; functional networks; fALFF
Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization.
Methodology and Principal Findings
We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network.
The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.
Personality describes persistent human behavioral responses to broad classes of environmental stimuli. Investigating how personality traits are reflected in the brain's functional architecture is challenging, in part due to the difficulty of designing appropriate task probes. Resting-state functional connectivity (RSFC) can detect intrinsic activation patterns without relying on any specific task. Here we use RSFC to investigate the neural correlates of the five-factor personality domains. Based on seed regions placed within two cognitive and affective ‘hubs’ in the brain—the anterior cingulate and precuneus—each domain of personality predicted RSFC with a unique pattern of brain regions. These patterns corresponded with functional subdivisions responsible for cognitive and affective processing such as motivation, empathy and future-oriented thinking. Neuroticism and Extraversion, the two most widely studied of the five constructs, predicted connectivity between seed regions and the dorsomedial prefrontal cortex and lateral paralimbic regions, respectively. These areas are associated with emotional regulation, self-evaluation and reward, consistent with the trait qualities. Personality traits were mostly associated with functional connections that were inconsistently present across participants. This suggests that although a fundamental, core functional architecture is preserved across individuals, variable connections outside of that core encompass the inter-individual differences in personality that motivate diverse responses.
The recent upsurge in interest about pediatric bipolar disorder (BD) has spurred the need for greater understanding of its neurobiology. Structural and functional magnetic resonance imaging (MRI) studies have implicated fronto-temporal dysfunction in pediatric BD. However, recent data suggest that task-dependent neural changes account for a small fraction of the brain’s energy consumption. We now report the first use of task-independent spontaneous resting state functional connectivity (RSFC) to study the neural underpinnings of pediatric BD.
We acquired a task-independent RSFC blood oxygen level-dependent fMRI scans while participants were at rest and also a high-resolution anatomical image (both at 3 Tesla) in BD and control youths (N=15 of each). Based on prior research, we focused on the left dorsolateral prefrontal cortex (DLPFC), amygdala, and accumbens. Image processing and group-level analyses followed that of prior work.
Our primary analysis showed that pediatric BD participants had significantly greater negative RSFC between the left DLPFC and the right superior temporal gyrus (STG) versus controls. Secondary analyses using partial correlation showed that BD and control youths had opposite phase relationships between spontaneous RSFC fluctuations in the left DLPFC and right STG.
Our data indicate that pediatric BD is characterized by altered task-independent functional connectivity in a fronto-temporal circuit that is also implicated in working memory and learning. Further study is warranted to determine the effects of age, sex, development, and treatment on this circuit in pediatric BD.
Bipolar Disorder; Child; Adolescent; Magnetic Resonance Imaging; Frontal Lobe; Temporal Lobe
Neuroimaging community usually employs spatial smoothing to denoise magnetic resonance imaging (MRI) data, e.g., Gaussian smoothing kernels. Such an isotropic diffusion (ISD) based smoothing is widely adopted for denoising purpose due to its easy implementation and efficient computation. Beyond these advantages, Gaussian smoothing kernels tend to blur the edges, curvature and texture of images. Researchers have proposed anisotropic diffusion (ASD) and non-local diffusion (NLD) kernels. We recently demonstrated the effect of these new filtering paradigms on preprocessing real degraded MRI images from three individual subjects. Here, to further systematically investigate the effects at a group level, we collected both structural and functional MRI data from 23 participants. We first evaluated the three smoothing strategies' impact on brain extraction, segmentation and registration. Finally, we investigated how they affect subsequent mapping of default network based on resting-state functional MRI (R-fMRI) data. Our findings suggest that NLD-based spatial smoothing maybe more effective and reliable at improving the quality of both MRI data preprocessing and default network mapping. We thus recommend NLD may become a promising method of smoothing structural MRI images of R-fMRI pipeline.
Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.
Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.
Functional homotopy, the high degree of synchrony in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions, is a fundamental characteristic of the brain’s intrinsic functional architecture. Yet, despite its prominence, the lifespan development of human brain’s homotopic resting-state functional connectivity (RSFC) is rarely directly examined in functional magnetic resonance imaging studies. Here, we systematically investigated age-related changes in homotopic RSFC in 214 healthy individuals ranging in age from 7 to 85. We observed marked age-related changes in homotopic RSFC with regionally specific developmental trajectories of varying levels of complexity. Sensorimotor regions tended to show increasing homotopic RSFC whereas higher order processing regions showed decreasing connectivity (i.e., increasing segregation) with age. More complex maturational curves were also detected, with regions such as the insula and lingual gyrus exhibiting quadratic trajectories, and the superior frontal gyrus and putamen exhibiting cubic trajectories. Sex-related differences in the developmental trajectory of functional homotopy were detected within dorsolateral prefrontal cortex (BA 9 and 46) and amygdala. Evidence of robust developmental effects in homotopic RSFC across the lifespan should serve to motivate studies of the physiological mechanisms underlying functional homotopy in neurodegenerative and psychiatric disorders.
brain development; age factors; brain homotopy; intrinsic brain activity; resting-state functional imaging; functional connectivity; functional MRI
The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by task performance remains unclear. Here we examine the relationship between a region’s pattern of RSFC across participants, and that same region’s level of BOLD activation during an Eriksen Flanker task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between task-induced activation and RSFC strength for 6 different seed regions (Fox et al., 2005), as well as the “default mode” and “task-positive” resting-state networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in task-induced BOLD activity were predicted by one of two resting-state properties: 1) the region’s positive connectivity strength with the task-positive network, or 2) its negative connectivity with the default mode network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and task-induced BOLD activity were located in transition zones between the default mode and task-positive networks. These results suggest that a common mechanism governs many brain regions’ neural activity during rest and its neural activity during task performance.
resting-state; task activity; intrinsic representation; voxel-matched; transition zones
Recently, a great deal of interest has arisen in resting state fMRI as a measure of tonic brain function in clinical populations. Most studies have focused on the examination of temporal correlation between resting state fMRI low-frequency oscillations (LFOs). Studies on the amplitudes of these low-frequency oscillations are rarely reported. Here, we used amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) to examine the amplitude of LFO in schizophrenia. Twenty-six healthy controls and 29 patients with schizophrenia or schizoaffective disorder participated. Our findings show that patients showed reduced low-frequency amplitude in proportion to the total frequency band investigated (i.e., fALFF) in the lingual gyrus, left cuneus, left insula/superior temporal gyrus, and right caudate and increased fALFF in the medial prefrontal cortex and the right parahippocampal gyrus. ALFF was reduced in patients in the lingual gyrus, cuneus, and precuneus and increased in the left parahippocampal gyrus. These results suggest LFO abnormalities in schizophrenia. The implication of these abnormalities for schizophrenic symptomatology is further discussed.
Low-frequency oscillation; Schizophrenia; Resting state fMRI
Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity (RSFC) maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (< 45 minutes) and long-term (5 − 16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.
test-retest reliability; intrinsic connectivity network; ICA; dual regression; resting state
The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test-retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the “default-mode” network. Additionally, we found that high amplitude LFO activity in specific brain regions was reliable across time. Further, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027 - 0.073 Hz) band as defined by Buzsáki et al. were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future “resting-state” fMRI datasets.
Magnetic resonance imaging (MRI) applied to the hippocampus is challenging in studies of the neurophysiology of memory and the physiopathology of numerous diseases such as epilepsy, Alzheimer’s disease, ischemia, and depression. The hippocampus is a well-delineated cerebral structure with a multi-layered organization. Imaging of hippocampus layers is limited to a few studies and requires high magnetic field and gradient strength. We performed one conventional MRI sequence on a 7T MRI in order to visualize and to delineate the multi-layered hippocampal structure ex vivo in rat brains. We optimized a volumic three-dimensional T2 Rapid Acquisition Relaxation Enhancement (RARE) sequence and quantified the volume of the hippocampus and one of its thinnest layers, the stratum granulare of the dentate gyrus. Additionally, we tested passive staining by gadolinium with the aim of decreasing the acquisition time and increasing image contrast. Using appropriated settings, six discrete layers were differentiated within the hippocampus in rats. In the hippocampus proper or Ammon’s Horn (AH): the stratum oriens, the stratum pyramidale of, the stratum radiatum, and the stratum lacunosum moleculare of the CA1 were differentiated. In the dentate gyrus: the stratum moleculare and the stratum granulare layer were seen distinctly. Passive staining of one brain with gadolinium decreased the acquisition time by four and improved the differentiation between the layers. A conventional sequence optimized on a 7T MRI with a standard receiver surface coil will allow us to study structural layers (signal and volume) of hippocampus in various rat models of neuropathology (anxiety, epilepsia, neurodegeneration).
To investigate functional brain networks, many graph-theoretical studies have defined nodes in a graph using an anatomical atlas with about a hundred partitions. Although use of anatomical node definition is popular due to its convenience, functional inhomogeneity within each node may lead to bias or systematic errors in the graph analysis. The current study was aimed to show functional inhomogeneity of a node defined by an anatomical atlas and to show its effects on the graph topology. For this purpose, we compared functional connectivity defined using 138 resting state fMRI data among 90 cerebral nodes from the automated anatomical labeling (AAL), which is an anatomical atlas, and among 372 cerebral nodes defined using a functional connectivity-based atlas as a ground truth, which was obtained using anatomy-constrained hierarchical modularity optimization algorithm (AHMO) that we proposed to evaluate the graph properties for anatomically defined nodes. We found that functional inhomogeneity in the anatomical parcellation induced significant biases in estimating both functional connectivity and graph-theoretical network properties. We also found very high linearity in major global network properties and nodal strength at all brain regions between anatomical atlas and functional atlas with reasonable network-forming thresholds for graph construction. However, some nodal properties such as betweenness centrality did not show significant linearity in some regions. The current study suggests that the use of anatomical atlas may be biased due to its inhomogeneity, but may generally be used in most neuroimaging studies when a single atlas is used for analysis.
Type 1 diabetes mellitus (T1DM) usually begins in childhood and adolescence and causes lifelong damage to several major organs including the brain. Despite increasing evidence of T1DM-induced structural deficits in cortical regions implicated in higher cognitive and emotional functions, little is known whether and how the structural connectivity between these regions is altered in the T1DM brain. Using inter-regional covariance of cortical thickness measurements from high-resolution T1-weighted magnetic resonance data, we examined the topological organizations of cortical structural networks in 81 T1DM patients and 38 healthy subjects. We found a relative absence of hierarchically high-level hubs in the prefrontal lobe of T1DM patients, which suggests ineffective top-down control of the prefrontal cortex in T1DM. Furthermore, inter-network connections between the strategic/executive control system and systems subserving other cortical functions including language and mnemonic/emotional processing were also less integrated in T1DM patients than in healthy individuals. The current results provide structural evidence for T1DM-related dysfunctional cortical organization, which specifically underlie the top-down cognitive control of language, memory, and emotion.