Automated Anatomical Labeling Research Articles

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework.

This work aims to establish a classification framework for the diagnosis of mild cognitive impairment (MCI) at different stages (early MCI and late MCI) through direct analysis of resting-state functional magnetic resonance imaging (rs-fMRI) signals and using the accuracy (total correct rate), specificity (correct rate of late MCI) and sensitivity (correct rate of early MCI) to validate its classification performance. All fMR images of subjects were parcellated into 116 regions of interest (ROIs) by applying the Anatomical Automatic Labeling (AAL) template, and the average rs-fMRI signals of each ROI were extracted. The Hilbert-Huang transform (HHT) was introduced into the framework to decompose each rs-fMRI signal into a series of intrinsic mode functions (IMFs) and to analyze these nonstationary and nonlinear time-series from the perspective of multiresolution. After obtaining the instantaneous frequencies and amplitudes of all IMFs of a signal, the Hilbert weighted frequencies (HWFs) were calculated and combined into a vector as the feature of the corresponding ROI. Support Vector Machine (SVM) was implemented to classify MCI at different stages. We used the independent two-sample t-test as the feature selection method and measured the classification performance through the leave-one-out cross-validation (LOOCV) method. Results on 77 early MCI (eMCI) and 64 late MCI (lMCI) with baseline rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) yielded 87.94% classification accuracy. Some of the brain regions with significant differences found by previous studies have been confirmed in this work. We found that HWF characteristics exhibited a significant downward trend in all cerebellar regions. The rs-fMRI signals in differential brain regions have not changed completely, but only altered in some narrow frequency bands. The analysis results showed that during the progress of MCI, the main changes of rs-fMRI were concentrated in IMF3, while IMFs with other indexes also contained HWF features with high SVM weights, such as Orbitofrontal superior frontal gyrus in IMF2, Insula in IMF4, and Lobule Ⅲ of vermis in IMF5, indicating that other IMFs provide important information for the diagnosis of MCI as well. This work confirmed the classification ability of HHT-based classification framework in classification of at different stages of MCI. Through the analysis, we found that during the progress of MCI the main changes of rs-fMRI were concentrated in IMF3, and HWF characteristics showed a significant downward trend in all cerebellar regions.

Abnormal Rich Club Organization of Structural Network as a Neuroimaging Feature in Relation With the Severity of Primary Insomnia.

PurposeInsomnia is the most prevalent sleep complaint in the general population but is often intractable due to uncertainty regarding the underlying pathomechanisms. Sleep is regulated by a network of neural structures interconnected with the core nodes of the brain connectome referred to as the “rich club”. We examined alterations in brain rich-club organization as revealed by diffusion tensor imaging (DTI) and the statistical relationships between abnormalities in rich-club metrics and the clinical features of primary insomnia (PI).MethodsThis study recruited 43 primary insomnia (PI) patients and 42 age-, sex-, and education level-matched healthy controls (HCs). Differences in global and regional network parameters between PI and healthy control groups were compared by nonparametric tests, and Spearman's correlations were calculated to assess associations of these network metrics with PI-related clinical features, including disease duration and scores on the Pittsburgh Sleep Quality Index, Insomnia Severity Index, Self-Rating Anxiety Scale, and Self-Rating Depression Scale.ResultsWeighted white matter networks exhibited weaker rich-club organization in PI patients than HCs across different thresholds (50%, 75%, and 90%) and parcellation schemes [automated anatomical labeling (AAL)-90 and AAL-1024]. Aberrant rich-club organization was found mainly in limbic-cortical-basal ganglia circuits and the default-mode network.ConclusionsAbnormal rich-club metrics are a characteristic feature of PI-related to disease severity. These metrics provide potential clues to PI pathogenesis and may be useful as diagnostic markers and for assessment of treatment response.

Altered resting-state functional networks in patients with hemodialysis: a graph-theoretical based study.

Recent studies have demonstrated that hemodialysis patients exhibit disruptions in functional networks with invisible cerebral alterations. We explored the alterations of functional connectivity in hemodialysis patients using the graph-theory method. A total of 46 hemodialysis patients (53.11 ± 1.58 years, 28 males) and 47 healthy controls (55.57 ± 0.86 years, 22 males) were scanned byusing resting-state functional magnetic resonance imaging. The brains of these patients were divided into 90 regions and functional connectivity was constructed with the automatic anatomical labeling atlas. In the defined threshold range, the graph-theory analysis was performed to compare the topological properties including global, regional and edge parameters between the hemodialysis and the healthy control groups. Both hemodialysis patients and healthy control subjects demonstrated common small-world property of the brain functional connections. At the global level, the parameters normalized clustering coefficients and small-worldness were significantly decreased in hemodialysis patients compared with those noted in healthy controls. At the regional level, abnormal nodal metrics (increased or decreased nodal degree, betweenness centrality and efficiency) were widely found in hemodialysis patients compared with those of healthy controls. The network-based statistical method was employed and two disrupted neural circuits with 18 nodes and 19 edges (P = 0.0139, corrected) and 10 nodes and 11 edges (P = 0.0399, corrected) were detected. Of note, the edge-increased functional connectivity was associated with the salience network and the frontal-temporal-basal ganglia connection, whereas the edge-decreased functional connectivity was associated with the frontoparietal network. The graph-theory method may be one of the potential tools to detect disruptions of cerebral functional connectivity and provide important evidence for understanding the neuropathology of hemodialysis patients from the disrupted network organization perspective.

Juvenile myoclonic epilepsy shows increased posterior theta, and reduced sensorimotor beta resting connectivity

BackgroundWidespread structural and functional brain network changes have been shown in Juvenile Myoclonic Epilepsy (JME) despite normal clinical neuroimaging. We sought to better define these changes using magnetoencephalography (MEG) and source space connectivity analysis for optimal neurophysiological and anatomical localisation. MethodsWe consecutively recruited 26 patients with JME who underwent resting state MEG recording, along with 26 age-and-sex matched controls. Whole brain connectivity was determined through correlation of Automated Anatomical Labelling (AAL) atlas source space MEG timeseries in conventional frequency bands of interest delta (1−4 Hz), theta (4−8 Hz), alpha (8−13 Hz), beta (13−30 Hz) and gamma (40−60 Hz). We used a Linearly Constrained Minimum Variance (LCMV) beamformer to extract voxel wise time series of ‘virtual sensors’ for the desired frequency bands, followed by connectivity analysis using correlation between frequency- and node-specific power fluctuations, for the voxel maxima in each AAL atlas label, correcting for noise, potentially spurious connections and multiple comparisons. ResultsWe found increased connectivity in the theta band in posterior brain regions, surviving statistical correction for multiple comparisons (corrected p < 0.05), and decreased connectivity in the beta band in sensorimotor cortex, between right pre- and post- central gyrus (p < 0.05) in JME compared to controls. ConclusionsAltered resting-state MEG connectivity in JME comprised increased connectivity in posterior theta – the frequency band associated with long range connections affecting attention and arousal - and decreased beta-band sensorimotor connectivity. These findings likely relate to altered regulation of the sensorimotor network and seizure prone states in JME.

Hemangiopericytomas: Spatial Intracranial Location in a Voxel-Based Mapping Study.

To investigate the preferred location of intracranial hemangiopericytomas (IHPCs) with voxel-based mapping and 3-dimensional reconstruction from MRI data. Gadolinium-enhanced tumors of 258 primary and single IHPCs were segmented semi-automatically, followed by manual checking and editing of boundaries. The lesions were registered to Montreal Neurological Institute standard anatomical space, and heat-map and 3-dimensional rendered frequency images were generated. All tumors were then superimposed on the Anatomical Automatic Labeling (AAL) template to further investigate the difference in the tumor location based on the voxel-wise frequency of occurrence with respect to laterality, sex, age, and pathologic grade. The 3-dimensional rendered images show that the tumors commonly located in the posterior cranial cavity, surrounding the tentorium. The posterior third of the superior sagittal sinus and the confluence of sinuses were commonly affected. According to the analysis of tumor occurrence frequency in the AAL template, IHPCs were mainly observed in the limbic lobe, occipital lobe, and cerebellum. Tumors in younger patients preferentially located in the right occipital region (P = .027), whereas those with higher pathological grade more often located in the left parietal lobe (P = .034). This is the first voxel-based study to explore the predilection site of IHPCs. Our study suggests that these tumors commonly affect the posterior cranial cavity, adjoining the tentorium and venous sinus. Further research is needed to investigate the possible factors underlying these topographic preferences.

Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification

The classification of mild cognitive impairment (MCI), which is a early stage of Alzheimer’s disease and is associated with brain structural and functional changes, is still a challenging task. Recent studies have shown great promise for improving the performance of MCI classification by combining multiple structural and functional features, such as grey matter volume and clustering coefficient. However, extracting which features and how to combine multiple features to improve the performance of MCI classification have always been challenging problems. To address these problems, in this study we propose a new method to enhance the feature representation of multi-modal MRI data by combining multi-view information to improve the performance of MCI classification. Firstly, we extract two structural features (including grey matter volume and cortical thickness) and two functional features (including clustering coefficient and shortest path length) of each cortical brain region based on automated anatomical labeling (AAL) atlas from both T1w MRI and rs-fMRI data of each subject. Then, in order to obtain features that are more helpful in distinguishing MCI subjects, an improved multi-task feature selection method, namely MTFS-gLASSO-TTR, is proposed. Finally, a multi-kernel learning algorithm is adopted to combine multiple features to perform the MCI classification task. Our proposed MCI classification method is evaluated on 315 subjects (including 105 LMCI subjects, 105 EMCI subjects and 105 NCs) with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed method achieves an accuracy of 88.5% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.897 for LMCI/NC classification, an accuracy of 82.7% and an AUC of 0.832 for EMCI/NC classification, and an accuracy of 79.6% and an AUC of 0.803 for LMCI/EMCI classification, respectively. In addition, by comparison, the accuracy and AUC values of our proposed method are better than those of some existing state-of-the-art methods in MCI classification. Overall, our proposed MCI classification method is effective and promising for automatic diagnosis of MCI in clinical practice.

A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control.

Chronic Fatigue Syndrome (CFS) is a debilitating condition estimated to impact at least 1 million individuals in the United States, however there persists controversy about its existence. Machine learning algorithms have become a powerful methodology for evaluating multi-regional areas of fMRI activation that can classify disease phenotype from sedentary control. Uncovering objective biomarkers such as an fMRI pattern is important for lending credibility to diagnosis of CFS. fMRI scans were evaluated for 69 patients (38 CFS and 31 Control) taken before (Day 1) and after (Day 2) a submaximal exercise test while undergoing the n-back memory paradigm. A predictive model was created by grouping fMRI voxels into the Automated Anatomical Labeling (AAL) atlas, splitting the data into a training and testing dataset, and feeding these inputs into a logistic regression to evaluate differences between CFS and control. Model results were cross-validated 10 times to ensure accuracy. Model results were able to differentiate CFS from sedentary controls at a 80% accuracy on Day 1 and 76% accuracy on Day 2 (Table 3). Recursive features selection identified 29 ROI's that significantly distinguished CFS from control on Day 1 and 28 ROI's on Day 2 with 10 regions of overlap shared with Day 1 (Figure 3). These 10 shared regions included the putamen, inferior frontal gyrus, orbital (F3O), supramarginal gyrus (SMG), temporal pole; superior temporal gyrus (T1P) and caudate ROIs. This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. This pattern provides a first step toward developing fMRI as a diagnostic biomarker and suggests this methodology could be emulated for other disorders. We concluded that a logistic regression model performed on fMRI data significantly differentiated CFS from Control.

Aberrant Thalamic-Centered Functional Connectivity in Patients with Persistent Somatoform Pain Disorder.

PurposeRecent task-based fMRI studies have shown that Persistent Somatoform Pain Disorder (PSPD) patients demonstrated aberrant activity in a wide range of brain regions associated with sensation, cognition and emotion. However, these specific task-based studies could not clearly uncover the alterations in the spontaneous brain networks that were associated with the general pain-related symptoms in PSPD.Patients and MethodsIn the present study, 13 PSPD patients and 23 matched healthy controls (HCs) were enrolled. Resting state and 3D structural imaging data were collected during magnetic resonance imaging (MRI) scans. Ninety regions of interest (ROIs) were selected from the automated anatomical labeling (AAL) template. The functional connectivity toolbox “CONN” was used to calculate the functional connectivity (FC) coefficients.ResultsOur results showed that PSPD patients exhibited increased FCs between the left thalamus and the right amygdala, the right hippocampus, and multiple sub-regions of the occipital lobe when compared to HCs. Correlation analysis revealed a negative correlation between the left thalamus-right amygdala FC and the level of anxiety in PSPD patients.ConclusionThese findings suggest that the altered FC between thalamus and amygdala may be the neural mechanisms underlying the pain-related anxiety in PSPD.

Visual Analysis of Epilepsy Diagnosis Based on Brain Functional Connections

Epilepsy is a transient neurological disorder associated with changes in the functional connections of the brain. Abnormal electrical discharges can be observed during an epileptic seizure. However, in the absence of an epileptic seizure, the anatomical structure of the brain and the electrical waves of the brain are not observed, making it difficult to explain the cause. This paper deals with together weighted imaging (DWI) sequence data in functional magnetic resonance imaging (FMRI) of epileptic patients before seizure, using Anatomical Automatic Labeling (AAL) template extracted 116 brain regions and the introduction of time series, a matrix of 116 × 116. Pearson correlation coefficient was calculated to investigate the pathological condition of brain function in epilepsy patients, using of neural network visualization system of innovative visual display and compared with the normal epileptic brain function to connect the image, with 38 cases of epilepsy by 187 cases of normal DWI experiment data, and can confirm the existence of brain function in patients with epilepsy connections. Cerebral neural network visualization system showed partial functional connection loss between frontal lobe and temporal lobe in epileptic group compared with normal control group.

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment.

Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer’s disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

Metastable Resting State Brain Dynamics.

Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation—BOLD—signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.

Distributed Global Functional Connectivity Networks Predict Responsiveness to L-DOPA and Subthalamic Deep Brain Stimulation

Abstract INTRODUCTION Brain circuit dysfunction in Parkinson's disease (PD) involves an extensive global network. A distinctive basal ganglia resting-state functional connectivity (rsFC) pattern has been linked with the ranked response to L-DOPA. We therefore sought to investigate global rsFC patterns associated with response to L-DOPA and to subthalamic nucleus (STN) deep brain stimulation (DBS) in patients with advanced PD. METHODS A total of 19 patients underwent 3-Tesla resting-state functional magnetic resonance imaging (rsfMRI) in the ON-medication state prior to STN DBS. Improvement in UPDRS-III hemibody scores were assessed following L-DOPA therapy and STN DBS. Global rsFC was measured between regions-of-interest (ROIs) defined by the Automated Anatomical Labeling (AAL) atlas and the Montreal Neurologic Institute (MNI) PD25 subcortical atlas. Seed- and network-level correlations were made with an FDR-P &lt; .005. Graph theoretical analysis was performed with an analysis threshold of FDR-P &lt; .005; and then looking at the top 15% of edges. RESULTS Response to L-DOPA and to DBS displayed cerebellar desynchronization with bilateral thalami and synchronization with bilateral ventromedial prefrontal cortices (vmPFC). L-DOPA response was additionally associated with desynchronization between the vmPFC and the fusiform gyrus. Meanwhile, DBS response was associated with more widespread areas, which have been implicated in visuomotor control and planning. Graph theory analysis revealed that DBS response was inversely related to global efficiency of the thalamus and putamen bilaterally. No significant graph metrics were found relative to L-DOPA response. CONCLUSION Response to DBS and to L-DOPA share similar characteristics, particularly in cerebello-thalamo-cortical circuits, including those that play a role in planning, learning, decision-making, and reward-based behavior. Preservation of distributed networks involved in visuomotor control and network integration of striatothalamocortical circuits appear to predict DBS response. These findings shed a light on the mechanism of action of DBS and L-DOPA and may help serve as useful treatment response biomarkers.

Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus.

Left-handers show lower asymmetry in manual ability when compared to right-handers. Unlike right-handers, left-handers do not show larger deactivation of the ipsilateral primary sensorimotor (SM1) cortex on functional magnetic resonance imaging when moving their dominant than their nondominant hand. However, it should be noted that morphometric MRI studies have reported no differences between right-handers and left-handers in volume, thickness, or surface area of the SM1 cortex. In this regard, magnetization transfer (MT) imaging is a technique with the potential to provide information on microstructural organization and macromolecular content of tissue. In particular, MT ratio index of the brain gray matter is assumed to reflect the variable content of afferent or efferent myelinated fibers, with higher MT ratio values being associated with increased fibers number or degree of myelination. The aim of this study was hence to assess, for the first time, through quantitative MT ratio measurements, potential differences in microstructural organization/characteristics of SM1 cortex between left- and right-handers, which could underlay handedness side. Nine left-handed and 9 right-handed healthy subjects, as determined by the Edinburgh handedness inventory, were examined with T1-weighted and MT-weighted imaging on a 3 T scanner. The hands of subjects were kept still during all acquisitions. Using FreeSurfer suite and the automatic anatomical labeling parcellations defined by the Destrieux atlas, we measured MT ratio, as well as cortical thickness, in three regions of interests corresponding to the precentral gyrus, the central sulcus, and the postcentral gyrus in the bilateral SM1 cortex. No significant difference between left- and right-handers was revealed in the thickness of the three partitions of the SM1 cortex. However, left-handers showed a significantly (p = 0.007) lower MT ratio (31.92% ± 0.96%) in the right SM1 central sulcus (i.e., the hand representation area for left-handers) as compared to right-handers (33.28% ± 0.94%). The results of this preliminary study indicate that quantitative MT imaging, unlike conventional morphometric MRI measurements, can be a useful tool to reveal, in SM1 cortex, potential microstructural correlates of handedness side.

Altered Global Synchronizations in Patients With Parkinson's Disease: A Resting-State fMRI Study.

Background: Abnormalities of cognitive and movement functions are widely reported in Parkinson’s disease (PD). The mechanisms therein are complicated and assumed to a coordination of various brain regions. This study explored the alterations of global synchronizations of brain activities and investigated the neural correlations of cognitive and movement function in PD patients.Methods: Thirty-five age-matched patients with PD and 35 normal controls (NC) were enrolled in resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Degree centrality (DC) was calculated to measure the global synchronizations of brain activity for two groups. Neural correlations between DC and cognitive function Frontal Assessment Battery (FAB), as well as movement function Unified Parkinson’s Disease Rating Scale (UPDRS-III), were examined across the whole brain within Anatomical Automatic Labeling (AAL) templates.Results: In the PD group, increased DC was observed in left fusiform gyrus extending to inferior temporal gyrus, left middle temporal gyrus (MTG) and angular gyrus, while it was decreased in right inferior opercular-frontal gyrus extending to superior temporal gyrus (STG). The DC in a significant region of the fusiform gyrus was positively correlated with UPDRS-III scores in PD (r = 0.41, p = 0.0145). Higher FAB scores were shown in NC than PD (p < 0.0001). Correlative analysis of PD between DC and FAB showed negative results (p < 0.05) in frontal cortex, whereas positive in insula and cerebellum. As for the correlations between DC and UPDRS-III, negative correlation (p < 0.05) was observed in bilateral inferior parietal lobule (IPL) and right cerebellum, whereas positive correlation (p < 0.05) in bilateral hippocampus and para-hippocampus gyrus (p < 0.01).Conclusion: The altered global synchronizations revealed altered cognitive and movement functions in PD. The findings suggested that the global functional connectivity in fusiform gyrus, cerebellum and hippocampus gyrus are critical regions in the identification of cognitive and movement functions in PD. This study provides new insights on the interactions among global coordination of brain activity, cognitive and movement functions in PD.

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma

BackgroundThe purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic.MethodsA DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject’s DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier.ResultsFor FA, the accuracy of classification between the 0–6, 6–12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0–6, 6–12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced.ConclusionsRBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.

Functional Magnetic Resonance Imaging Activation Optimization in the Setting of Brain Tumor-Induced Neurovascular Uncoupling Using Resting-State Blood Oxygen Level-Dependent Amplitude of Low Frequency Fluctuations.

The goal of this study was to demonstrate that a novel resting state BOLD ALFF (amplitude of low frequency fluctuations)-based correction method can substantially enhance the detectability of motor task activation in the presence of tumor-induced neurovascular uncoupling (NVU). Twelve de novo brain tumor patients who underwent comprehensive clinical BOLD fMRI exams including task fMRI and resting state fMRI (rsfMRI) were evaluated. Each patient displayed decreased/absent task fMRI activation in the ipsilesional primary motor cortex in the absence of corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from general linear model (GLM) analysis (reflecting motor activation vs. rest). ALFF maps were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and ipsilesional (IL) hemispheres were parcellated using an Automated Anatomical Labeling (AAL) template for each patient. A novel ALFF-based correction method was used to identify the NVU affected voxels in the ipsilesional primary motor cortex (PMC), and a correction factor was applied to normalize the baseline Z-scores for these voxels. In all cases, substantially greater activation was seen on post-ALFF correction motor activation maps within the ipsilesional precentral gyri than in the pre-ALFF correction activation maps. We have demonstrated the feasibility of a new resting state ALFF-based technique for effective correction of brain tumor-related NVU in the primary motor cortex.

Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity.

Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.

Variability of Resting-State Functional MRI Graph Theory Metrics across 3T Platforms.

Graph theory analysis of brain connectivity data is a promising tool for studying the function of the healthy and diseased brain. The consistency of resting-state functional MRI (rsfMRI) connectivity measures across multiple scanner types is an important factor in designing multi-institutional research studies and has important implications for the potential use of this technique in a heterogeneous clinical setting. We sought to quantitatively study the interscanner variability of rsfMRI graph theory metrics obtained from healthy volunteers scanned on three different scanner platforms. In this prospective Institutional Review Board approved study, 9 healthy volunteers were enrolled for brain MRI on three 3T scanners (Magnetom Prisma, Skyra, and Trio, Siemens, Erlangen, Germany) in three separate scan sessions within approximately 1 week. Standard preprocessing of rsfMRI was performed with SPM12. Subject scans were normalized to Montreal Neurologic Institute (MNI) space, and connectivity of 116 regions-of-interests based on the automated anatomic labeling (AAL) atlas was calculated using Conn toolbox. Whole-network graph theory metrics were calculated using Brain Connectivity Toolbox, and intraclass correlation (ICC) across three scan sessions was assessed. A total of 25 rsfMRI exams were completed in 9 subjects with a median-intersession time of 3 days. Among all three sessions, there was good to excellent agreement in characteristic path length and global efficiency (ICC: .79, .79) and good agreement in the transitivity, local efficiency, and clustering coefficient (ICC=.72, .69, .62). There was high consistency of graph theory metrics of rsfMRI connectivity networks among healthy volunteers scanned on three different generation 3T MRI scanners.

Aberrant resting-state oscillatory brain activity in Parkinson's disease patients with visual hallucinations: An MEG source-space study.

To gain insight into possible underlying mechanism(s) of visual hallucinations (VH) in Parkinson's disease (PD), we explored changes in local oscillatory activity in different frequency bands with source-space magnetoencephalography (MEG). Eyes-closed resting-state MEG recordings were obtained from 20 PD patients with hallucinations (Hall+) and 20 PD patients without hallucinations (Hall-), matched for age, gender and disease severity. The Hall+ group was subdivided into 10 patients with VH only (unimodal Hall+) and 10 patients with multimodal hallucinations (multimodal Hall+). Subsequently, neuronal activity at source-level was reconstructed using an atlas-based beamforming approach resulting in source-space time series for 78 cortical and 12 subcortical regions of interest in the automated anatomical labeling (AAL) atlas. Peak frequency (PF) and relative power in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) were compared between Hall+ and Hall-, unimodal Hall+ and Hall-, multimodal Hall+ and Hall-, and unimodal Hall+ and multimodal Hall+ patients. PF and relative power per frequency band did not differ between Hall+ and Hall-, and multimodal Hall+ and Hall- patients. Compared to the Hall- group, unimodal Hall+ patients showed significantly higher relative power in the theta band (p = 0.005), and significantly lower relative power in the beta (p = 0.029) and gamma (p = 0.007) band, and lower PF (p = 0.011). Compared to the unimodal Hall+, multimodal Hall+ showed significantly higher PF (p = 0.007). In conclusion, a subset of PD patients with only VH showed slowing of MEG-based resting-state brain activity with an increase in theta activity, and a concomitant decrease in beta and gamma activity, which could indicate central cholinergic dysfunction as underlying mechanism of VH in PD. This signature was absent in PD patients with multimodal hallucinations.

Hierarchical Feature Extraction for Early Alzheimer’s Disease Diagnosis

Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). In this paper, we propose a novel voxel-based hierarchical feature extraction (VHFE) method for the early AD diagnosis. First, we parcellate the whole brain into 90 regions of interests (ROIs) based on an automated anatomical labeling (AAL) template. To split the uninformative data, we select the informative voxels in each ROI with a baseline of their values and arrange them into a vector. Then, the first stage features are selected based on the correlation of the voxels between different groups. Next, the brain feature maps of each subject made up of the fetched voxels are fed into a convolutional neural network (CNN) to learn the deeply hidden features. Finally, to validate the effectiveness of the proposed method, we test it with the subset of the AD neuroimaging (ADNI) database. The testing results demonstrate that the proposed method is robust with a promising performance in comparison with the state-of-the-art methods.