KEYWORDS: Magnetic resonance imaging, Electric fields, Magnetism, Data modeling, Neuroimaging, Matrices, Education and training, Machine learning, Brain, Muscles
Transcranial magnetic stimulation (TMS) stands as a widely employed neuromodulation technique for addressing various brain disorders. The hand motor hotspot (hMHS) holds particular significance in TMS applications, serving to ascertain personalized treatment targets and stimulation intensity. However, the conventional determination of hMHS remains timeconsuming. Our objective was to expedite the identification of hMHS solely based on neuroimaging data. In this investigation, we pinpointed the hMHS on the cortical surface in depressed patients utilizing TMS-derived data and magnetic resonance imaging (MRI) data. Employing the kernel density estimation method, we developed a probability map for hMHS, subsequently utilizing a machine learning (ML) model to discern subjects suitable for the probability map application. The hMHS probability map was established, and the vertex with the highest probability was designated as the group hMHS. For subjects closely aligning with the group hMHS, direct application of the hMHS probability map was feasible. Conversely, for other subjects, our ML model, trained on cortical structure data of the sulci, could identify them. Our method achieved an 88% accuracy in hMHS determination and, when compared to traditional methods, exhibited an average time-saving of approximately 50%. In conclusion, our proposed method offers an efficient and rapid solution for hMHS identification during TMS treatment.
Numerous studies have demonstrated substantial inter-individual symptom heterogeneity among patients with schizophrenia, which seriously affects the quantification of diagnosis and treatment schema. Normative model is a statistical model offering quantitative measurements of abnormal deviations under interindividual heterogeneity. Here, we explored the individual-specific associations among morphologic deviations from normative ranges of brain structure and specific symptomatology structure on three different dimensions without the effect of general disease effects. Specifically, we employed an exploratory bi-factor model for the PANSS scale and built normative models for two cortical measurements: cortical area and thickness. Significant correlations among different cortical measurements and latent symptom groups were observed, which could provide evidence to understand the pathophysiology of schizophrenia symptoms.
Brain functional activity involves complex cellular, metabolic, and vascular chain reactions, making it difficult to comprehend. Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) have been combined into a multimodal neuroimaging method that captures both electrophysiological and hemodynamic information to explore the spatiotemporal characteristics of brain activity. Because of the significance of visually evoked functional activity in clinical applications, numerous studies have explored the amplitude of the visual evoked potential (VEP) to clarify its relationship with the hemodynamic response. However, relatively few studies have investigated the influence of latency, which has been frequently used to diagnose visual diseases, on the hemodynamic response. Moreover, because the latency and the amplitude of VEPs have different roles in coding visual information, investigating the relationship between latency and the hemodynamic response should be helpful. In this study, checkerboard reversal tasks with graded contrasts were used to evoke visual functional activity. Both EEG and fNIRS were employed to investigate the relationship between neuronal electrophysiological activities and the hemodynamic responses. The VEP amplitudes were linearly correlated with the hemodynamic response, but the VEP latency showed a negative linear correlation with the hemodynamic response.
Cerebral oximeters measure continuous cerebral oxygen saturation using near-infrared spectroscopy (NIRS) technology noninvasively. It has been involved into operating room setting to monitor oxygenation within patient’s brain when surgeons are concerned that a patient’s levels might drop. Recently, cerebral oxygen saturation has also been related with chronic cerebral vascular insufficiency (CCVI). Patients with CCVI would be benefited if there would be a wearable system to measure their cerebral oxygen saturation in need. However, there has yet to be a wearable wireless cerebral oximeter to measure the saturation in 24 hours. So we proposed to develop the wearable wireless cerebral oximeter. The mechanism of the system follows the NIRS technology. Emitted light at wavelengths of 740nm and 860nm are sent from the light source penetrating the skull and cerebrum, and the light detector(s) receives the light not absorbed during the light pathway through the skull and cerebrum. The amount of oxygen absorbed within the brain is the difference between the amount of light sent out and received by the probe, which can be used to calculate the percentage of oxygen saturation.
In the system, it has one source and four detectors. The source, located in the middle of forehead, can emit two near infrared light, 740nm and 860nm. Two detectors are arranged in one side in 2 centimeters and 3 centimeters from the source. Their measurements are used to calculate the saturation in the cerebral cortex. The system has included the rechargeable lithium battery and Bluetooth smart wireless micro-computer unit.
The cognitive deficits of schizophrenia are largely resistant to current treatment, and are thus a life-long burden to patients. The MATRICS consensus cognitive battery (MCCB) provides a reliable and valid assessment of cognition across a comprehensive set of cognitive domains for schizophrenia. In resting-state fMRI, functional connectivity associated with MCCB has not yet been examined. In this paper, the interrelationships between MCCB and the abnormalities seen in two types of functional measures from resting-state fMRI—fractional amplitude of low frequency fluctuations (fALFF) and functional network connectivity (FNC) maps were investigated in data from 47 schizophrenia patients and 50 age-matched healthy controls. First, the fALFF maps were generated and decomposed by independent component analysis (ICA), and then the component showing the highest correlation with MCCB composite scores was selected. Second, the whole brain was separated into functional networks by group ICA, and the FNC maps were calculated. The FNC strengths with most significant correlations with MCCB were displayed and spatially overlapped with the fALFF component of interest. It demonstrated increased cognitive performance associated with higher fALFF values (intensity of regional spontaneous brain activity) in prefrontal regions, inferior parietal lobe (IPL) but lower ALFF values in thalamus, striatum, and superior temporal gyrus (STG). Interestingly, the FNC showing significant correlations with MCCB were in well agreement with the activated regions with highest z-values in fALFF component. Our results support the view that functional deficits in distributed cortico-striato-thalamic circuits and inferior parietal lobe may account for several aspects of cognitive impairment in schizophrenia.
KEYWORDS: Hemodynamics, Brain, Computing systems, Demodulation, Sensors, Near infrared, Signal detection, Channel projecting optics, Near infrared spectroscopy, Signal generators
Abundant study on the hemodynamic response of a brain have brought quite a few advances in technologies of measuring it. The most benefitted is the functional near infrared spectroscope (fNIRS). A variety of devices have been developed for different applications. Because portable fNIRS systems were more competent to measure responses either of special subjects or in natural environment, several kinds of portable fNIRS systems have been reported. However, they all required a computer for receiving data. The extra computer increases the cost of a fNIRS system. What’s more noticeable is the space required to locate the computer even for a portable system. It will discount the portability of the fNIRS system. So we designed a self-contained eight channel fNIRS system, which does not demand a computer to receive data and display data in a monitor. Instead, the system is centered by an ARM core CPU, which takes charge in organizing data and saving data, and then displays data on a touch screen. The system has also been validated by experiments on phantoms and on subjects in tasks.
Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.
A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.
Multi-atlas based segmentation methods have recently attracted much attention in medical image segmentation. The
multi-atlas based segmentation methods typically consist of three steps, including image registration, label propagation,
and label fusion. Most of the recent studies devote to improving the label fusion step and adopt a typical image
registration method for registering atlases to the target image. However, the existing registration methods may become
unstable when poor image quality or high anatomical variance between registered image pairs involved. In this paper, we
propose an iterative image segmentation and registration procedure to simultaneously improve the registration and
segmentation performance in the multi-atlas based segmentation framework. Particularly, a two-channel registration
method is adopted with one channel driven by appearance similarity between the atlas image and the target image and
the other channel optimized by similarity between atlas label and the segmentation of the target image. The image
segmentation is performed by fusing labels of multiple atlases. The validation of our method on hippocampus
segmentation of 30 subjects containing MR images with both 1.5T and 3.0T field strength has demonstrated that our
method can significantly improve the segmentation performance with different fusion strategies and obtain segmentation
results with Dice overlap of 0.892±0.024 for 1.5T images and 0.902±0.022 for 3.0T images to manual segmentations.
For subcortical structure segmentation, multi-atlas based segmentation methods have attracted great interest due to their
competitive performance. Under this framework, using deformation fields generated for registering atlas images to the
target image, labels of the atlases are first propagated to the target image space and further fused somehow to get the
target segmentation. Many label fusion strategies have been proposed and most of them adopt predefined weighting
models which are not necessarily optimal. In this paper, we propose a local label learning (L3) strategy to estimate the
target image's label using statistical machine learning techniques. Specifically, we use Support Vector Machine (SVM)
to learn a classifier for each of the target image voxels using its neighboring voxels in the atlases as a training dataset.
Each training sample has dozens of image features extracted around its neighborhood and these features are optimally
combined by the SVM learning method to classify the target voxel. The key contribution of this method is the
development of a locally specific classifier for each target voxel based on informative texture features. The validation
experiment on 57 MR images has demonstrated that our method generates segmentation results of hippocampal with a
dice overlap of 0.908±0.023 to manual segmentations, statistically significantly better than state-of-the-art segmentation
algorithms.
In functional neuroimaging studies, the inter-subject alignment of functional magnetic resonance imaging (fMRI) data is
a necessary precursor to improve functional consistency across subjects. Traditional structural MRI based registration
methods cannot achieve accurate inter-subject functional consistency in that functional units are not necessarily
consistently located relative to anatomical structures due to functional variability across subjects. Although spatial
smoothing commonly used in fMRI data preprocessing can reduce the inter-subject functional variability, it may blur the
functional signals and thus lose the fine-grained information. In this paper we propose a novel functional signal based
fMRI image registration method which aligns local functional connectivity patterns of different subjects to improve the
inter-subject functional consistency. Particularly, the functional connectivity is measured using Pearson correlation. For
each voxel of an fMRI image, its functional connectivity to every voxel in its local spatial neighborhood, referred to as
its local functional connectivity pattern, is characterized by a rotation and shift invariant representation. Based on this
representation, the spatial registration of two fMRI images is achieved by minimizing the difference between their
corresponding voxels' local functional connectivity patterns using a deformable image registration model. Experiment
results based on simulated fMRI data have demonstrated that the proposed method is more robust and reliable than the
existing fMRI image registration methods, including maximizing functional correlations and minimizing difference of
global connectivity matrices across different subjects. Experiment results based on real resting-state fMRI data have
further demonstrated that the proposed fMRI registration method can statistically significantly improve functional
consistency across subjects.
The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated
informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a
novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The
functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity
pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity
patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace
distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed
to select independent components for constructing the most discriminative functional connectivity pattern. The
discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and
31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising
classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies
discriminative functional networks that are informative for schizophrenia diagnosis.
In original mammographic images obtained by X-ray radiography, only a small part of detected information is displayed
to the human observer. A method aimed at minimizing image noise while optimizing contrast of mammographic image
features is presented in this paper, for more accurate detection of microcalcification clusters. The method is based on the
contourlet transform, which is a multiresolution, local and directional image representation. The difference from wavelet
and other multiscale expansion lies in that the contourlet transform is constructed by using non-separable filter banks in
discrete-domain, thus it can effectively capture important features of images. The enhancement procedure consists of two
steps: noise filtering by the Stein's thresholding and denoised contourlet coefficients modification via a nonlinear
mapping function. The experimental results have shown an improved visualization of significant mammographic features
by the proposed method. A comparison with other enhancement algorithms is also discussed by employing a measure
named target to background contrast ratio using variance.
Diffuse optical tomography (DOT) is an appropriate tool for non-invasive exploration of human brain activation. The
activation of sensorimotor cortex has been studied by several researchers since the first images of human brain were
generated in 1995. However, high-quality images of sensorimotor cortex can not be obtained until the emerging of
high-resolution DOT which uses a multi-centered geometry for arranging optical fibers. In this study, we did two
experiments using our CW5 instrument (TechEn, USA). In the first experiment, the subject was asked to move his four
fingers of right hand for 30 seconds, followed by 30 seconds of rest. Data collection lasted 420 seconds. In the second
experiment, the subject was asked to tap his thumb against the other four fingers. Two conclusions can be reached from
the experiments. Firstly, larger activated regions can be found on motor cortex in experiment 2 than in experiment 1.
This indicates that high-resolution DOT can detect larger activated brain region when moving five fingers comparing to
moving four fingers. Secondly, few activated regions can be found on sensory cortex in experiment 1, but it can be
clearly found on sensory cortex in experiment 2. Up to our knowledge, it is the first time DOT has detected activated
region on sensory cortex during motorial task.
Functional near-infrared spectroscopy (fNIRS) has been used to investigate the changes in the concentration of
oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin in brain issue during several cognitive tasks. In the present
study, by means of multichannel dual wavelength light-emitting diode continuous-wave (CW) NIRS, we investigated the
blood oxygenation changes of prefrontal cortex in 18 healthy subjects while performing a verbal n-back task (0-back and
2-back), which has been rarely investigated by fNIRS. Compared to the 0-back task (control task), we found a significant
increase of O2Hb and total amount of hemoglobin (THb) in left and right ventrolateral prefrontal cortex (VLPFC) during
the execution of the 2-back task compared to the 0-back task (p<0.05, FDR corrected). This result is consistent with the
previous functional neuroimaging studies that have found the VLPFC activation related to verbal working memory.
However, we found no significant hemisphere dominance. In addition, the effects of gender and its interaction with task
performance on O2Hb concentration change were suggested in the present study. Our findings not only confirm that
multichannel fNIRS is suitable to detect spatially specific activation during the performance of cognitive tasks; but also
suggest that it should be cautious of gender-dependent difference in cerebral activation when interpreting the fNIRS data
during cognitive tasks.
We propose a novel multicentered mode for arrangement of optical fibers to improve the imaging performance of reflectance diffuse optical imaging (rDOI). Simulations performed using a semi-infinite model show that the proposed multicentered geometries can achieve a maximum of 42 overlapping measurements. The contrast-to-noise ratio (CNR) analysis indicates that the best spatial resolution is 1 mm in radius and the contrast resolution is less than 1.05 for the multicentered geometries. The results from simulations indicate significant improvement in image quality compared to the single-centered mode and previous geometries. Additional experimental results on a single human subject lead to the conclusion that the proposed multicentered geometries are appropriate for exploring activations in the human brain. From the results of this research, we conclude that the proposed multicentered mode could advance the performance of rDOI both in image quality and practical convenience.
Both flat-panel detectors and cylindrical detectors have been used in CT systems for data acquisition. The cylindrical detector generally offers a sampling of a transverse image plane more uniformly than does a flat-panel detector. However, in the longitudinal dimension, the cylindrical and flat-panel detectors offer similar sampling of the image space. In this work, we investigate a detector of spherical shape, which can yield uniform sampling of the 3D image space because the solid angle subtended by each individual detector bin remains unchanged. We have extended the backprojection-filtration (BPF) algorithm, which we have developed previously for cone-beam CT, to reconstruct images in cone-beam CT with a spherical detector. We also conduct computer-simulation studies to validate the extended BPF algorithm. Quantitative results in these numerical studies indicate that accurate images can be obtained from data acquired with a spherical detector by use of our extended BPF cone-beam algorithms.
Statistical Shape Analysis (SSA) is a powerful tool for noninvasive studies of pathophysiology and diagnosis of brain diseases. It also provides a shape constraint for the segmentation of brain structures. There are two key problems in SSA: the representation of shapes and their alignments. The widely used parameterized representations are obtained by preserving angles or areas and the alignments of shapes are achieved by rotating parameter net. However, representations preserving angles or areas do not really guarantee the anatomical correspondence of brain structures. In this paper, we incorporate shape-based landmarks into parameterization of banana-like 3D brain structures to address this problem. Firstly, we get the triangulated surface of the object and extract two landmarks from the mesh, i.e. the ends of the banana-like object. Then the surface is parameterized by creating a continuous and bijective mapping from the surface to a spherical surface based on a heat conduction model. The correspondence of shapes is achieved by mapping the two landmarks to the north and south poles of the sphere and using an extracted origin orientation to select the dateline during parameterization. We apply our approach to the parameterization of lateral ventricle and a multi-resolution shape representation is obtained by using the Discrete Fourier Transform.
A new nonrigid registration method has been developed in this paper. In the proposed algorithm, we use αB-Spline transformation based on free-form deformation model to register images. The αB-Spline not only possesses many desirable geometrical and computational properties as B-spline, but also enhances the shape-control capability. It uses the linear singular blending technique, which is derived from the blending parameters defined at the B-spline control vertices. The volume can be transformed by changing the positions of control vertices and the value of blending parameters. We first use affine transformation to coarsely match two images, then warp the image by altering the positions of the control points, at last, adjust the values of blending parameters to change the image finely. We combine the SSD similarity measure with the regularization of Laplacian model as the cost function. Compared with that of the affine and B-spline transformation, the result of the proposed method is better.
In this work, T1-, T2- and PD-weighted MR images of multiple sclerosis (MS) patients, providing information on the properties of tissues from different aspects, are treated as three independent information sources for the detection and segmentation of MS lesions. Based on information fusion theory, a knowledge guided information fusion framework is proposed to accomplish 3-D segmentation of MS lesions. This framework consists of three parts: (1) information extraction, (2) information fusion, and (3) decision. Information provided by different spectral images is extracted and modeled separately in each spectrum using fuzzy sets, aiming at managing the uncertainty and ambiguity in the images due to noise and partial volume effect. In the second part, the possible fuzzy map of MS lesions in each spectral image is constructed from the extracted information under the guidance of experts' knowledge, and then the final fuzzy map of MS lesions is constructed through the fusion of the fuzzy maps obtained from different spectrum. Finally, 3-D segmentation of MS lesions is derived from the final fuzzy map. Experimental results show that this method is fast and accurate.
KEYWORDS: Brain, Image segmentation, Tissues, Neuroimaging, Magnetic resonance imaging, Image processing algorithms and systems, Detection and tracking algorithms, Data modeling, Image registration, Head
White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.
Extracting features from fingerprints is a crucial step in fingerprint verification and recognition. Here, features are represented as minutiae, such as end points and bifurcation points. Many algorithms for this issue have been developed recently. This paper presents a fingerprint feature extraction method through which minutiae are extracted directly from original gray-level fingerprint images without binarization and thinning. Our algorithm improves the performance of the existing ones along this stream. Our experimental results demonstrate that our approach can achieve better performance in both efficiency and robustness.
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