Ching-Po Lin

Expertise Modulates the Perception of Pain in Others

Perceiving the pain of others activates a large part of the pain matrix in the observer [1]. Because this shared neural representation can lead to empathy or personal distress [2, 3], regulatory mechanisms must operate in people who inflict painful procedures in their practice with patient populations in order to prevent their distress from impairing their ability to be of assistance. In this functional magnetic resonance imaging MRI study, physicians who practice acupuncture were compared to naive participants while observing animated visual stimuli depicting needles being inserted into different body parts, including the mouth region, hands, and feet. Results indicate that the anterior insula somatosensory cortex, periaqueducal gray, and anterior cingulate cortex were significantly activated in the control group, but not in the expert group, who instead showed activation of the medial and superior prefrontal cortices and the temporoparietal junction, involved in emotion regulation and theory of mind.

Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease

Recent research on Alzheimers disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimers brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.

Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data.

Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45 degrees , and down to 30 degrees for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90 degrees, consistent with previous simulation results. Finally, for a 45 degrees crossing, the minimum b-value required to resolve the fibre orientations was 4000 s/mm(2) for QBI, 2000 s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.

Atypical Development of White Matter Microstructure in Adolescents with Autism Spectrum Disorders

Diffusion tensor imaging (DTI) studies in adolescents with autism spectrum disorders (ASD) indicate aberrant neurodevelopment of frontal white matter (WM), potentially underlying abnormal social cognition and communication in ASD. Here, we further use tract-based spatial statistics (TBSS) to examine the developmental change of WM skeleton (i.e., the most compact whole-brain WM) during adolescence in ASD. This whole-brain DTI used TBSS measures fractional anisotropy (FA) and longitudinal and radial diffusivities in fifty adolescents, 25 ASD and 25 controls. Results show that adolescents with ASD versus controls had significantly reduced FA in the right posterior limb of internal capsule (increased radial diffusivity distally and reduced longitudinal diffusivity centrally). Adolescents with ASD versus controls (covarying for age and IQ) had significantly greater FA in the frontal lobe (reduced radial diffusivity), right cingulate gyrus (reduced radial diffusivity), bilateral insula (reduced radial diffusivity and increased longitudinal diffusivity), right superior temporal gyrus (reduced radial diffusivity), and bilateral middle cerebellar peduncle (reduced radial diffusivity). Notably, a significant interaction with age by group was found in the right paracentral lobule and bilateral superior frontal gyrus as indicated by an age-related FA gain in the controls whilst an age-related FA loss in the ASD. To our knowledge, this is the first study to use TBSS to examine WM in individuals with ASD. Our findings indicate that the frontal lobe exhibits abnormal WM microstructure as well as an aberrant neurodevelopment during adolescence in ASD, which support the frontal disconnectivity theory of autism.

The perception of pain in others suppresses somatosensory oscillations: A magnetoencephalography study

Accumulating evidence demonstrates that similar neural circuits are activated during the first-hand experience of pain and the observation of pain in others. However, most functional MRI studies did not detect signal change in the primary somatosensory cortex during pain empathy. To test if the perception of pain in others involves the primary somatosensory cortex, neuromagnetic oscillatory activity was recorded from the primary somatosensory cortex in 16 participants while they observed static pictures depicting body parts in painful and non-painful situations. The left median nerve was stimulated at the wrist, and the poststimulus rebounds of the approximately 10-Hz somatosensory cortical oscillations were quantified. Compared to the baseline condition, the level of the approximately 10-Hz oscillations was suppressed during both of the observational situations, indicating the activation of the primary somatosensory cortex. Importantly, watching painful compared to non-painful situations suppressed somatosensory oscillations to a significant stronger degree. In addition, the suppression caused by perceiving others in the painful relative to the non-painful situations correlated with the perspective taking subscale of the interpersonal reaction index. These results, consistent with the mirror-neuron system, demonstrate that the perception of pain in others modulates neural activity in primary somatosensory cortex and supports the idea that the perception of pain in others elicits subtle somatosensory activity that may be difficult to detect by fMRI techniques.

Validation of Diffusion Tensor Magnetic Resonance Axonal Fiber Imaging with Registered Manganese-Enhanced Optic Tracts

Noninvasive mapping of white matter tracts using diffusion tensor magnetic resonance imaging (DTMRI) is potentially useful in revealing anatomical connectivity in the human brain. However, a gold standard for validating DTMRI in defining axonal fiber orientation is still lacking. This study presents the first validation of the principal eigenvector of the diffusion tensor in defining axonal fiber orientation by superimposing DTMRI with manganese-enhanced MRI of optic tracts. A rat model was developed in which optic tracts were enhanced by manganese ions. Manganese ion (Mn(2+)) is a potent T1-shortening agent and can be uptaken and transported actively along the axon. Based on this property, we obtained enhanced optic tracts with a T1-weighted spin-echo sequence 10 h after intravitreal injection of Mn(2+). The images were compared with DTMRI acquired with exact spatial registration. Deviation angles between tangential vectors of the enhanced tracts and the principal eigenvectors of the diffusion tensor were then computed pixel by pixel. We found that under signal-to-noise (SNR) of 30, the variance of deviation angles was (13.27 degrees). In addition, the dependence of this variance on SNR obeys stochastic behavior if SNR is greater than 10. Based on this relation, we estimated that an rms deviation of less than 10 degrees could be achieved with DTMRI when SNR is 40 or greater. In conclusion, our method bypasses technical difficulties in conventional histological approach and provides an in vivo gold standard for validating DTMRI in mapping white matter tracts.

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimers disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimers disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using knowledge from the existing literature.

Elevated expression of TDP-43 in the forebrain of mice is sufficient to cause neurological and pathological phenotypes mimicking FTLD-U

TDP-43 is a multifunctional DNA/RNA-binding factor that has been implicated in the regulation of neuronal plasticity. TDP-43 has also been identified as the major constituent of the neuronal cytoplasmic inclusions (NCIs) that are characteristic of a range of neurodegenerative diseases, including the frontotemporal lobar degeneration with ubiquitin+ inclusions (FTLD-U) and amyotrophic lateral sclerosis (ALS). We have generated a FTLD-U mouse model (CaMKII-TDP-43 Tg) in which TDP-43 is transgenically overexpressed in the forebrain resulting in phenotypic characteristics mimicking those of FTLD-U. In particular, the transgenic (Tg) mice exhibit impaired learning/memory, progressive motor dysfunction, and hippocampal atrophy. The cognitive and motor impairments are accompanied by reduced levels of the neuronal regulators phospho–extracellular signal-regulated kinase and phosphorylated cAMP response element-binding protein and increased levels of gliosis in the brains of the Tg mice. Moreover, cells with TDP-43+, ubiquitin+ NCIs and TDP-43–deleted nuclei appear in the Tg mouse brains in an age-dependent manner. Our data provide direct evidence that increased levels of TDP-43 protein in the forebrain is sufficient to lead to the formation of TDP-43+, ubiquitin+ NCIs and neurodegeneration. This FTLD-U mouse model should be valuable for the mechanistic analysis of the role of TDP-43 in the pathogenesis of FTLD-U and for the design of effective therapeutic approaches of the disease.

Structural and cognitive deficits in remitting and non-remitting recurrent depression: A voxel-based morphometric study

Remission is the optimal outcome for major depressive disorder (MDD), but many patients do not improve appreciably despite treatment with medication. Treatment-resistant patients may experience deterioration in cognitive functions. Research has reported structural abnormalities in certain brain areas that may contribute to a poor clinical response. We hypothesize that there will be structural differences between patients able to achieve remission and those responding poorly to antidepressants. In the first voxel-based morphometric (VBM) study comparing remitting with non-remitting MDD, we investigated gray matter volume (GMV) differences between depressives to determine which structural abnormalities existed, and correlated these with diminished cognitive functioning. Of 44 adults with recurrent MDD, 19 had full remissions and 25 were non-remitters after a 6-week trial with antidepressant treatment. Remission was defined by 17-item Hamilton Depression Rating Scale scores of </=7 for at least 2 weeks. VBM and neuropsychological studies were conducted on all patients and 25 healthy controls. The patients who remitted revealed milder visual attention deficits than did controls. This correlated with reduced GMV in the left postcentral gyrus (Brodmann area, or BA, 3) and the bilateral medial/superior frontal gyrus (BA 6). The non-remitting patients had reduced GMV in the left dorsolateral prefrontal cortex (DLPFC, BA 9), and impaired acoustic and visual attention associated with GMV differences in several cortical regions, thalamus and amygdala/parahippocampal gyrus. These findings indicated that patients whose MDD remitted were cognitively and morphologically different from non-remitters. Voxel-based structural deficits in the left DLPFC may characterize a subgroup of people with recurrent MDD who respond poorly to antidepressants.

Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography

The function of the corpus callosum (CC) is to distribute perceptual, motor, cognitive, learned, and voluntary information between the two hemispheres of the brain. Accurate parcellation of the CC according to fiber composition and fiber connection is of upmost important. In this work, population‐based probabilistic connection topographies of the CC, in the standard Montreal Neurological Institute (MNI) space, are estimated by incorporating anatomical cytoarchitectural parcellation with high angular resolution diffusion imaging (HARDI) tractography. First, callosal fibers are extracted using multiple fiber assignment by continuous tracking algorithm based on q‐ball imaging (QBI), on 12 healthy and young subjects. Then, the fiber tracts are aligned in the standard MNI coordinate system based on a tract‐based transformation scheme. Next, twenty‐eight Brodmanns areas on the surface of cortical cortex are registered to the MNI space to parcellate the aligned callosal fibers. Finally, the population‐based topological subdivisions of the midsagittal CC to each cortical target are then mapped. And the resulting subdivisions of the CC that connect to the frontal and somatosensory associated cortex are also showed. To our knowledge, it is the first topographic subdivisions of the CC done using HARDI tractography and cytoarchitectonic information. In conclusion, this sophisticated topography of the CC may serve as a landmark to further understand the correlations between the CC, brain intercommunication, and functional cytoarchitectures. Hum Brain Mapp 2009.