A Functional Magnetic Resonance Imaging. Study. Motor imagery (MI) may be defined as the mental rehearsal of simple or complex motor acts that is not accompanied by overt. Two kinds of mental representations of motor acts can be generated by normal subjects: an “internal” or first- person. Mahoney and Avener, 1. Unlike visual imagery (VI), internal (or kinesthetic) MI is difficult to describe verbally. Nevertheless, it is usually. Decety et al., 1. Wang and Morgan, 1. This issue has been addressed using a variety of brain mapping techniques. Early studies investigating regional cerebral. PET) techniques demonstrated the activation during. MI of different motor- related regions, such as the supplementary motor area, lateral frontal (premotor) areas, and cerebellum. Ingvar and Philipson, 1. Roland et al., 1. Fox et al., 1. 98. Decety et al., 1. Moreover, in the study by Stephan et al., only two of six subjects showed activated foci in or near the anterior bank of. Brodmann area 4) in humans (Roland and Zilles, 1. Geyer et al., 1. 99. Primary sensorimotor cortex activation during MI has been denied by two early f. MRI works (Rao et al., 1. Sanes et al., 1. 99. Leonardo et al., 1. Sabbah et al., 1. A Functional Magnetic Resonance Imaging Study. Functional MRI of human brain activation at. The only CPT that is both QEEG and fMRI validated. No claims are made for any specific or general benefits for any one individual using these software tools. Clusters were identified by eye as groups. Centers of gravity of fMRI activation clusters and of connectivity. We used the Brain Voyager QX software (Brain Innovation. We calibrated the eye tracker before starting each fMRI run. This study was supported by the program of Grant-in-Aid for Scientific Research (A). These studies, however, did not provide a quantitative assessment of the relative contribution of precentral and postcentral. The results suggest that overlapping neural networks in the. All gave their informed. Handedness was. assessed using a short questionnaire based on the Edinburgh scale (Oldfield, 1. The experiments were performed using a Magnetom SP 4. Siemens, Erlangen, Germany) superconducting 1. Functional MRI studies on sensory guided eye movements have detected activation in the precentral.Eye movements exceeding 1–2. To analyze fMRI data. It has been shown that individuals with autism spectrum disorders. Eye tracking methodology provides a direct assessment of. Real-time fMRI neurofeedback: Progress and. EPI images are extracted from the MR scanner online, analyzed by third-party software. Learned regulation of spatially localized brain activation using real-time fMRI. T whole- body magnetic. MR) system equipped with a standard circularly polarized head coil. Head motion was minimized by an adjustable. Field homogeneity was adjusted by a global shimming procedure for each subject, with line widths being. To locate the precentral and postcentral gyri, multislice. T1- weighted spin- echo sagittal, axial, and coronal images . Two adjacent oblique (axial to sagittal) planes were then defined along the central sulcus. Talairach and Tournoux, 1. FMRI Activation in the Human Frontal Eye Field Is Correlated. Marino for creating the saccade marking program. Functional magnetic resonance imaging or functional MRI (fMRI) is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting changes associated with blood flow. This technique relies on. Colebatch et al., 1. Grafton et al., 1. Rumeau et al., 1. The anatomy of these oblique planes was shown on. T1- weighted images . The more superficial plane covered. For the activation studies, images from the same planes were acquired using a gradient- echo. FLASH) sequence (TR/TE = 9. The FLASH images were interpolated. Total scan time for one slice. VI was taken as a reference state to. Subjects were instructed to perform the motor sequence at a constant. The order of finger- tapping was simply 2- 3- 4- 5- 2- 3- 4- 5. They were requested to scan the mental scene and to focus on particular objects but not to imagine themselves. It was emphasized that during the MI and VI tasks, the arms had to remain still with the right. All of them reported being able to execute the tasks, although some judged it quite. MI or VI) over the whole experimental period. Hand movements. were monitored throughout the experiments using a video camera. Dynamic data sets were acquired in blocks of four images. Subjects started the execution of the next task immediately. The functional sequence, however, started 1. Kwong et al., 1. 99. Bandettini et al., 1. Friston et al., 1. In a second group. VI and MI tasks. The task order was MP–Rest–MI–VI. A total of 3. 6 (4. Image analyses were performed using a Silicon Graphics Indy workstation. For each study and each anatomical image, three regions. ROIs) were identified and manually outlined by one investigator and independently confirmed by another: the postcentral. Post. CG) and the anterior (Pre. CGAnt) and posterior (Pre. CGPost) portions of the precentral gyrus. In the superficial. Pre. CGAnt and Pre. CGPost was traced at approximately 1/3 the distance between the central sulcus. In the deeper plane, the same boundary corresponded typically. Thus, Pre. CGPost included the anterior bank and crown of the central sulcus. As a first approximation, Post. CG corresponds to. Brodmann areas 3, 1, 2), Pre. CGPost to the “hand region” of the primary. Pre. CGAnt to the adjacent portion of the lateral premotor cortex (area 6). The number of pixels. ROIs (mean . Care was taken to exclude pixels associated with structures other than nerve tissue, such as visible. Briefly, registration was accomplished by minimizing the error functional. E(u,v,h) = . After finding the (u,v,h) that minimizes the error functional, I(x,y) was transformed with. T(. Then the minimization was repeated; that is, simple descent gradient was used to minimize E. The procedure was first performed with a. J(x,y) that has been smoothed with a Gaussian filter with full- width at half- maximum (FWHM) equal to 4 pixels. After minimizing. J(x,y) smoothed with an FWHM = 1 pixel. Even if subtle artifacts caused by head motion during single- image acquisition. A preliminary analysis performed on the same set of images showed that the number of activated pixels. MP was not significantly different using the present registration algorithm or that described by Woods et al. In each case, the “base” functional image was acquired immediately after the. T1- weighted anatomical image of the same plane. The boundaries of the three ROIs identified on anatomical images were automatically. ROIs. Mean values of signal intensity of all pixels (in the superficial and deep planes) lying within the. ROIs, or of pixel populations selected by correlation analyses (see below), were then calculated and analyzed by repeated- measures. ANOVA, using the SPSS for Windows software package. To ascertain potential variations of task- related neural activation over. Pre. CGAnt, Pre. CGPost. Post. CG) and task (MP, MI, VI and rest in experiment 2) as within- subject factors. The Mauchly test of sphericity was used. If the sphericity assumption appeared to be violated (p < 0. Greenhouse–Geisser . Whenever first- level tests. Helmert) contrasts were applied to assess differences between variable levels. A value. of p < 0. A correlation coefficient of 0. Because the highest number of statistical comparisons in the subject displaying. ROIs was 1. 1,4. 96 (3. ROIs . Preliminary analyses, done on 3. ROI . For each subject and each ROI, the occurrence of at least 4 pixels. To identify sites of activation. MRI scans were resized into the anatomical space of the atlas of Talairach and Tournoux (1. The Talairach coordinates of the centers of mass of the activated pixels within each ROI were then calculated using software. AFNI) developed by Robert Cox. EMG recordings were not performed during the f. MRI study because of technical limitations. In a separate experimental session. MR equipment, EMG activity was recorded in each volunteer from surface electrodes overlaying the thenar. Subjects performed the three tasks (VI, MI. MP) in the same order as during the f. MRI experiment 1, and each recording session lasted . After conventional rectification. EMG data, integral values of EMG activity over successive 4 sec periods were calculated in. ANOVA. Although no subject made overt movements of the right arm during MI, a mild increase. EMG activity during MI relative to the control condition (VI) was observed in 4 of 1. However, the integrated. EMG data were not significantly affected by MI in the whole population at thenar (F = 0. F = 0. 0. 5,p > 0. The rate and amplitude of hand movements. MP task showed little intraindividual and interindividual variations, as judged independently by two investigators. A four- way, repeated- measures ANOVA (region. MI was associated with significantly higher values (t = 2. VI. Moreover, images obtained during MP were characterized by higher values than both of the other conditions. No significant difference was found for data obtained in different blocks or for different images within a block. In all regions, f. MRI signals were significantly modulated by task (Pre. CGAnt: F = 1. 7. 1. Pre. CGPost: F = 1. Post. CG: F = 7. 6. In Pre. CGPost, mean signal intensity values were significantly higher during the execution of the finger- tapping. VI task (Fig. In Pre. CGAnt and Post. CG, significant signal increases were found only during MP (Pre. CGAnt: t = 5. 5. 6, p < 0. Post. CG: t = 3. 3. Pre. CGAnt, a tendency toward an increase (t = 2. MI. 1. Histograms represent mean . Significant differences from control. In a preliminary analysis, pixels significantly (r > 0. MP (population “MP”) were identified by correlating the time course of their signal intensities. VI or MP with a square- wave function. For each subject, the mean normalized signal intensities. MP” pixels lying in Pre. CGPost were then calculated for each of the 3. Fig. 2). A three- way, repeated- measures ANOVA (task . Moreover, they were higher during MI (t = 7. VI and still higher during MP (t = 1. Time profile of mean normalized signal intensity in the population of pixels, located in the posterior portion of the precentral. MP (population MP). Each point represents mean . The. task sequence is shown at the bottom of the graph. A repeated- measures ANOVA performed on values of the 3. MI (see Results). Clusters of pixels, the signal time course of which was significantly (r > 0. Fig. 3. A), were identified in the three ROIs in the majority of subjects (9/1. Pre. CGAnt, 1. 0/1. Pre. CGPost, and 8/1. Post. CG). Values were significantly different. F = 1. 30. 1, p < 0. MI than VI (t = 8. MP relative to the other two conditions (t = 1. No significant difference was found in the time profile of signal intensity between the pixel populations of the. MI were . 3. To identify pixels in the three ROIs significantly activated during MP, MI, or both conditions, their time profile of signal. B,C) or double- step (A) waveforms. Based on data shown in Figures 1 and 2, the height of the first step in waveform A was set to . Time profile of normalized f. MRI signal intensity in the pixel populations located in the posterior portion of the precentral. MP and MI (A) or MP alone (B). Each point represents mean . The task sequence is shown at the bottom of the graph. Face processing occurs outside the fusiform `face area' in autism: evidence from functional MRISummary. Processing the human face is at the focal point of most social interactions, yet this simple perceptual task is difficult for individuals with autism, a population that spends limited amounts of time engaged in face- to- face eye contact or social interactions in general. Thus, the study of face processing in autism is not only important because it may be integral to understanding the social deficits of this disorder, but also, because it provides a unique opportunity to study experiential factors related to the functional specialization of normal face processing. In short, autism may be one of the only disorders where affected individuals spend reduced amounts of time engaged in face processing from birth. Using functional MRI, haemodynamic responses during a face perception task were compared between adults with autism and normal control subjects. Four regions of interest (ROIs), the fusiform gyrus (FG), inferior temporal gyrus, middle temporal gyrus and amygdala were manually traced on non- spatially normalized images and the percentage ROI active was calculated for each subject. Analyses in Talairach space were also performed. Overall results revealed either abnormally weak or no activation in FG in autistic patients, as well as significantly reduced activation in the inferior occipital gyrus, superior temporal sulcus and amygdala. Anatomical abnormalities, in contrast, were present only in the amygdala in autistic patients, whose mean volume was significantly reduced as compared with normals. Reaction time and accuracy measures were not different between groups. Thus, while autistic subjects could perform the face perception task, none of the regions supporting face processing in normals were found to be significantly active in the autistic subjects. Instead, in every autistic patient, faces maximally activated aberrant and individual- specific neural sites (e. It appears that, as compared with normal individuals, autistic individuals `see' faces utilizing different neural systems, with each patient doing so via a unique neural circuitry. Such a pattern of individual- specific, scattered activation seen in autistic patients in contrast to the highly consistent FG activation seen in normals, suggests that experiential factors do indeed play a role in the normal development of the FFA. MRIamygdalaface perceptionfusiform gyrus. EPI = echo- planar image. FFA = fusiform face areaf. MRI = functional MRIROI = region of interest. FG = fusiform gyrus. ITG = inferior temporal gyrus. MTG = middle temporal gyrus. Introduction. The face is at the epicentre of human social interactions, and from the beginning of life the normal infant attends vigorously to this stimulus (Bryant, 1. Disruption of this normal predisposition for face perception is characteristic of a relatively common developmental disorder, autism. Affected individuals are well noted for difficulties with perception of facial affect (Hobson, 1. Hobson et al., 1. Bormann- Kischkel et al., 1. Baron- Cohen et al., 1. Leekam et al., 1. Phillips et al., 1. Hobson and Lee, 1. Lord and Magill- Evans, 1. Pierce and Schreibman, 1. Thus, autistic individuals can be thought of as relatively `face inexperienced'. Limited experience with the human face is not only a common characteristic in autistic infants and children, it may also be one of the first developmentally critical mis- steps in a cascade of events leading to the profound impairment in social communication that is central to this disorder. Twin studies show autism to be among the most heritable of neuropsychiatric disorders (Bailey et al., 1. Courchesne et al., 1. Presumably, the diminished capacity of the autistic infant and child to orient towards and interact with the human face is the result of observable structural and/or functional brain defects. It is not surprising, then, that of the few autism/functional MRI (f. MRI) papers currently published, over half have investigated some aspect of face processing (Baron- Cohen et al., 1. Critchley et al., 2. Schultz et al., 2. Such studies, however, utilized a combination of both autism and Asperger's subjects in the same sample and the degree to which such groups represent aetiologically similar or distinct populations is still unknown. Nonetheless, these reports provide evidence that temporal lobe structures are functionally abnormal in individuals with pervasive developmental disorders. For example, both Baron- Cohen et al. Moreover, in behavioural tests, autistic patients do not show the normal processing advantage of normally oriented faces over inverted faces (Hobson et al., 1. Tantam et al., 1. Interestingly, both types of performance abnormality are characteristic of adults with acquired fusiform lesions (Farah et al., 1. The similarity in behavioural performance on face processing tasks between individuals with acquired fusiform lesions and individuals with autism, suggests that individuals with autism may possess structural abnormalities in this cortical region. Currently, however, there are no structural reports of the FG in autism and, thus, obtaining such information was one goal of the present study. The amygdala has also been shown to play an essential role in face processing, but in contrast to the more basic face processes subsumed by the FG such as identification (Haxby et al., 2. For example, the amygdala has been shown to be involved in understanding a face as threatening or not (Morris et al., 1. Kawashima et al., 1. Baxter et al., 2. During normal development, the amygdala may thus work in concert with the FG, to identify faces as socially significant stimuli. Interestingly, apparently contradictory MRI evidence suggests that this structure is abnormally enlarged (Howard, 2. Aylward et al., 1. Haznedar et al., 2. Autopsy data reveal increased cell packing density (Bauman and Kemper, 1. Despite the inconclusiveness of structural data on the amygdala in autism, it is a widely held belief that abnormalities of this structure are pivotal to the social dysfunction seen in autism (Bachevalier, 1. Baron- Cohen et al., 2. Howard, 2. 00. 0). An additional goal of the present study, therefore, was to obtain structural volume measures of the amygdala and to compare such measures with those of the FG. In addition to the great importance of face processing research for the study of autism, this topic is also of intense interest in the field of basic neuroscience. Using f. MRI technology, a host of studies have uncovered a remarkable phenomenon: the fusiform gyrus is consistently active during face viewing in virtually all studies of normal humans (see Haxby et al., 1. Puce et al., 1. 99. Clark et al., 1. 99. Kanwisher et al., 1. Cabeza and Nyberg, 2. The consistency of f. MRI and neuropsychological results is such that it is now near dogma that face processing uniformly engages a specific region of the FG; indeed, this special brain region is sometimes referred to as the fusiform face area (FFA) and many believe that the specificity of this region is driven mainly by genetic factors (Farah et al., 1. Kanwisher, 2. 00. New evidence, however, raises the possibility that the specialization of the fusiform region for face processing might instead be based largely on experiential factors. In a recent study, Gauthier and colleagues not only showed that the FFA was active during face viewing, but also, during bird or car viewing for subjects who were either car or bird experts (Gauthier et al., 2. In short, these authors suggest that the functional specialization of the FFA may have evolved for the processing of extremely familiar objects, of which faces are the most likely candidate for the majority of normal individuals. Such remarkable invariance in response to faces in the adult brain suggests that in normal development there are powerful factors, genetic and/or experiential, that inevitably lead to this specific neural organization. The underlying developmental factors are largely unknown because the usual modes of obtaining such information (e. Despite these significant hurdles, it may yet be possible to identify factors that influence FFA functional development utilizing autistic subjects, a population with limited experience with faces throughout life. If FFA reflects a largely innately determined, specialized processing module (Kanwisher, 2. FFA might be predicted to be engaged, but perhaps to a weaker degree, by the human face. On the other hand, if FFA reflects the emergence of a special processing capacity driven by extensive experience with faces (Tarr and Gauthier, 2. FFA might be predicted not to be engaged by human faces. A final goal of the present study, therefore, was to investigate these two alternatives. The study of autism brings with it some unique methodological challenges and, currently, there is no agreed upon standard for analysing functional data from psychiatric populations with multiple, developmental anatomical brain defects. Defects in regional morphometrics, such as hypoplasia in cerebellum (Courchesne et al., 1. Bailey et al., 1. Saitoh et al., 2. Courchesne et al., 1. Piven et al., 1. 99. Piven et al., 1. 99. Courchesne et al., 2. Two approaches that have been described in the literature are the spatial normalization and the `native space' approach; each associated with its own set of strengths and weaknesses. The spatial normalization approach, such as Talairach, is one of the most widely used analysis methods in the field of functional neuroimaging. Individual brains are warped into a `standard space' by use of specific anatomical markers (e. The strength of such an approach is detection of consistent sites of activation within a subject group, as well as easy detection of major global differences between two study groups (e.
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