摘要:

AbstractAdvances in human neurobiology are now made possible through methods which combine structural magnetic resonance imaging (MRI), three‐dimensional reconstruction, and statistical analysis. MRI‐based reconstruction enables the in vivo quantification of regional cortical surface area (rCSA) while inter‐group comparisons uncover relationships of cortical morphometry with genotype, sex, and developmental abnormalities. In studies on normals we have found strong associations between the rCSA of monozygotic twins as compared to unrelated pairings. Further analysis of this data uncovered significant differences between the male and female twins in left hemisphere rCSA. When these methods were applied to brains of dyslexic subjects and controls, we identified a pattern of differences involving all major subdivisions of both hemispheres. Taken together, these techniques can illuminate structurefunction issues in both normal and diseased brains. ©1996 Wiley‐L

ISSN:1065-9471    

DOI:10.1002/hbm.460030402

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractThe purpose of this study was to determine the mediating neuroanatomy of implicit and explicit sequence learning using a modified version of the serial reaction time (SRT) paradigm. Subjects were seven healthy, right‐handed adults (three male, four female, mean age 26.7, range 18–43 yr). PET data were acquired via the oxygen‐15‐labeled‐carbon dioxide inhalation method while subjects performed the SRT. Subjects were scanned during two blocks each of (1) no sequence (Random), (2) single‐blind, 12‐item sequence (Implicit), and (3) unblinded, same sequence (Explicit). Whole‐brain‐normalized images reflecting relative regional cerebral blood flow (rCBF) were transformed to Talairach space, and statistical parametric maps (SPMs) of z‐scores were generated for comparisons of interest. The threshold for significant activation was defined as z‐score ≥ 3.00. Behavioral data demonstrated significant learning (P<.05) for Implicit and Explicit conditions. Tests of explicit knowledge reflected non‐significant explicit contamination during the Implicit condition. Foci of significant activation in the Implicit condition were found in right ventral premotor cortex, right ventral caudate/nucleus accumbens, right thalamus, and bilateral area 19; activation in the Explicit condition included primary visual cortex, peri‐sylvian cortex, and cerebellar vermis. Activations in visual and language areas during the Explicit condition may reflect conscious learning strategies including covert verbal rehearsal and visual imagery. Right‐sided premotor, striatal, and thalamic activations support the notion that implicit sequence learning is mediated by cortico‐striatal pathways, preferentially within the right hem

ISSN:1065-9471    

DOI:10.1002/hbm.460030403

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractConventionalt‐statistics and cross‐correlation coefficients are commonly used for analysis of functional magnetic resonance images. The sensitivity of these statistics is usually low because severe Bonferroni‐type corrections are required for multiple statistical comparisons to minimize the false‐positive error. In the human brain, most functional areas are larger in size than a single image pixel, and coactivation of numerous contiguous pixels is expected. The probability of occurrence of clusters due to random noise is small and can be modeled. Cluster size and intensity thresholding can be used to assess statistical significance. Previous cluster analysis strategies used Gaussian models, working best with low spatial resolution images (e.g., positron emission tomography). We present a new cluster analysis model applicable to data with little or even no covariance between adjacent pixels. Computer simulations and phantom experiments were used to verify this strategy. Our new method is substantially more sensitive than both the conventional intensity‐only thresholding (IOT) method and the previous cluster method for signal change less than 6%, with maximum significant enhancement in sensitivity of 12.8 and 3.8 times, respectively. The results obtained from normal volunteers with visual stimulation further confirm the effectiveness of our new approach and show an average increase in detected activation area of 3.1 times over the IOT method and of 1.6 times over the previous cluster method using the new approach. ©1996 Wiley

ISSN:1065-9471    

DOI:10.1002/hbm.460030404

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractThis work addresses the complexity of neuronal interactions, the nature of this complexity and how it can be characterised in real neurophysiological processes. A measure of complexity has been introduced recently (Tononi et al. [1994]: Proc Natl Acad Sci USA 91:5033–5037) that is sensitive to the joint constraints imposed by two principles of brain organisation: functional segregation and functional integration. Functional segregation implies that the dynamics of a cortical area should reflect the multidimensional attributes for which that area is specialised (in other words, regional dynamics should show a relatively high entropy). Conversely, functional integration implies a distributed and divergent influence of every cortical area on the remaining areas (i.e., the overall dynamics should show a low entropy). Our measure is based on the profile of entropies of different sized regions of the brain. Complexity is high when smaller regions have (on average) a relatively high entropy with respect to the entropy of the whole system. This measure is equivalent to the (average) mutual information between all small regions and the rest of the system in question.We have applied this measure to nonlinear simulations and to neurophysiological data obtained with fMRI during photic stimulation. Because patterns of activity in the brain are intermediate between a state of incoherence, with regionally specific dynamics and a state of global coherence, we predicted that simulated nonlinear processes with similar characteristics would have a high complexity. In the language of nonlinear dynamics we hypothesised that the greatest complexity would be found somewhere between high‐dimensional, chaotic behaviour and low‐dimensional, orderly behaviour. Equivalently, using the metaphor of loosely coupled oscillators, we predicted that complexity would be highest in the domain between asynchronous oscillations and global synchrony. This hypothesis was confirmed using nonlinear neuronal simulations. In addition, we demonstrate that the complexity of neurophysiological data is easily measured and can show a significant complexity when compared to suitable control processes. © 1996 Wiley‐L

ISSN:1065-9471    

DOI:10.1002/hbm.460030405

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

ISSN:1065-9471    

DOI:10.1002/hbm.460030401

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY