A unique opportunity to understand genetic determinants of cognition is offered by Williams syndrome (WS), a well-characterized hemideletion on chromosome 7q11.23 that causes extreme, specific weakness in visuospatial construction (the ability to visualize an object as a set of parts or construct a replica). Using multimodal neuroimaging, we identified a neural mechanism underlying the WS visuoconstructive deficit. Hierarchical assessment of visual processing with fMRI showed isolated hypoactivation in WS in the parietal portion of the dorsal stream. In the immediately adjacent parietooccipital/intraparietal sulcus, structural neuroimaging showed a gray matter volume reduction in participants with WS. Path analysis demonstrated that the functional abnormalities could be attributed to impaired input from this structurally altered region. Our observations confirm a longstanding hypothesis about dorsal stream dysfunction in WS, demonstrate effects of a localized abnormality on visual information processing in humans, and define a systems-level phenotype for mapping genetic determinants of visuoconstructive function.

Although gyral and sulcal patterns are highly heritable, and emerge in a tightly controlled sequence during development, very little is known about specific genetic contributions to abnormal gyrification or the resulting functional consequences. Williams syndrome (WS), a genetic disorder caused by hemizygous microdeletion on chromosome 7q11.23 and characterized by abnormal brain structure and striking cognitive (impairment in visuospatial construction) and behavioral (hypersocial/anxious) phenotypes, offers a unique opportunity to study these issues. We performed a detailed analysis of sulcal depth based on geometric cortical surface representations constructed from high-resolution magnetic resonance imaging scans acquired from participants with WS and from healthy controls who were matched for age, sex, and intelligence quotient, and compared between-group differences with those obtained from a voxel-based morphometry analysis. We found bilateral reductions in sulcal depth in the intraparietal/occipitoparietal sulcus (PS) in the brains of participants with WS, as well as in the collateral sulcus and the orbitofrontal region in the left hemisphere. The left-hemisphere PS in the WS group averaged 8.5 mm shallower than in controls. Sulcal depth findings in the PS corresponded closely to measures of reduced gray matter volume in the same area, providing evidence that the gray matter volume loss and abnormal sulcal geometry may be related. In the context of previous functional neuroimaging findings demonstrating functional alterations in the same cortical regions, our results further define the neural endophenotype underlying visuoconstructive deficits in WS, set the stage for defining the effects of specific genes, and offer insight into genetic mechanisms of cortical gyrification.

Little is known about genetic regulation of the development of white matter. This knowledge is critical in understanding the pathophysiology of neurodevelopmental syndromes associated with altered cognition as well as in elucidating the genetics of normal human cognition. The hemideletion of Ϸ25 genes on chromosome 7q11.23 that causes Williams syndrome (WS) includes genes that regulate cytoskeletal dynamics in neurons, especially LIMK1 and CYLN2, and therefore offers the opportunity to investigate the role of these genes in the formation of white matter tracts. We used diffusion tensor imaging to demonstrate alteration in white matter fiber directionality, deviation in posterior fiber tract course, and reduced lateralization of fiber coherence in WS. These abnormalities are consistent with an alteration of the late stages of neuronal migration, define alterations of white matter structures underlying dissociable behavioral phenotypes in WS, and provide human in vivo information about genetic control of white matter tract formation.development ͉ LIMK1 ͉ neuronal growth cone ͉ neuronal migration ͉ tractography

Summary Searching for a neurobiological understanding of human intellectual capabilities has long occupied those very capabilities. Brain gyrification, or folding of the cortex, is as highly-evolved and variable a characteristic in humans as is intelligence. Indeed, gyrification scales with brain size, and relationships between brain size and intelligence have been demonstrated in humans [1-3]. However, gyrification shows a large degree of variability that is independent from brain size [4-6], suggesting that the former may independently contribute to cognitive abilities, and thus supporting a direct investigation of this parameter in the context of intelligence. Moreover, uncovering the regional pattern of such an association could offer insights into evolutionary and neural mechanisms. We tested for this brain-behavior relationship in two separate, independently-collected, large cohorts: 440 healthy adults and 662 healthy children, using high-resolution structural neuroimaging and comprehensive neuropsychometric batteries. In both samples, general cognitive ability was significantly associated (pfdr<0.01) with increasing gyrification in a network of neocortical regions, including large portions of the prefrontal cortex, inferior parietal lobule, and temporoparietal junction, as well as the insula, cingulate cortex, and fusiform gyrus, a regional distribution that was nearly identical in both samples (Dice similarity coefficient=0.80). This neuroanatomical pattern is consistent with an existing, well-known proposal, the Parieto-Frontal Integration Theory of Intelligence [7], and is also consistent with research in comparative evolutionary biology showing rapid neocortical expansion of these regions in humans relative to other species. These data provide a framework for understanding the neurobiology of human cognitive abilities, and suggest a potential neurocellular association.

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Before their disappearance from the fossil record approximately 40,000 years ago, Neanderthals, the ancient hominin lineage most closely related to modern humans, interbred with ancestors of present-day humans. The legacy of this gene flow persists through Neanderthal-derived variants that survive in modern human DNA; however, the neural implications of this inheritance are uncertain. Here, using MRI in a large cohort of healthy individuals of European-descent, we show that the amount of Neanderthal-originating polymorphism carried in living humans is related to cranial and brain morphology. First, as a validation of our approach, we demonstrate that a greater load of Neanderthal-derived genetic variants (higher “NeanderScore”) is associated with skull shapes resembling those of known Neanderthal cranial remains, particularly in occipital and parietal bones. Next, we demonstrate convergent NeanderScore-related findings in the brain (measured by gray- and white-matter volume, sulcal depth, and gyrification index) that localize to the visual cortex and intraparietal sulcus. This work provides insights into ancestral human neurobiology and suggests that Neanderthal-derived genetic variation is neurologically functional in the contemporary population.

Williams syndrome (WS) is a rare neurodevelopmental disorder caused by a 1.6 Mb microdeletion on chromosome 7q11.23 and characterized by hypersocial personality and prominent visuospatial construction impairments. Previous WS studies have identified functional and structural abnormalities in the hippocampal formation, prefrontal regions crucial for amygdala regulation and social cognition, and the dorsal visual stream, notably the intraparietal sulcus (IPS). Although aberrant ventral stream activation has not been found in WS, object-related visual information that is processed in the ventral stream is a critical source of input into these abnormal regions. The present study, therefore, examined neural interactions of ventral stream areas in WS. Using a passive face- and house-viewing paradigm, activation and functional connectivity of stimulus-selective regions in fusiform and parahippocampal gyri, respectively, were investigated. During house viewing, significant activation differences were observed between participants with WS and a matched control group in IPS. Abnormal functional connectivity was found between parahippocampal gyrus and parietal cortex and between fusiform gyrus and a network of brain regions including amygdala and portions of prefrontal cortex. These results indicate that abnormal upstream visual object processing may contribute to the complex cognitive/behavioral phenotype in WS and provide a systems-level characterization of genetically mediated abnormalities of neural interactions.

Back-propagation neural networks were used to classify PET scans as either normal or abnormal, with abnormal subjects defined as subjects who had previously been clinically diagnosed with memory disorders. Numerous neural network experiments were performed in order to achieve optimization with respect to number of hidden unJts and training duration. Optimizations and performance evaluations were based on ROC analysis, in which the area under the ROC curve was the figure of merit The neural network's performance was better than that of dlscrlminant analysis, and comparable to the expen's performance. despite the low resolution image data, which consisted of one value per brain lobe, provided to the network. INTRODUCI10NQuantitatJve approaches to the analysis and/or classification of Positron Emission Tomography (PE1) scans usually involve a reglon-of·interest (ROI) analysis, in which regional metabolic function in the brain is evaluated [I). Pattern recognition stUdies are then performed on these data.Various pattern recognltion technJques, including the back-propagation neural network (2), have been applied to the classification of normal and abnormal PET scans based on ROI data. Neural networks appear to perform better than standard statistical methods like dlscrlminant analysis (3).In the literature describing various recent applications of neural networks, there appears to be relatJvely little standardization In neural-network training. Networks are often trained to satisfy panicular "convergence criteria", which essentially specify how well the hypersurfaces defined by the network are able to separate the different classes comprising the trainJng set A more imponant consIderation in most drcumstances, however, is the abillty of a network to generalize and Identify previOUSly-unseen patterns, an issue which involves the number of traIning patterns, the dimensionality of these patterns, the architecture of the network (e.g., the number of hidden unJts) and number of trainJng Iterations. Complex networks trained on high-dlmenslonaJ patterns for an excessive number of iterations may tend to "memorize" their training sets, and learn criteria that are not generally applicable to populations of given pattern classes. Evaluation of a network's abillty to generalize is accomplished by cross-validation studies, that is, testing trained networks on new and independent data sets. The question then arises: what is the most appropriate figure of merit for performance evaluation?The ROC (Relative-Operating-Characteristic) method of analysis has recently come to be recognized as an Objective and comprehensive way to evaluate diagnostic systems, since It measures a diagnostic system's performance independent of dedsion biases and prior probabillties (4). The ROC curve represents a system's performance at several different settings of the particular dedsion criteria. The area under the curve is the "only performance measure available that is unin1Juenced by dedsion biases and prior probabillties. and it places the per...