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The properties of the hemodynamic latencies in functional maps have been relatively unexplored. Accurate methods of estimating hemodynamic latencies are needed to take advantage of this feature of fMRI. A fully automated, weighted least-squares (WLS) method for estimating temporal latencies is reported. Using a weighted linear model, the optimal latency and amplitude of the fMRI response can be determined for those voxels that pass a detection threshold. There is evidence from previous studies that the hemodynamic response may be time-locked to the stimulus within certain limits, less variable earlier in its evolution, and able to resolve information about relative hemodynamic timing. This information can be used to test hypotheses about the sequence and spatial distribution of neural activity. The method can be used to weight the earliest evolution of the hemodynamic response more heavily and decrease bias resulting from the hemodynamic response function. Additionally, the WLS method can control for varying response shapes across the brain and improve latency comparisons between brain regions. The WLS method was developed to study the properties of hemodynamic latencies, which may be increasingly important as event-related fMRI continues to be advanced. Magn Reson Med 44:947-954, 2000.

Cognitive dysfunction is a core feature of schizophrenia, and persons at risk for schizophrenia may show subtle deficits in attention and working memory. In this study, we investigated the relationship between integrity of functional brain networks and performance in attention and working memory tasks as well as schizophrenia risk. Methods: A total of 235 adults representing 3 levels of risk (102 outpatients with schizophrenia, 70 unaffected first-degree relatives of persons with schizophrenia, and 63 unrelated healthy controls [HCs]) completed restingstate functional magnetic resonance imaging and a battery of attention and working memory tasks (Brief Test of Attention, Hopkins Verbal Learning Test, and Brief Visuospatial Memory Test) on the same day. Functional networks were defined based on coupling with seeds in the dorsal anterior cingulate cortex, dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and primary visual cortex. Networks were then dissected into regional clusters of connectivity that were used to generate individual interaction matrices representing functional connectivity within each network. Results: Both patients with schizophrenia and their first-degree relatives showed cognitive dysfunction compared with HCs. First canonicals indicated an inverse relationship between cognitive performance and connectivity within the DLPFC and MPFC networks. Multivariate analysis of variance revealed multivariate main effects of higher schizophrenia risk status on increased connectivity within the DLPFC and MPFC networks. Conclusions: These data suggest that excessive connectivity within brain networks coupled to the DLPFC and MPFC, respectively, accompany cognitive deficits in persons at risk for schizophrenia. This might reflect compensatory reactions in neural systems required for cognitive processing of attention and working memory tasks to brain changes associated with schizophrenia.

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