Aberrant Temporal Connectivity in Persons at Clinical High Risk for Psychosis.

BACKGROUND: Schizophrenia, a neurodevelopmental disorder, involves abnormalities in functional connectivity (FC) across distributed neural networks, which are thought to antedate the emergence of psychosis. In a cohort of adolescents and young adults at clinical high risk (CHR) for psychosis, we applied data-driven approaches to resting-state fMRI data so as to systematically characterize FC abnormalities during this period and determine whether these abnormalities are associated with psychosis risk and severity of psychotic symptoms. METHODS: Fifty-one CHR participants and 47 matched healthy controls (HCs) were included in our analyses. Twelve of these CHR participants developed psychosis within 3.9 years. We estimated one multivariate measure of FC and studied its relationship to CHR status, conversion to psychosis and positive symptom severity. RESULTS: Multivariate analyses revealed between-group differences in whole-brain connectivity patterns of bilateral temporal areas, mostly affecting their functional connections to the thalamus. Further, more severe positive symptoms were associated with greater connectivity abnormalities in the anterior cingulate and frontal cortex. CONCLUSIONS: Our study demonstrates that the well-established FC abnormalities of the thalamus and temporal areas observed in schizophrenia are also present in the CHR period, with aberrant connectivity of the temporal cortex most associated with psychosis risk.

Functional MRI study of mild Alzheimer's disease using amplitude of low frequency fluctuation analysis / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Previous studies have shown that the functional brain activity in the resting state is impaired in Alzheimer's disease (AD) patients. However, most studies focused on the relationship between different brain areas, rather than the amplitude or strength of the regional brain activity. The purpose of this study was to explore the functional brain changes in AD patients by measuring the amplitude of the blood oxygenation level dependent (BOLD) functional MRI (fMRI) signals.</p><p><b>METHODS</b>Twenty mild AD patients and twenty healthy elderly subjects participated in the fMRI scan. The amplitude of low frequency fluctuation (ALFF) was calculated using REST software.</p><p><b>RESULTS</b>Compared with the healthy elderly subjects, the mild AD patients showed decreased ALFF in the right posterior cingulate cortex, right ventral medial prefrontal cortex, and in the bilateral dorsal medial prefrontal cortex. No brain region with increased ALFF was found in the AD group compared with the control group.</p><p><b>CONCLUSIONS</b>The reduced activity in the posterior cingulate cortex and medial prefrontal cortex observed in the present study suggest that the functional abnormalities of those areas are at an early stage of AD. The ALFF analysis may provide a useful tool in fMRI study of AD.</p>

DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI.

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.