Conventional t-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.Q 1996 Wiley-Liss, h e .

After eating, the human brain senses a biochemical change and then signals satiation, but precisely when this occurs is unknown. Even for well-established physiological systems like glucose-insulin regulation, the timing of interaction between hormonal processes and neural events is inferred mostly from blood sampling. Recently, neuroimaging studies have provided in vivo information about the neuroanatomical correlates of the regulation of energy intake. Temporal orchestration of such systems, however, is crucial to the integration of neuronal and hormonal signals that control eating behaviour. The challenge of this functional magnetic resonance imaging study is to map not only where but also when the brain will respond after food ingestion. Here we use a temporal clustering analysis technique to demonstrate that eating-related neural activity peaks at two different times with distinct localization. Importantly, the differentiated responses are interacting with an internal signal, the plasma insulin. These results support the concept of temporal parcellation of brain activity, which reflects the different natures of stimuli and responses. Moreover, this study provides a neuroimaging basis for detecting dynamic processes without prior knowledge of their timing, such as the acute effects of medication and nutrition in the brain.

The prefrontal cortex, a part of the limbic-thalamic-cortical network, participates in regulation of mood, cognition and behavior and has been implicated in the pathophysiology of major depressive disorder (MDD). Many neuropsychological studies demonstrate impairment of working memory in patients with MDD. However, there are few functional neuroimaging studies of MDD patients during working memory processing, and most of the available ones included medicated patients or patients with both MDD and bipolar disorder. We used functional magnetic resonance imaging (fMRI) to measure prefrontal cortex function during working memory processing in untreated depressed patients with MDD. Fifteen untreated individuals with Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition recurrent MDD (mean age7s.d. = 34.3711.5 years) and 15 healthy comparison subjects (37.7712.1 years) matched for age, sex and race were studied using a GE/Elscint 2T MR system. An echo-planar MRI sequence was used to acquire 24 axial slices. The n-back task (0-back, 1-back and 2-back) was used to elicit frontal cortex activation. Data were analyzed with a multiple regression analysis using the FSL-FEAT software. MDD patients showed significantly greater left dorsolateral cortex activation during the n-back task compared to the healthy controls (P < 0.01), although task performance was similar in the two groups. Furthermore, the patients showed significant anterior cingulate cortex activation during the task, but the comparison subjects did not (P < 0.01). This study provides in vivo imaging evidence of abnormal frontolimbic circuit function during working memory processing in individuals with MDD.

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance.