Although nent onn PC( (41) patients without AD (17) s has been d 3 not e in animal and nan s AD en the Fig.5. dua for ss of fit to sta ard default- od of AD the hehavio of the default. teriz although the ral ork a role in that it me ng pport fo our hyp sis that the de and n our previous study,we hypothe dthat the etwork ural imaging ethod the sin the nd p rk in he The e in and SPECT be u ve re fiel he ng of a sub of f 15.T most likel ly lost elder itivity of ficit findin One that the t b t of the that of 13 AD pat in the with our ting sympl (40 bila 22.1m and (defic ersity gr emp that AD nly de thdirct.casetha th t I also det whether better to gain more insight into the putative memory functions o during a true in bant resting paradigm,as opposed to during a sensory PNA51Marh30.20041vol.1o11no.131464 prominent role in the default-mode network, network activity is deficient in AD compared to healthy elderly controls, and a metric of network activity shows promise as a clinical marker of AD. Currently, the behavioral correlates of the default-mode net￾work remain uncharacterized, although there are several poten￾tially inclusive hypotheses. Some investigators have suggested that the network has a role in attending to environmental stimuli, both internally and externally generated (18, 34). Others have suggested that it mediates processes such as reviewing past knowledge in preparing for future actions (35). The current findings offer further support for our hypothesis that the default￾mode network is closely involved in episodic memory processing. In our previous study, we hypothesized that the network might have some role in memory processing based on the prominent role of the PCC within it and evidence linking the PCC to memory functions (16, 36, 37). Although we detected a cluster of posterior parahippocampal coactivation in that 3-T study, there was little else to implicate medial temporal memory regions in the default-mode network. By contrast, we have now detected significant bilateral hippocampalentorhinal cortex coactivation in the default-mode network in healthy young and elderly subjects as well as unilateral MTL coactivation in the AD group. We believe the discrepancy in hippocampal coactivation between our initial study and the current study can be traced to the different field strengths in the two studies. That is, the hippocampal coactivation in the default-mode network detected across all three groups here at 1.5-T was most likely lost to susceptibility artifact at 3-T (38). One of the most consistent species-independent findings in neuroscience is that the hip￾pocampus is integral to episodic memory processing (39). Fur￾ther, episodic memory loss is the cardinal feature of AD and the most common presenting symptom (40). Combining these two facts with our findings of (i) significant bilateral hippocampal coactivation in the two healthy Washington University groups and (ii) deficient hippocampal activity in the AD group makes a compelling, albeit indirect, case that the network plays a critical role in episodic memory processing. In future studies, we hope to gain more insight into the putative memory functions of the default-mode network by exploring correlations between net￾work activity and neuropsychological measures. Although the MTL is the initial site of histopathological changes in AD (8), the PCC is the most common site of early metabolic and perfusion abnormalities. Disrupted connectivity between the hippocampusentorhinal cortex and the PCC has been invoked as the mechanism behind PCC hypometabolism and hypoperfusion in early AD (3, 9). There is evidence from a number of human studies supporting prominent connectivity between the PCC and MTL. PET studies have demonstrated task-driven PCC and MTL interactions across groups of subjects (15, 16). Studies of ‘‘retrosplenial amnesia’’ (41) and PCC hypometabolism in amnestic patients without AD (17) also provide support for PCC–MTL interactions. At the neuronal level, connectivity between these two regions has been demon￾strated in animal studies (10–13). The ICA approach used here extracts the network en bloc and does not provide direct mea￾sures of interregional connectivity. Nonetheless, based on bur￾geoning evidence in animal and human studies, a strong case can be made that the coactivation of PCC and MTL detected here reflects connectivity between these two regions. Activity in these two regions was deficient in the AD group compared to the elderly controls (Fig. 4). Although reduced connectivity with parietal or other cortical regions could account for decreased PCC activity, given the relatively focal MTL pathology in early AD and the converging evidence for MTL-PCC connectivity, we believe our findings support the hypothesis that impaired MTL– PCC connectivity accounts for the decreased PCC metabolism perfusion detected in PET and SPECT studies (1, 2, 9). A unique advantage provided by the method used here is that it allows one to examine task-independent network activity in individual subjects. It is this critical distinction that allowed us to demonstrate the clinical potential of our approach in the diag￾nosis of AD. By using a standard template to select the best-fit component for each subject, we have developed an automated ICA technique for detecting the default-mode network. The relative stability of the network across laboratories, field strengths, and healthy subject populations also speaks to its universality. It appears that the default-mode network is a readily and reproducibly detectable neural network operating in the resting state and in tasks with low cognitive demand. Detection of the network at 1.5 T is important in that the vast majority of clinical scans are done at this field strength. A number of attributes make ICA-based detection of the default￾mode network a promising candidate in the ongoing quest to find a safe noninvasive biomarker of incipient AD. Unlike most structural imaging methods, the process can be automated, minimizing manpower requirements and the potential for inves￾tigator bias. The absence of a task eliminates issues such as performance differences among groups and practice effects with repeated scanning. The enhanced spatial resolution and reliance on endogenous signal rather than radionuclide tracers also make this approach preferable to PET and SPECT methods. We have reported a metric here, reflecting the goodness of fit of a subject’s default-mode network to a standard default-mode template, which distinguishes individual AD subjects from healthy elderly subjects with a sensitivity of 85% and a specificity of 77%. These sensitivity and specificity values are in the range considered clinically relevant by a recent Working Group on biomarkers in AD (42). The results are particularly encouraging in light of the fact that 8 of 13 AD patients were in the earliest stages of the disease (Clinical Dementia Rating score of 0.5) (22). In subsequent studies, this approach may be optimized by using a template that includes only regions where network activity differs between AD subjects and elderly controls (Fig. 4). It will also be important to determine whether better sensitivity and specificity can be achieved by examining network activity during a true resting paradigm, as opposed to during a sensory￾Fig. 5. Individual scores for goodness of fit to standard default-mode network. A scattergram shows the median goodness of fit for each subject in the AD and healthy elderly groups using the Stanford University ICA-derived default-mode template. The group means were significantly different in a two-sample t test (P  0.01). The horizontal line indicates a cutoff point of 2.1 where 11 of 13 AD subjects and 10 of 13 elderly subjects are correctly cate￾gorized, yielding a sensitivity of 85% and a specificity of 77%. Greicius et al. PNAS March 30, 2004 vol. 101 no. 13 4641 NEUROSCIENCE