The extent of task-related fMRI activation can vary as a function of task difficulty. Also the efficiency or capacity of the brain networks underlying task performance can change with aging. We asked whether the expression of a network underlying task performance would differ as a function of task demand in old and young individuals. 26 younger and 23 older healthy adults performed a delayed item recognition task that used the response signal method to parametrically manipulate the extrinsic difficulty of the task by imposing five different deadlines for recognition response. Both age groups showed a speed accuracy trade-off, but the younger group achieved greater discriminability at the longer deadlines. We identified a spatial pattern of fMRI activation during the probe phase whose expression increased as the response deadline shortened and the task became more difficult. This pattern was expressed to a greater degree by the old group at the long deadlines, when the task was easiest. By contrast, this pattern was expressed to greater degree by the younger group at the short deadlines, when the task was hardest. This suggests reduced efficiency and capacity of this network in older subjects. These findings suggest that neuroimaging studies comparing task-related activation across groups with different cognitive abilities must be interpreted in light of the relative difficulty of the task for each group.

Subjects performed a continuous tracking concurrently with an intermittent visual detection task to investigate the existence of competition for a capacity-limited stage (a bottleneck stage). Both perceptual and response-related processes between the two tasks were examined behaviorally and the changes in brain activity during dual-tasking relative to single-task were also assessed. Tracking error and joystick speed were analyzed for changes that were time-locked to visual detection stimuli. The associated brain activations were examined with functional magnetic resonance imaging (fMRI). These were analyzed using mixed block and event-related models to tease apart sustained neural activity and activations associated with individual events. Increased tracking error and decreased joystick speed were observed relative to the target stimuli in the dual-task condition only, which supports the existence of a bottleneck stage in response-related processes. Neuroimaging data show decreased activation to target relative to non-target stimuli in the dual-task condition in the left primary motor and somatosensory cortices controlling right-hand tracking, consistent with the tracking interference observed in behavioral data. Furthermore, the ventral attention system, rather than the dorsal attention system, was found to mediate task coordination between tracking and visual detection.

This functional neuroimaging (fMRI) study examined the neural networks (spatial patterns of covarying neural activity) associated with the speed-accuracy tradeoff (SAT) in younger adults. The response signal method was used to systematically increase probe duration (125, 250, 500, 1,000 2,000 ms) in a nonverbal delayed-item recognition task. A covariance-based multivariate approach identified three networks that varied with probe duration – indicating that the SAT is driven by three distributed neural networks.

Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.

Spatiotemporal and recognition memory are affected by aging in humans and macaque monkeys. To investigate whether these deficits are coupled with atrophy of memory-related brain regions, T1-weighted magnetic resonance images were acquired and volumes of the cerebrum, ventricles, prefrontal cortex (PFC), calcarine cortex, hippocampus, and striatum were quantified in young and aged rhesus monkeys. Subjects were tested on a spatiotemporal memory procedure (delayed response [DR]) that requires the integrity of the PFC and a medial temporal lobe-dependent recognition memory task (delayed nonmatching to sample [DNMS]). Region of interest analyses revealed that age inversely correlated with striatal, dorsolateral prefrontal cortex (dlPFC), and anterior cingulate cortex volumes. Hippocampal volume predicted acquisition of the DR task. Striatal volume correlated with DNMS acquisition, whereas total prefrontal gray matter, prefrontal white matter, and dlPFC volumes each predicted DNMS accuracy. A regional covariance analysis revealed that age-related volumetric changes could be captured in a distributed network that was coupled with declining performance across delays on the DNMS task. This volumetric analysis adds to growing evidence that cognitive aging in primates arises from region-specific morphometric alterations distributed across multiple memory-related brain systems, including subdivisions of the PFC.

By comparing hemodynamic signals acquired immediately before and during activation, functional magnetic resonance imaging (fMRI) is well suited for mapping acute changes in brain function. However, it remains unclear whether fMRI can map functional changes over longer periods. Here, we address this issue by empirically testing the feasibility of arterial spin labeling (ASL) fMRI to detect changes in cerebral blood flow (CBF) with baseline and task separated by 1 month. To increase the sensitivity of the method, we applied an algorithm that yielded flow density (CBFd) images that were independent of tissue content. To increase the accuracy, we developed a technique that generated arterial transit time at each voxel, independently. Results showed that activation changes in CBFd during the same session were statistically the same as across 30 days. The activation CBFd on day-30 was 34% (motor) and 25% (visual) higher than the respective baselines of 83 and 107 mL/100 g/min obtained on day-1. Furthermore, the signal-to-noise ratio of the CBFd measurement was 2.1 and 2.9 times higher than that of the conventional CBF for within-subject and across-subjects comparisons, respectively (n=9 healthy young subjects). Taken together, these results indicate that CBFd measure could be better suited than net CBF to map long-term changes in brain function.

Functional magnetic resonance imaging (fMRI) studies have shown that repetition priming of visual objects is typically accompanied by a reduction in activity for repeated compared to new stimuli (repetition suppression). However, the spatial distribution and direction (suppression vs. enhancement) of neural repetition effects can depend on the pre-experimental familiarity of stimuli. The first goal of this study was to further probe the link between repetition priming and repetition suppression/enhancement for visual objects and how this link is affected by stimulus familiarity. A second goal was to examine whether priming of familiar and unfamiliar objects following a single stimulus repetition is supported by the same processes as priming following multiple repetitions within the same task. In this endeavor, we examined both between and within subjects correlations between priming and fMRI repetition effects for familiar and globally unfamiliar visual objects during the first and third repetition of the stimuli. We included reaction time of individual trials as a linear regressor to identify brain regions whose repetition effects varied with response facilitation on a trial-by-trial basis. The results showed that repetition suppression in bilateral fusiform gyrus, was selectively correlated with priming of familiar objects that had been repeated once, likely reflecting facilitated perceptual processing or the sharpening of perceptual representations. Priming during the third repetition was correlated with repetition suppression in prefrontal and parietal areas for both familiar and unfamiliar stimuli, possibly reflecting a shift from top-down controlled to more automatic processing that occurs for both item types.

Neurophysiological studies have provided evidence of primary motor cortex hyperexcitability in primary dystonia, but several functional imaging studies suggest otherwise. To address this issue, we measured sensorimotor activation at both the regional and network levels in carriers of the DYT1 dystonia mutation and in control subjects. We used 15Oxygen-labelled water and positron emission tomography to scan nine manifesting DYT1 carriers, 10 non-manifesting DYT1 carriers and 12 age-matched controls while they performed a kinematically controlled motor task; they were also scanned in a non-motor audio-visual control condition. Within- and between-group contrasts were analysed with statistical parametric mapping. For network analysis, we first identified a normal motor-related activation pattern in a set of 39 motor and audio-visual scans acquired in an independent cohort of 18 healthy volunteer subjects. The expression of this pattern was prospectively quantified in the motor and control scans acquired in each of the gene carriers and controls. Network values for the three groups were compared with ANOVA and post hoc contrasts. Voxel-wise comparison of DYT1 carriers and controls revealed abnormally increased motor activation responses in the former group (P < 0.05, corrected; statistical parametric mapping), localized to the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and the inferior parietal cortex. Network analysis of the normative derivation cohort revealed a significant normal motor-related activation pattern topography (P < 0.0001) characterized by covarying neural activity in the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and cerebellum. In the study cohort, normal motor-related activation pattern expression measured during movement was abnormally elevated in the manifesting gene carriers (P < 0.001) but not in their non-manifesting counterparts. In contrast, in the non-motor control condition, abnormal increases in network activity were present in both groups of gene carriers (P < 0.001). In this condition, normal motor-related activation pattern expression in non-manifesting carriers was greater than in controls, but lower than in affected carriers. In the latter group, measures of normal motor-related activation pattern expression in the audio-visual condition correlated with independent dystonia clinical ratings (r = 0.70, P = 0.04). These findings confirm that overexcitability of the sensorimotor system is a robust feature of dystonia. The presence of elevated normal motor-related activation pattern expression in the non-motor condition suggests that abnormal integration of audio-visual input with sensorimotor network activity is an important trait feature of this disorder. Lastly, quantification of normal motor-related activation pattern expression in individual cases may have utility as an objective descriptor of therapeutic response in trials of new treatments for dystonia and related disorders.

Background

Regional cerebral blood flow (CBF), a good indirect index of cerebral pathologic changes in Alzheimer disease (AD), is more severely reduced in patients with higher educational attainment and IQ when controlling for clinical severity. This has been interpreted as suggesting that cognitive reserve allows these patients to cope better with the pathologic changes in AD.

Objective

To evaluate whether premorbid engagement in various activities may also provide cognitive reserve.

Design

We evaluated intellectual, social, and physical activities in 9 patients with early AD and 16 healthy elderly controls who underwent brain H215O positron emission tomography. In voxelwise multiple regression analyses that controlled for age and clinical severity, we investigated the association between education, estimated premorbid IQ, and activities, and CBF.

Results

In accordance with previous findings, we replicated an inverse association between education and CBF and IQ and CBF in patients with AD. In addition, there was a negative correlation between previous reported activity score and CBF in patients with AD. When both education and IQ were added as covariates in the same model, a higher activity score was still associated with more prominent CBF deficits. No significant associations were detected in the controls.

Conclusions

At any given level of clinical disease severity, there is a greater degree of brain pathologic involvement in patients with AD who have more engagement in activities, even when education and IQ are taken into account. This may suggest that interindividual differences in lifestyle may affect cognitive reserve by partially mediating the relationship between brain damage and the clinical manifestation of AD.

Although multivariate analytic techniques might identify diagnostic patterns that are not captured by univariate methods, they have rarely been used to study the neural correlates of Alzheimer's disease (AD) or cognitive impairment. Nonquantitative H215O PET scans were acquired during rest in 17 probable AD subjects selected for mild severity [mean-modified Mini Mental Status Examination (mMMS) 46/57; SD 5.1], 16 control subjects (mMMS 54; SD 2.5) and 23 subjects with minimal to mild cognitive impairment but no dementia (mMMS 53; SD 2.8). Expert clinical reading had low success in discriminating AD and controls. There were no significant mean flow differences among groups in traditional univariate SPM Voxel-wise analyses or region of interest (ROI) analyses. A covariance pattern was identified whose mean expression was significantly higher in the AD as compared to controls (P = 0.03; sensitivity 76–94%; specificity 63–81%). Sites of increased concomitant flow included insula, cuneus, pulvinar, lingual, fusiform, superior occipital and parahippocampal gyri, whereas decreased concomitant flow was found in cingulate, inferior parietal lobule, middle and inferior frontal, supramarginal and precentral gyri. The covariance analysis-derived pattern was then prospectively applied to the cognitively impaired subjects: as compared to subjects with Clinical Dementia Rating (CDR) = 0, subjects with CDR = 0.5 had significantly higher mean covariance pattern expression (P = 0.009). Expression of this pattern correlated inversely with Selective Reminding Test total recall (r = −0.401, P = 0.002), delayed recall (r = −0.351, P = 0.008) and mMMS scores (r = −0.401, P = 0.002) in all three groups combined. We conclude that patients with AD may differentially express resting cerebral blood flow covariance patterns even at very early disease stages. Significant alterations in expression of resting flow covariance patterns occur even for subjects with cognitive impairment. Expression of covariance patterns correlates with cognitive and functional performance measures, holding promise for meaningful associations with underlying biopathological processes.

In order to understand the brain networks that mediate cognitive reserve, we explored the relationship between subjects’ network expression during the performance of a memory test and an index of cognitive reserve. Using H215O positron emission tomography, we imaged 17 healthy older subjects and 20 young adults while they performed a serial recognition memory task for nonsense shapes under two conditions: low demand, with a unique shape presented in each study trial; and titrated demand, with a study list size adjusted so that each subject recognized shapes at 75% accuracy. A factor score that summarized years of education, and scores on the NART and the WAIS-R Vocabulary subtest was used as an index of cognitive reserve. The scaled subprofile model was used to identify a set of functionally connected regions (or topography) that changed in expression across the two task conditions and was differentially expressed by the young and elderly subjects. The regions most active in this topography consisted of right hippocampus, posterior insula, thalamus, and right and left operculum; we found concomitant deactivation in right lingual gyrus, inferior parietal lobe and association cortex, left posterior cingulate, and right and left calcarine cortex. Young subjects with higher cognitive reserve showed increased expression of the topography across the two task conditions. Because this topography, which is responsive to increased task demands, was differentially expressed as a function of reserve level, it may represent a neural manifestation of innate or acquired reserve. In contrast, older subjects with higher cognitive reserve showed decreased expression of the topography across tasks. This suggests some functional reorganization of the network used by the young subjects. Thus, for the old subjects this topography may represent an altered, compensatory network that is used to maintain function in the face of age-related physiological changes.

Objective

Few previous studies have investigated the association between APOE genotype and brain activation during performance of cognitive tasks in healthy middle-aged and elderly subjects, and the results have been mixed. The authors investigated APOE-mediated differential brain activation in a group of healthy elderly subjects.

Methods

Using H2 15O positron emission tomography (PET), they imaged 32 healthy subjects (26 non-ε4 carriers and 6 ε4 carriers) performing a serial shape-recognition memory task under two conditions: Simple Demand (SD), in which one shape was presented in each study trial, and Titrated Demand (TD), in which study list length was adjusted so that each subject recognized words at approximately 75% accuracy. Multiple-regression analyses were performed, with the “activation” difference (TD–SD PET counts) as the dependent variable and the APOE genotype (presence versus absence of the ε4 allele) as the independent variable.

Results

Compared with non-carriers, ε4 carriers exhibited significantly decreased TD–SD activation differences in the left superior temporal, right superior frontal, left postcental, left precuneus, and posterior cingulate gyrus because ε4 carriers (versus non-carriers) showed increased activation during the SD and decreased activation during the TD condition.

Conclusion

Patterns of brain activation during a nonverbal memory task differed as a function of APOE genotype and, therefore, of genetic risk for Alzheimer disease (AD). Differences in activation were not a reflection of task difficulty, but indicate memory-related altered cognitive processing. Brain regions with decreased activation in the ε4 subjects may result from subclinical incipient AD pathology and/or APOE-related neurophysiologic heterogeneity.

As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.

Research has indicated that there may be age-related and Alzheimer’s disease (AD)-related reductions in regional cerebral blood flow (rCBF) in the brain. This study explored differences in age- and AD-related rCBF patterns in the context of cognitive aging using a multivariate approach to the analysis of H2 15O PET data. First, an rCBF covariance pattern that distinguishes between a group of younger and older adults was identified. Individual subject’s expression of the identified age-related pattern was significantly correlated with their performance on tests of memory, even after controlling for the effect of age. This finding suggests that subject expression of the covariance pattern explained additional variation in performance on the memory tasks. The age-related covariance pattern was then compared to an AD-related covariance pattern. There was little evidence that the two covariance patterns were similar, and the age-related pattern did a poor job of differentiating between cognitively-healthy older adults and those with probable AD. The findings from this paper are consistent with the multifactorial nature of cognitive aging.

The accuracy of the cerebral blood flow (CBF) imaging methods in humans has been impeded by the partial volume effects (PVE), which are a consequence of the limited spatial resolution. Because of brain atrophy, PVE can be particularly problematic in imaging the elderly and can considerably overestimate the CBF difference with the young. The primary goal of this study was to separate the structural decline from the true CBF reduction in elderly. To this end, a PVE-correction algorithm was applied on the CBF images acquired with spin-echo EPI continuous arterial spin labeling MRI (voxel size = 3.4 × 3.4 × 8 mm3). Tissue-specific CBF images that were independent of voxels’ tissue fractional volume were obtained in elderly (N = 30) and young (N = 26); mean age difference was 43 years. Globally, PVE-corrected gray matter CBF was 88.2 ± 16.1 and 107.3±17.5 mL/100g min in elderly and young, respectively. The largest PVE contribution was found in the frontal lobe and accounted for an additional 10% and 12% increase in the age-related CBF difference between men and women, respectively. The GM-to-WM CBF ratios were found to be on average 3.5 in elderly and 3.9 in young. Whole brain voxelwise comparisons showed marked CBF decrease in anterior cingulate (bilateral), caudate (bilateral), cingulate gyrus (bilateral), cuneus (left), inferior frontal gyrus (left), insula (left), middle frontal gyrus (left), precuneus (bilateral), prefrontal cortex (bilateral), superior frontal gyrus (bilateral) in men and amygdala (bilateral), hypothalamus (left), hippocampus (bilateral), and middle frontal gyrus (right) in women.

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

The purpose of this preliminary study was to examine cerebral blood flow (CBF) as measured by arterial spin labeling (ASL) in tissue classified as white matter hyperintensities (WMH), normal appearing white matter, and grey matter. Seventeen healthy older adults received structural and ASL MRI. Cerebral blood flow was derived for three tissue types: WMH, normal appearing white matter, and grey matter. Cerebral blood flow was lower in WMH areas relative to normal appearing white matter, which in turn, was lower than grey matter. Regions with consistently lower CBF across individuals were more likely to appear as WMH. Results are consistent with an emerging literature linking diminished regional perfusion with the risk of developing WMH.

To capture patterns of normal age-associated atrophy, we previously used a multivariate statistical approach applied to voxel based morphometry that identified age-associated gray and white matter covariance networks (Brickman, et al. 2007). The current study sought to examine the stability of these patterns by forward applying the identified networks to an independent sample of neurologically healthy younger and older adults. Forty-two younger and 35 older adults were imaged with standard high-resolution structural magnetic resonance imaging (MRI). Individual images were spatially normalized and segmented into gray and white matter. Covariance patterns that were previously identified with scaled subprofile model analyses were prospectively applied to the current sample to identify to what degree the age-associated patterns were manifest. Older individuals were also assessed with a modified version of the Mini Mental State Examination (mMMSE). Gray matter covariance pattern expression discriminated between younger and older participants with high optimal sensitivity (100%) and specificity (90.5%). While the two groups differed in the degree of white matter pattern expression (t (75) = 5.26, p < 0.001), classification based on white matter expression was relatively low (sensitivity = 80% and specificity = 61.9%). Among older adults, chronological age was significantly associated with increased gray matter pattern expression (r (32) = 0.591, p < 0.001) but not with performance on the mMMSE (r (31) = −0.314, p = 0.085). However, gray matter pattern expression was significantly associated with performance on the mMMSE (r (31) = −0.405, p = 0.024). The findings suggest that the previously derived age-associated covariance pattern for gray matter is reliable and may provide information that is more functionally meaningful than chronological age.

Continuous arterial spin labeling (CASL) MRI was combined with multivariate analysis for detection of an Alzheimer's Disease (AD) related cerebral blood flow (CBF) covariance pattern. Whole brain resting CBF maps were obtained using spin echo, echo planar imaging (SE-EPI) CASL in patients with mild AD (n=12, age = 70.7± 8.7, 7 males, modified Mini-Mental State Examination (mMMS) = 38.7/57 ± 11.1) and age-matched healthy controls (HC) (n = 20; age = 72.1 ± 6.5, 8 males). A covariance pattern for which the mean expression was significantly higher (p < 0.0005) in AD than HC was identified containing Posterior Cingulate, Superior Temporal, Parahippocampal, and Fusiform gyri, as well as Thalamus, Insula, and Hippocampus. The results from this analysis were supplemented with those from the more standard, ROI and voxelwise, univariate techniques. All ROIs (17/hemisphere) showed significant decrease in CBF in AD [p < 0.001 for all ROIs, αcorrected = 0.05]. The area under the ROC curve for discriminating AD vs. HC was 0.97 and 0.94 for covariance pattern and gray matter ROI, respectively. Fewer areas of depressed CBF in AD were detected using voxelwise analysis [corrected, p < 0.05]. These areas were: Superior Temporal, Cingulate, Middle Temporal, Fusiform gyri, as well as Inferior Parietal Lobule and Precuneus. When tested on extensive split-half analysis to map out the replicability of both multivariate and univariate approaches, the expression of the pattern from multivariate analysis was superior to that of the univariate.

Sixteen healthy young adults (ages 18–32) and 16 healthy older adults (ages 67–81) completed a delayed response task in which they saw the following visual sequence: memory stimuli (2 abstract shapes; 3,000 ms), a blank delay (5,000 ms), a probe stimulus of variable duration (one abstract shape; 125, 250, 500, 1,000, or 2,000 ms), and a mask (500 ms). Subjects decided whether the probe stimulus matched either of the memory stimuli; they were instructed to respond during the mask, placing greater emphasis on speed than accuracy. The authors used D. L. Hintzman & T. Curran’s (1994) 3-parameter compound bounded exponential model of speed–accuracy tradeoff to describe changes in discriminability associated with total processing time. Group-level analysis revealed a higher rate parameter and a higher asymptote parameter for the young adult group, but no difference across groups in x-intercept. Proxy measures of cognitive reserve (Y. Stern et al., 2005) predicted the rate parameter value, particularly in older adults. Results suggest that in working memory, aging impairs both the maximum capacity for discriminability and the rate of information accumulation, but not the temporal threshold for discriminability.

We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer’s disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker’s diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.

Epidemiologic evidence suggests that cognitive reserve (CR) mitigates the effects of aging on cognitive function. The goal of this study was to see whether a common neural mechanism for CR could be demonstrated in brain imaging data acquired during the performance of two tasks with differing cognitive processing demands. Young and elder subjects were scanned with fMRI while performing a delayed item response tasks that used either letters (40 young, 18 old), or shapes (24 young, 21 old). Difficulty or load was manipulated by varying the number of stimuli that were presented for encoding. Load-dependent fMRI signal corresponding to each trial component (stimulus presentation, retention delay, and probe) and task (letter or shape) were regressed onto 2 putative CR variables. Canonical variates analysis was applied to the resulting maps of regression coefficients, separately for each trial component, to summarize the imaging data – CR relationships. There was a latent brain pattern noted in the stimulus presentation phase that manifested similar relationships between load-related encoding activation and CR variables across the letter and shape tasks in the young but not the elder age group. This spatial pattern could represent a general neural instantiation of CR that is affected by the aging process.

As clinical and cognitive neurosciences mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention because they have attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, in contrast, cannot directly address functional connectivity in the brain. Apart from this conceptual difference, the covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. We provide two examples that illustrate different uses of multivariate techniques in cognitive and clinical neuroscience. We hope this contribution helps facilitate wider dissemination of these techniques in the research community.

In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.