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Progressive supranuclear palsy (PSP) is a rare movement disorder and often difficult to distinguish clinically from Parkinson’s disease (PD) and multiple system atrophy (MSA) in early phases. In this study, we report reproducible disease-related topographies of brain network and regional glucose metabolism associated with PSP in clinically-confirmed independent cohorts of PSP, MSA and PD patients and healthy controls in the USA and China. Using (18)F-FDG PET images from PSP and healthy subjects, we applied spatial covariance analysis with bootstrapping to identify a PSP-related pattern (PSPRP) and estimate its reliability, and evaluated the ability of network scores for differential diagnosis. We also detected regional metabolic differences using statistical parametric mapping analysis. We produced a highly reliable PSPRP characterized by relative metabolic decreases in the middle prefrontal cortex/cingulate, ventrolateral prefrontal cortex, striatum, thalamus and midbrain, covarying with relative metabolic increases in the hippocampus, insula and parieto-temporal regions. PSPRP network scores correlated positively with PSP duration and accurately discriminated between healthy, PSP, MSA and PD groups in two separate cohorts of parkinsonian patients at both early and advanced stages. Moreover, PSP patients shared many overlapping areas with abnormal metabolism in the same cortical and subcortical regions as in the PSPRP. With rigorous cross-validation, this study demonstrated highly comparable and reproducible PSP-related metabolic topographies at network and regional levels across different patient populations and PET scanners. Metabolic brain network activity may serve as a reliable and objective marker of PSP, although cross-validation applying recent diagnostic criteria and classification is warranted.

In healthy subjects, brain activation in motor regions is greater during the visual perception of "natural" target motion, which complies with the two-thirds power law, than of "unnatural" motion, which does not. It is unknown whether motion perception is normally mediated by a specific network that can be altered in the setting of disease. We used block-design functional magnetic resonance imaging and covariance analysis to identify normal network topographies activated in response to "natural" versus "unnatural" motion. A visual motion perception-related pattern (VPRP) was identified in 12 healthy subjects, characterized by covarying activation responses in the inferior parietal lobule, frontal operculum, lateral occipitotemporal cortex, amygdala, and cerebellum (Crus I). Selective VPRP activation during "natural" motion was confirmed in 12 testing scans from healthy subjects. Consistent network activation was not seen, however, in 29 patients with dystonia, a neurodevelopmental disorder in which motion perception pathways may be involved. Using diffusion tractography, we evaluated the integrity of anatomical connections between the major VPRP nodes. Indeed, fiber counts in these pathways were substantially reduced in the dystonia subjects. In aggregate, the findings associate normal motion perception with a discrete brain network which can be disrupted under pathological conditions.

Parkinson's disease (PD) is associated with distinct metabolic covariance patterns that relate to the motor and cognitive manifestations of the disorder. It is not known, however, how the expression of these patterns relates to measurements of nigrostriatal dopaminergic activity from the same individuals. To explore these associations, we studied 106 PD subjects who underwent cerebral PET with both 18F-fluorodeoxyglucose (FDG) and 18F-fluoro-L-dopa (FDOPA). Expression values for the PD motor- and cognition-related metabolic patterns (PDRP and PDCP, respectively) were computed for each subject; these measures were correlated with FDOPA uptake on a voxel-by-voxel basis. To explore the relationship between dopaminergic function and local metabolic activity, caudate and putamen FDOPA PET signal was correlated voxel-wise with FDG uptake over the entire brain. PDRP expression correlated with FDOPA uptake in caudate and putamen (P

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras.

Spatial covariance mapping can be used to identify and measure the activity of disease-related functional brain networks. While this approach has been widely used in the analysis of cerebral blood flow and metabolic PET scans, it is not clear whether it can be reliably applied to resting state functional MRI (rs-fMRI) data. In this study, we present a novel method based on independent component analysis (ICA) to characterize specific network topographies associated with Parkinson's disease (PD). Using rs-fMRI data from PD and healthy subjects, we used ICA with bootstrap resampling to identify a PD-related pattern that reliably discriminated the two groups. This topography, termed rs-MRI PD-related pattern (fPDRP), was similar to previously characterized disease-related patterns identified using metabolic PET imaging. Following pattern identification, we validated the fPDRP by computing its expression in rs-fMRI testing data on a prospective case basis. Indeed, significant increases in fPDRP expression were found in separate sets of PD and control subjects. In addition to providing a similar degree of group separation as PET, fPDRP values correlated with motor disability and declined toward normal with levodopa administration. Finally, we used this approach in conjunction with neuropsychological performance measures to identify a separate PD cognition-related pattern in the patients. This pattern, termed rs-fMRI PD cognition-related pattern (fPDCP), was topographically similar to its PET-derived counterpart. Subject scores for the fPDCP correlated with executive function in both training and testing data. These findings suggest that ICA can be used in conjunction with bootstrap resampling to identify and validate stable disease-related network topographies in rs-fMRI. Hum Brain Mapp 38:617-630, 2017. © 2016 Wiley Periodicals, Inc.

It is assumed widely that the clinical expression of Parkinson's Disease (PD), both motor and cognitive, is subtended by topographically distributed brain networks. However, little is known about the functional neuroanatomy of executive dysfunction in PD. Our objective was to validate further in a PD group the use of network analysis to assess the relationship between executive processes and pathological disorganization of frontostriatal networks. We studied 15 patients with idiopathic PD, and 7 age-matched normal controls, using resting [18F]fluorodeoxyglucose (FDG) and high-resolution positron emission tomography (PET). We carried out network analysis on regional metabolic data to identify specific covariation patterns associated with motor and executive dysfunction. We detected two independent patterns relating respectively to the two clinical abnormalities. The first pattern (principal component 1) was topographically similar to that described previously in other PD populations. Subject scores for this pattern discriminated patients from controls and correlated significantly with bradykinesia ratings (P = 0.013, r = 0.655) in PD patients. The second pattern (principal component 2) was characterized by relative ventromedial frontal, hippocampal, and striatal hypometabolism, associated with mediodorsal thalamic hypermetabolism. In the PD group, scores from this pattern correlated with scores on the conditional associative learning (CAL; P = 0.01, r = 0.690) and the Brown Peterson paradigm (BPP; P = 0.017, r = −0.651) tests, respectively assessing strategy and planning, and working memory. According to these findings, the networks subserving bradykinesia and executive dysfunction in PD seems to be topographically distinct and to involve different aspects of subcortico-cortical processing. Hum. Brain Mapping 22:236–245, 2004. © 2004 Wiley-Liss, Inc.

Early‐stage nondemented Parkinson's disease (PD((es))) patients can learn short but not long sequences as well as controls. We have previously shown that to achieve normal performance, PD((es)) patients activated the same right‐sided cortical regions as controls plus the homologous left sided cortex and bilateral cerebellum. In this study, we evaluated two related hypotheses to explain the behavioral abnormalities and the increased bilateral brain activation observed in the PD((es)) group. Hypothesis 1 proposed that PD((es)) patients recruit regions from a normal bilateral network specialized for sequence learning that healthy controls would activate if performing difficult tasks. Thus, PD((es)) patients can learn short sequences as well as controls. Hypothesis 2 proposed that information processing within the network in the PD((es)) group is impaired. Thus, PD((es)) patients cannot learn as difficult a sequence as controls. To test hypothesis 1, we increased task difficulty and statistical power in the control group and showed that the control and the PD((es)) groups activated the same regions. To test hypothesis 2, we analyzed the equal performance data using two partial least squares (PLS) multivariate analyses. The task‐PLS analysis showed that to perform equally with controls, the PD((es)) group expressed the normal bilateral network more than the control group. The behavior‐PLS analysis showed that the correlation between learning performance and regional activation was significantly different between the groups. We conclude that PD((es)) patients have near normal learning if task difficulty is moderate because they can recruit additional regions from a normal bilateral network specialized for sequence learning. However, when a difficult task would normally require bilateral activation, PD((es)) patients fail to learn because information processing within the network is impaired. Hum. Brain Mapp. 20:246–258, 2003. © 2003 Wiley‐Liss, Inc.

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras.

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. The current study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting Positron Emission Tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after eight weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel-wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical-limbic-frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal-parietal-frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimer’s dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression.