Quantifying significance of topographical similarities of disease-related brain metabolic patterns

A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism

OBJECTIVE

The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP).

METHODS

Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale.

RESULTS

We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions.

INTERPRETATION

XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Longitudinal changes in metabolic network activity in early Alzheimer's disease

INTRODUCTION

The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN).

METHODS

Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans  = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated.

RESULTS

Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels.

DISCUSSION

The results point to the potential utility of ADRP as an imaging biomarker of AD progression.

Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses

Primary dystonia is thought to emerge through abnormal functional relationships between basal ganglia and cerebellar motor circuits. These interactions may differ across disease subtypes and provide a novel biomarker for diagnosis and treatment. Using a network mapping algorithm based on resting-state functional MRI (rs-fMRI), a method that is readily implemented on conventional MRI scanners, we identified similar disease topographies in hereditary dystonia associated with the DYT1 or DYT6 mutations and in sporadic patients lacking these mutations. Both networks were characterized by contributions from the basal ganglia, cerebellum, thalamus, sensorimotor areas, as well as cortical association regions. Expression levels for the two networks were elevated in hereditary and sporadic dystonia, and in non-manifesting carriers of dystonia mutations. Nonetheless, the distribution of abnormal functional connections differed across groups, as did metrics of network organization and efficiency in key modules. Despite these differences, network expression correlated with dystonia motor ratings, significantly improving the accuracy of predictions based on thalamocortical tract integrity obtained with diffusion tensor MRI (DTI). Thus, in addition to providing unique information regarding the anatomy of abnormal brain circuits, rs-fMRI functional networks may provide a widely accessible method to help in the objective evaluation of new treatments for this disorder.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network

BACKGROUND AND PURPOSE

Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored.

METHODS

We explored 2-[¹⁸ F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods.

RESULTS

The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization.

CONCLUSIONS

The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography

PURPOSE

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value.

METHODS

A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[¹⁸F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored.

RESULTS

The newly identified DLBRP shared topographic similarities with ADRP (R² = 24%) and PDRP (R² = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time.

CONCLUSION

DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Metabolic imaging and plasticity

Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.

Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.

Cognition-Related Functional Topographies in Parkinson's Disease: Localized Loss of the Ventral Default Mode Network

Cognitive dysfunction in Parkinson's disease (PD) is associated with increased expression of the PD cognition-related pattern (PDCP), which overlaps with the normal default mode network (DMN). Here, we sought to determine the degree to which the former network represents loss of the latter as a manifestation of the disease process. To address this, we first analyzed metabolic images (fluorodeoxyglucose positron emission tomography [PET]) from a large PD sample with varying cognitive performance. Cognitive impairment in these patients correlated with increased PDCP expression as well as DMN loss. We next determined the spatial relationship of the 2 topographies at the subnetwork level. To this end, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from an independent population. This approach uncovered a significant PD cognition-related network that resembled previously identified PET- and rs-fMRI-based PDCP topographies. Further analysis revealed selective loss of the ventral DMN subnetwork (precuneus and posterior cingulate cortex) in PD, whereas the anterior and posterior components were not affected by the disease. Importantly, the PDCP also included a number of non-DMN regions such as the dorsolateral prefrontal and medial temporal cortex. The findings show that the PDCP is a reproducible cognition-related network that is topographically distinct from the normal DMN.

Changes in Metabolic Activity and Gait Function by Dual-Task Cognitive Game-Based Treadmill System in Parkinson's Disease: Protocol of a Randomized Controlled Trial

Balance and gait impairments, and consequently, mobility restrictions and falls are common in Parkinson’s disease (PD). Various cognitive deficits are also common in PD and are associated with increased fall risk. These mobility and cognitive deficits are limiting factors in a person’s health, ability to perform activities of daily living, and overall quality of life. Community ambulation involves many dual-task (DT) conditions that require processing of several cognitive tasks while managing or reacting to sudden or unexpected balance challenges. DT training programs that can simultaneously target balance, gait, visuomotor, and cognitive functions are important to consider in rehabilitation and promotion of healthy active lives. In the proposed multi-center, randomized controlled trial (RCT), novel behavioral positron emission tomography (PET) brain imaging methods are used to evaluate the molecular basis and neural underpinnings of: (a) the decline of mobility function in PD, specifically, balance, gait, visuomotor, and cognitive function, and (b) the effects of an engaging, game-based DT treadmill walking program on mobility and cognitive functions. Both the interactive cognitive game tasks and treadmill walking require continuous visual attention, and share spatial processing functions, notably to minimize any balance disturbance or gait deviation/stumble. The ability to “walk and talk” normally includes activation of specific regions of the prefrontal cortex (PFC) and the basal ganglia (site of degeneration in PD). The PET imaging analysis and comparison with healthy age-matched controls will allow us to identify areas of abnormal, reduced activity levels, as well as areas of excessive activity (increased attentional resources) during DT-walking. We will then be able to identify areas of brain plasticity associated with improvements in mobility functions (balance, gait, and cognition) after intervention. We expect the gait-cognitive training effect to involve re-organization of PFC activity among other, yet to be identified brain regions. The DT mobility-training platform and behavioral PET brain imaging methods are directly applicable to other diseases that affect gait and cognition, e.g., cognitive vascular impairment, Alzheimer’s disease, as well as in aging.

Metabolic Network Abnormalities in Drug-Naïve Parkinson's Disease

BACKGROUND

An ideal imaging biomarker for a neurodegenerative disorder should be able to measure abnormalities in the earliest stages of the disease.

OBJECTIVE

We investigated metabolic network changes in two independent cohorts of drug-naïve Parkinson's disease (PD) patients who have not been exposed to dopaminergic medication.

METHODS

We scanned 85 de novo, drug-naïve PD patients and 85 age-matched healthy control subjects from Italy (n = 96) and the United States (n = 74) with [¹⁸ F]-fluorodeoxyglucose PET. All patients had clinical follow-ups to verify the diagnosis of idiopathic PD. Spatial covariance analysis was used to identify and validate de novo PD-related metabolic patterns in the Italian and U.S. cohorts. We compared the de novo PD-related metabolic patterns to the original PD-related pattern that was identified in more advanced patients who had been on chronic dopaminergic treatment.

RESULTS

De novo PD-related metabolic patterns were identified in each of the two independent cohorts of drug-naïve PD patients, and each differentiated PD patients from healthy control subjects. Expression values for these disease patterns were elevated in drug-naïve PD patients relative to healthy controls in the identification as well as in each of the validation subgroups. The two de novo PD-related metabolic patterns were topographically very similar to each other and to the original PD-related pattern.

CONCLUSIONS

Reproducible PD-related patterns are expressed in de novo, drug-naïve PD patients. In PD, disease-related metabolic patterns have stereotyped topographies that develop independently of chronic levodopa treatment. © 2019 International Parkinson and Movement Disorder Society.

LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks

The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.

Network Structure and Function in Parkinson's Disease

Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinson's disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Introduction

The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers.

Methods

We studied a total of 148 dementia patients from the Feinstein Institute (Center-A: 25 bvFTD and 10 Alzheimer's disease), Technical University of Munich (Center-B: 44 bvFTD and 29 FTD language variants), and Alzheimer's Disease Neuroimaging Initiative (40 Alzheimer's disease subjects). To identify the covariance pattern of bvFTD (behavioral variant frontotemporal dementia–related pattern [bFDRP]), we applied principal component analysis to combined 18F-fluorodeoxyglucose–positron emission tomography scans from bvFTD and healthy subjects. The phenotypic specificity and clinical correlates of bFDRP expression were assessed in independent testing sets.

Results

The bFDRP was identified in Center-A data (24.1% of subject × voxel variance; P < .001), reproduced in Center-B data (P < .001), and independently validated using combined testing data (receiver operating characteristics–area under the curve = 0.97; P < .0001). The expression of bFDRP was specifically elevated in bvFTD patients (P < .001) and was significantly higher at more advanced disease stages (P = .035:duration; P < .01:severity).

Discussion

The bFDRP can be used as a quantitative imaging marker to gauge the underlying disease process and aid in the differential diagnosis of bvFTD.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson's disease

PURPOSE

The purpose of this study was to evaluate the effects of different image reconstruction algorithms on topographic characteristics and diagnostic performance of the Parkinson's disease related pattern (PDRP).

METHODS

FDG-PET brain scans of 20 Parkinson's disease (PD) patients and 20 normal controls (NC) were reconstructed with six different algorithms in order to derive six versions of PDRP. Additional scans of 20 PD, 25 atypical parkinsonism (AP) patients and 20 NC subjects were used for validation. PDRP versions were compared by assessing differences in topographies, individual subject scores and correlations with patient's clinical ratings. Discrimination of PD from NC and AP subjects was evaluated across cohorts.

RESULTS

The region weights of the six PDRPs highly correlated (R ≥ 0.991; p < 0.0001). All PDRPs' expressions were significantly elevated in PD relative to NC and AP subjects (p < 0.0001) and correlated with clinical ratings (R ≥ 0.47; p < 0.05). Subject scores of the six PDRPs highly correlated within each of individual healthy and parkinsonian groups (R ≥ 0.972, p < 0.0001) and were consistent across the algorithms when using the same reconstruction methods in PDRP derivation and validation. However, when derivation and validation reconstruction algorithms differed, subject scores were notably lower compared to the reference PDRP, in all subject groups.

CONCLUSION

PDRP proves to be highly reproducible across FDG-PET image reconstruction algorithms in topography, ability to differentiate PD from NC and AP subjects and clinical correlation. When calculating PDRP scores in scans that have different reconstruction algorithms and imaging systems from those used for PDRP derivation, a calibration with NC subjects is advisable.

Disruption of network for visual perception of natural motion in primary dystonia

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-related network topographies characterized with resting state functional MRI

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.

Metabolic resting-state brain networks in health and disease

The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.