Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy.

BACKGROUND: To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES: To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS: This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS: Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS: Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork.

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Adaptive and pathological connectivity responses in Parkinson's disease brain networks.

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session.

Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.

Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease.

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson's disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries.

BACKGROUND: Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side. OBJECTIVE: We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages. METHODS: 45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages. RESULTS: Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally. CONCLUSION: Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.

Gene therapy reduces Parkinson's disease symptoms by reorganizing functional brain connectivity.

Gene therapy is emerging as a promising approach for treating neurological disorders, including Parkinson's disease (PD). A phase 2 clinical trial showed that delivering glutamic acid decarboxylase (GAD) into the subthalamic nucleus (STN) of patients with PD had therapeutic effects. To determine the mechanism underlying this response, we analyzed metabolic imaging data from patients who received gene therapy and those randomized to sham surgery, all of whom had been scanned preoperatively and at 6 and 12 months after surgery. Those who received GAD gene therapy developed a unique treatment-dependent polysynaptic brain circuit that we termed as the GAD-related pattern (GADRP), which reflected the formation of new polysynaptic functional pathways linking the STN to motor cortical regions. Patients in both the treatment group and the sham group expressed the previously reported placebo network (the sham surgery-related pattern or SSRP) when blinded to the treatment received. However, only the appearance of the GADRP correlated with clinical improvement in the gene therapy-treated subjects. Treatment-induced brain circuits can thus be useful in clinical trials for isolating true treatment responses and providing insight into their underlying biological mechanisms.

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.

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.

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.

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

Multivariate analytical routines have become increasingly popular in the study of cerebral function in health and in disease states. Spatial covariance analysis of functional neuroimaging data has been used to identify and validate characteristic topographies associated with specific brain disorders. Voxel-wise correlations can be used to assess similarities and differences that exist between covariance topographies. While the magnitude of the resulting topographical correlations is critical, statistical significance can be difficult to determine in the setting of large data vectors (comprised of over 100,000 voxel weights) and substantial autocorrelation effects. Here, we propose a novel method to determine the p-value of such correlations using pseudo-random network simulations.

Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners.

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.

Identification of disease-related spatial covariance patterns using neuroimaging data.

The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Parkinson's disease: increased motor network activity in the absence of movement.

We used a network approach to assess systems-level abnormalities in motor activation in humans with Parkinson's disease (PD). This was done by measuring the expression of the normal movement-related activation pattern (NMRP), a previously validated activation network deployed by healthy subjects during motor performance. In this study, NMRP expression was prospectively quantified in (15)O-water PET scans from a PD patient cohort comprised of a longitudinal early-stage group (n = 12) scanned at baseline and at two or three follow-up visits two years apart, and a moderately advanced group scanned on and off treatment with either subthalamic nucleus deep brain stimulation (n = 14) or intravenous levodopa infusion (n = 14). For each subject and condition, we measured NMRP expression during both movement and rest. Resting expression of the abnormal PD-related metabolic covariance pattern was likewise determined in the same subjects. NMRP expression was abnormally elevated (p < 0.001) in PD patients scanned in the nonmovement rest state. By contrast, network activity measured during movement did not differ from normal (p = 0.34). In the longitudinal cohort, abnormal increases in resting NMRP expression were evident at the earliest clinical stages (p < 0.05), which progressed significantly over time (p = 0.003). Analogous network changes were present at baseline in the treatment cohort (p = 0.001). These abnormalities improved with subthalamic nucleus stimulation (p < 0.005) but not levodopa (p = 0.25). In both cohorts, the changes in NMRP expression that were observed did not correlate with concurrent PD-related metabolic covariance pattern measurements (p > 0.22). Thus, the resting state in PD is characterized by changes in the activity of normal as well as pathological brain networks.

Abnormal network topographies and changes in global activity: absence of a causal relationship.

Changes in regional brain activity can be observed following global normalization procedures to reduce variability in the data. In particular, spurious regional differences may appear when scans from patients with low global activity are compared to those from healthy subjects. It has thus been suggested that the consistent increases in subcortical activity that characterize the abnormal Parkinson's disease-related metabolic covariance pattern (PDRP) are artifacts of global normalization, and that similar topographies can be identified in scans from healthy subjects with varying global activity. To address this issue, we examined the effects of experimental reductions in global metabolic activity on PDRP expression. Ten healthy subjects underwent ¹8F-fluorodeoxyglucose PET in wakefulness and following sleep induction. In all subjects, the global metabolic rate (GMR) declined with sleep (mean -34%, range: -17 to -56%), exceeding the test-retest differences of the measure (p<0.001). By contrast, sleep-wake differences in PDRP expression did not differ from test-retest differences, and did not correlate (R²=0.04) with concurrent declines in global metabolic activity. Indeed, despite significant GMR reductions in sleep, PDRP values remained within the normal range. Likewise, voxel weights on the principal component patterns resulting from combined analysis of the sleep and wake scans did not correlate (R²<0.07) with the corresponding regional loadings on the PDRP topography. In aggregate, the data demonstrate that abnormal PDRP expression is not induced by reductions in global activity. Moreover, significant declines in GMR are not associated with the appearance of PDRP-like spatial topographies.

Abnormal metabolic brain networks in a nonhuman primate model of parkinsonism.

Parkinson's disease (PD) is associated with a characteristic regional metabolic covariance pattern that is modulated by treatment. To determine whether a homologous metabolic pattern is also present in nonhuman primate models of parkinsonism, 11 adult macaque monkeys with parkinsonism secondary to chronic systemic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 12 age-matched healthy animals were scanned with [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET). A subgroup comprising five parkinsonian and six control animals was used to identify a parkinsonism-related pattern (PRP). For validation, analogous topographies were derived from other subsets of parkinsonian and control animals. The PRP topography was characterized by metabolic increases in putamen/pallidum, thalamus, pons, and sensorimotor cortex, as well as reductions in the posterior parietal-occipital region. Pattern expression was significantly elevated in parkinsonian relative to healthy animals (P<0.00001). Parkinsonism-related topographies identified in the other derivation sets were very similar, with significant pairwise correlations of region weights (r>0.88; P<0.0001) and subject scores (r>0.74; P<0.01). Moreover, pattern expression in parkinsonian animals correlated with motor ratings (r>0.71; P<0.05). Thus, homologous parkinsonism-related metabolic networks are demonstrable in PD patients and in monkeys with experimental parkinsonism. Network quantification may provide a useful biomarker for the evaluation of new therapeutic agents in preclinical models of PD.

Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues.

Consistent functional brain abnormalities in Parkinson's disease (PD) are difficult to pinpoint because differences from the normal state are often subtle. In this regard, the application of multivariate methods of analysis has been successful but not devoid of misinterpretation and controversy. The Scaled Subprofile Model (SSM), a principal components analysis (PCA)-based spatial covariance method, has yielded critical information regarding the characteristic abnormalities of functional brain organization that underlie PD and other neurodegenerative disorders. However, the relevance of disease-related spatial covariance patterns (metabolic brain networks) and the most effective methods for their derivation has been a subject of debate. We address these issues here and discuss the inherent advantages of proper application as well as the effects of the misapplication of this methodology. We show that ratio pre-normalization using the mean global metabolic rate (GMR) or regional values from a "reference" brain region (e.g. cerebellum) that may be required in univariate analytical approaches is obviated in SSM. We discuss deviations of the methodology that may yield erroneous or confounding factors.

Three-fold cross-validation of parkinsonian brain patterns.

Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.

Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features.

In the current paper, we describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls, and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.