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Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach.

ABSTRACT: According to expert consensus, dystonia can be classified as focal, segmental, multifocal, and generalized, based on the affected body distribution. To provide an empirical and data-driven approach to categorizing these distributions, we used a data-driven clustering approach to compare frequency and co-occurrence rates of non-focal dystonia in pre-defined body regions using the Dystonia Coalition (DC) dataset. We analyzed 1,618 participants with isolated non-focal dystonia from the DC database. The analytic approach included construction of frequency tables, variable-wise analysis using hierarchical clustering and independent component analysis (ICA), and case-wise consensus hierarchical clustering to describe associations and clusters for dystonia affecting any combination of eighteen pre-defined body regions. Variable-wise hierarchical clustering demonstrated closest relationships between bilateral upper legs (distance = 0.40), upper and lower face (distance = 0.45), bilateral hands (distance = 0.53), and bilateral feet (distance = 0.53). ICA demonstrated clear grouping for the a) bilateral hands, b) neck, and c) upper and lower face. Case-wise consensus hierarchical clustering at k = 9 identified 3 major clusters. Major clusters consisted primarily of a) cervical dystonia with nearby regions, b) bilateral hand dystonia, and c) cranial dystonia. Our data-driven approach in a large dataset of isolated non-focal dystonia reinforces common segmental patterns in cranial and cervical regions. We observed unexpectedly strong associations between bilateral upper or lower limbs, which suggests that symmetric multifocal patterns may represent a previously underrecognized dystonia subtype.

SUBMITTER: Younce JR

PROVIDER: S-EPMC10621194 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach.

Younce J R JR Cascella R H RH Berman B D BD Jinnah H A HA Bellows S S Feuerstein J J Wagle Shukla A A Mahajan A A Chang F C F FCF Duque K R KR Reich S S Richardson S Pirio SP Deik A A Stover N N Luna J M JM Norris S A SA

Dystonia (Lausanne, Switzerland) 20230608

According to expert consensus, dystonia can be classified as focal, segmental, multifocal, and generalized, based on the affected body distribution. To provide an empirical and data-driven approach to categorizing these distributions, we used a data-driven clustering approach to compare frequency and co-occurrence rates of non-focal dystonia in pre-defined body regions using the Dystonia Coalition (DC) dataset. We analyzed 1,618 participants with isolated non-focal dystonia from the DC database. ...[more]

PMID: 37920445

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Project description:BackgroundData-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships.MethodsA community-ascertained sample of individuals (N = 347, 18-59 years of age) completed a battery of behavioral measures, psychiatric assessment, and resting-state functional magnetic resonance imaging in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid hierarchical clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate distance matrix regression examined functional connectome differences between adjacent groups.ResultsReduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid hierarchical clustering of these six factors identified two, four, and eight nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks.ConclusionsData-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction, not only yielded clinically meaningful groups, but also captured behavioral and neurobiological variation among healthy individuals.

Project description:BackgroundPhenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.MethodsWe recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.ResultsParticipants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.ConclusionWe found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

Project description:ImportanceIsolated dystonia and Parkinson disease (PD) are disorders of the basal gangliothalamocortical network. They have largely distinct clinical profiles, but both disorders respond to deep brain stimulation (DBS) in the same subcortical targets using similar stimulation paradigms, suggesting pathophysiologic overlap. We hypothesized that, similar to PD, isolated dystonia is associated with elevated cortical neuronal synchronization.ObjectiveTo investigate the electrophysiologic characteristics of the sensorimotor cortex arm-related area using a temporary subdural electrode strip in patients with isolated dystonia and PD undergoing DBS implantation in the awake state.Design, setting, and participantsAn observational study recruited patients scheduled for DBS at the University of California, San Francisco and the San Francisco Veterans Affairs Medical Center. Data were collected from May 1, 2008, through April 1, 2015. Findings are reported for 22 patients with isolated cervical or segmental dystonia (8 with [DYST-ARM] and 14 without [DYST] arm symptoms) and 14 patients with akinetic rigid PD. Data were analyzed from November 1, 2014, through May 1, 2015.Main outcomes and measuresCortical local field potentials, power spectral density, and phase-amplitude coupling (PAC).ResultsAmong our 3 groups that together included 36 patients, cortical PAC was present in primary motor and premotor arm-related areas for all groups, but the DYST group was less likely to exhibit increased PAC (P = .008). Similar to what has been shown for patients with PD, subthalamic DBS reversibly decreased PAC in a subset of patients with dystonia who were studied before and during intraoperative test stimulation (n = 4). At rest, broadband gamma (50-200 Hz) power in the primary motor cortex was greater in the DYST-ARM and PD groups compared with the DYST group, whereas alpha (8-13 Hz) and beta (13-30 Hz) power was comparable in all 3 groups. During movement, the DYST-ARM group had impaired beta and low gamma desynchronization in the primary motor cortex.Conclusions and relevanceIsolated dystonia and PD have physiologic overlap with respect to high levels of motor cortex synchronization and reduction of cortical synchronization by subthalamic DBS, providing an explanation for their similar therapeutic response to basal ganglia stimulation.

Dataset Information

Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach.

Publications

Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach.

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