Annals of Neurology最新文献

Objective

Cognitive impairment in Parkinson's disease (PD) can show a very heterogeneous trajectory among patients. Here, we explored the mechanisms involved in the expression and prediction of different cognitive phenotypes over 4 years.

Methods

In 2 independent cohorts (total n = 475), we performed a cluster analysis to identify trajectories of cognitive progression. Baseline and longitudinal level II neuropsychological assessments were conducted, and baseline structural magnetic resonance imaging, resting electroencephalogram and neurofilament light chain plasma quantification were carried out. Linear mixed-effects models were used to study longitudinal changes. Risk of mild cognitive impairment and dementia were estimated using multivariable hazard regression. Spectral power density from the electroencephalogram at baseline and source localization were computed.

Results

Two cognitive trajectories were identified. Cluster 1 presented stability (PD-Stable) over time, whereas cluster 2 showed progressive cognitive decline (PD-Progressors). The PD-Progressors group showed an increased risk for evolving to PD mild cognitive impairment (HR 2.09; 95% CI 1.11–3.95) and a marked risk for dementia (HR 4.87; 95% CI 1.34–17.76), associated with progressive worsening in posterior-cortical-dependent cognitive processes. Both clusters showed equivalent clinical and sociodemographic characteristics, structural magnetic resonance imaging, and neurofilament light chain levels at baseline. Conversely, the PD-Progressors group showed a fronto-temporo-occipital and parietal slow-wave power density increase, that was in turn related to worsening at 2 and 4 years of follow-up in different cognitive measures.

Interpretation

In the absence of differences in baseline cognitive function and typical markers of neurodegeneration, the further development of an aggressive cognitive decline in PD is associated with increased slow-wave power density and with a different profile of worsening in several posterior-cortical-dependent tasks. ANN NEUROL 2024;96:981–993

Objective

Training clinician–scientists is a primary objective of many academic neurology departments, as these individuals are uniquely positioned to perform insightful clinical or laboratory-based research informed both by clinical knowledge and their own experiences caring for patients. Despite its importance, training clinician–scientists has perhaps never been so challenging. The National Institute of Neurologic Disorders and Stroke (NINDS) R25 program was designed in an attempt to support these individuals, decrease the time needed to obtain National Institutes of Health K awards, and to help educate a cohort of trainees preparing for a career in academic neurology. We endeavored to describe the structure and features of the program while examining its outcomes.

Methods

R25 outcome data from 2009 to 2024 were reviewed. Statistical comparisons were made using 2-sided Mann–Whitney U testing.

Results

A total of 67% of adult neurologists who received an R25 had a successful application for a National Institutes of Health K award compared with 45% of adult neurologists who had not received R25 support (p < 0.0001). Among child neurologists, 73% who applied went on to receive K funding after R25 support, compared with 45% who had not been part of the R25 program (p < 0.001). The average time between completion of residency and obtaining a K award for R25 participants was decreased by 26 months among those with an MD/PhD degree, and 32 months for those with an MD degree compared with non-R25 individuals.

Interpretation

The R25 program has been successful in achieving its training goals, but stands as only one component of support for aspiring clinician–scientists. Investments and commitments made by academic neurology departments are key to supporting this success. ANN NEUROL 2024;96:625–632

Objective

To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).

Materials

We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses.

Results

FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases.

Interpretation

The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944–957

Objectives

Spinocerebellar ataxia type 1 (SCA1) is a rare autosomal dominant neurodegenerative disease. Objective surrogate markers sensitive to detect changes in disease severity are needed to reduce sample sizes in interventional trials and identification of predictors of faster disease progression would facilitate patient selection, enrichment, or stratification in such trials.

Methods

We performed a prospective 1-year longitudinal, multimodal study in 34 ataxic SCA1 individuals and 21 healthy controls. We collected clinical, patient-reported outcomes, biochemical and magnetic resonance (MR) biomarkers at baseline and after 1 year. We determined 1-year progression and evaluated the potential predictive value of several baseline markers on 1-year disease progression.

Results

At baseline, multiple structural and spectroscopic MR markers in pons and cerebellum differentiated SCA1 from healthy controls and correlated with disease severity. Plasma and cerebrospinal fluid (CSF) neurofilament light (NfL) chain and CSF glial fibrillary acidic protein (GFAP) were elevated in SCA1. In longitudinal analysis, total brainstem and pontine volume change, inventory of non-ataxia signs (INAS) count, and SCA functional index (SCAFI) showed larger responsiveness compared to the Scale for Assessment and Rating of Ataxia (SARA).

Longer disease duration, longer non-expanded CAG repeat length, and higher disease burden were associated with faster SARA increase after 1-year in the SCA1 group. Similarly, lower baseline brainstem, pontine, and cerebellar volumes, as well as lower levels of N-acetylaspartate and glutamate in the cerebellar white matter, were also associated with faster SARA increase.

Interpretation

Our results guide the selection of the most sensitive measures of disease progression in SCA1 and have identified features associated with accelerated progression that could inform the design of clinical trials. ANN NEUROL 2024;96:774–787

Objective

Glymphatic system is a recently discovered macroscopic waste clearance system associated with numerous neurological diseases. However, little is known about glymphatic system development in neonates. We sought to evaluate diffusion along the perivascular space (ALPS) index, a proxy for glymphatic system function, in neonates and investigate its potential associations with maturation, sex, and preterm birth.

Methods

Diffusion magnetic resonance imaging (MRI) data in 418 neonates, including 92 preterm neonates (57 males) and 326 term neonates (175 males), from the Developing Human Connectome Project were used for evaluating ALPS index. Linear regression modeling was performed to assess group differences in the ALPS index according to preterm birth and sex. Pearson's and partial correlation analysis were performed to assess the association between the ALPS index and gestational age (GA) as well as postmenstrual age (PMA) at MRI. Moderation analysis was performed to assess the moderation effect of preterm birth on the relationship between the ALPS index and PMA.

Results

Compared to term neonates, preterm neonates exhibited lower ALPS indices (p < 0.001). The ALPS index positively correlated with PMA (p = 0.004) and GA (p < 0.001). Preterm birth (p = 0.013) had a significant moderation effect on the relationship between the ALPS index and PMA. Sex had no significant direct effect (p = 0.639) or moderation effect (p = 0.333) on ALPS index.

Interpretation

Glymphatic system development is a dynamic process in neonates, which can be moderated by preterm birth, the ALPS index could serve as a sensitive biomarker for monitoring this process. ANN NEUROL 2024;96:970–980

Objective

To understand the etiological landscape and phenotypic differences between 2 developmental and epileptic encephalopathy (DEE) syndromes: DEE with spike–wave activation in sleep (DEE-SWAS) and epileptic encephalopathy with spike–wave activation in sleep (EE-SWAS).

Methods

All patients fulfilled International League Against Epilepsy (ILAE) DEE-SWAS or EE-SWAS criteria with a Core cohort (n = 91) drawn from our Epilepsy Genetics research program, together with 10 etiologically solved patients referred by collaborators in the Expanded cohort (n = 101). Detailed phenotyping and analysis of molecular genetic results were performed. We compared the phenotypic features of individuals with DEE-SWAS and EE-SWAS. Brain-specific gene co-expression analysis was performed for D/EE-SWAS genes.

Results

We identified the etiology in 42/91 (46%) patients in our Core cohort, including 29/44 (66%) with DEE-SWAS and 13/47 (28%) with EE-SWAS. A genetic etiology was identified in 31/91 (34%). D/EE-SWAS genes were highly co-expressed in brain, highlighting the importance of channelopathies and transcriptional regulators. Structural etiologies were found in 12/91 (13%) individuals. We identified 10 novel D/EE-SWAS genes with a range of functions: ATP1A2, CACNA1A, FOXP1, GRIN1, KCNMA1, KCNQ3, PPFIA3, PUF60, SETD1B, and ZBTB18, and 2 novel copy number variants, 17p11.2 duplication and 5q22 deletion. Although developmental regression patterns were similar in both syndromes, DEE-SWAS was associated with a longer duration of epilepsy and poorer intellectual outcome than EE-SWAS.

Interpretation

DEE-SWAS and EE-SWAS have highly heterogeneous genetic and structural etiologies. Phenotypic analysis highlights valuable clinical differences between DEE-SWAS and EE-SWAS which inform clinical care and prognostic counseling. Our etiological findings pave the way for the development of precision therapies. ANN NEUROL 2024;96:932–943

Objective

Multiple acyl-coenzyme A dehydrogenase deficiency (MADD) is a disorder of fatty acid oxidation and considered an inborn error of metabolism. In recent years, we have diagnosed an increasing number of patients where, despite extensive investigation, no disease-causing mutations have been found. We therefore investigated a cohort of consecutive patients, with the objective to detect possible non-genetic causes.

Methods

We searched the patient records and the registry of muscle biopsies, for patients with MADD, diagnosed within the past 10 years. The patient records were reviewed regarding symptoms, clinical findings, comorbidities, drugs, diagnostic investigations, and response to treatment. In addition, complementary investigations of muscle tissue were performed.

Results

We identified 9 patients diagnosed with late-onset MADD. All presented with muscle weakness and elevated levels of creatine kinase. A lipid storage myopathy was evident in the muscle biopsies, as was elevated acylcarnitines in blood. Despite thorough genetic investigations, a probable genetic cause was found in only 2 patients. Remarkably, all 7 patients without disease-causing mutations were treated with sertraline. In some cases, a deterioration of symptoms closely followed dose increase, and discontinuation resulted in an improved acylcarnitine profile. All 9 patients responded to riboflavin treatment with normalization of creatine kinase and muscle biopsy findings, and in 8 patients the clinical symptoms clearly improved.

Interpretation

Our findings strongly suggest that sertraline may induce an acquired form of MADD in some patients. Importantly, riboflavin treatment seems to be similarly effective as in genetic MADD, but discontinuation of sertraline is reasonably warranted. ANN NEUROL 2024;96:802–811

Objective

Restless legs syndrome (RLS) is a neurological condition that causes uncomfortable sensations in the legs and an irresistible urge to move them, typically during periods of rest. The genetic basis and pathophysiology of RLS are incompletely understood. We sought to identify additional novel genetic risk factors associated with RLS susceptibility.

Methods

We performed a whole-genome sequencing and genome-wide association meta-analysis of RLS cases (n = 9,851) and controls (n = 38,957) in 3 population-based biobanks (All of Us, Canadian Longitudinal Study on Aging, and CARTaGENE).

Results

Genome-wide association analysis identified 9 independent risk loci, of which 8 had been previously reported, and 1 was a novel risk locus (LMX1B, rs35196838, OR 1.14, 95% CI 1.09–1.19, p value = 2.2 × 10⁻⁹). Furthermore, a transcriptome-wide association study also identified GLO1 and a previously unreported gene, ELFN1. A genetic correlation analysis revealed significant common variant overlaps between RLS and neuroticism (r_g = 0.40, se = 0.08, p value = 5.4 × 10⁻⁷), depression (r_g = 0.35, se = 0.06, p value = 2.17 × 10⁻⁸), and intelligence (r_g = −0.20, se = 0.06, p value = 4.0 × 10⁻⁴).

Interpretation

Our study expands the understanding of the genetic architecture of RLS, and highlights the contributions of common variants to this prevalent neurological disorder. ANN NEUROL 2024;96:994–1005