Myotonic Dystrophy Type 1 (DM1): Clinical Characteristics and Disease Progression in a Large Cohort.

BACKGROUND: DM1 is a multisystem disorder caused by expansion of a CTG triplet repeat in the 3' non-coding region of DMPK. Neuropsychological consequences and sleep abnormalities are important associations in DM1. OBJECTIVE: To describe the clinical phenotype, disease progression and characterize the sleep alterations and cognitive abnormalities in a sub-set of patients. MATERIALS AND METHODS: A retrospective study on 120 genetically confirmed DM1 cases. Findings in neuropsychological assessment and multiple sleep questionnaires were compared with 14 age and sex matched healthy individuals. All 120 patients were contacted through letters/telephonic consultation/hospital visits to record their latest physical and functional disabilities. RESULTS: The mean age at symptom onset was 23.1 ± 11.4 years, M: F = 3.8:1, mean duration of illness = 14.3 ± 9.5 years. Clinically 54.2% had adult onset form, juvenile = 27.5%, infantile = 10.8%, late adult onset = 7.5%. Paternal transmission occurred more frequently. The predominant initial symptoms were myotonia (37.5%), hand weakness (21.7%), lower limb weakness (23.3%) and bulbar (10%). Twenty patients completed sleep questionnaires (SQ). Abnormal scores were noted in Epworth sleepiness scale (55%); Pittsburgh sleep quality index (45%); Berlin SQ (30%); Rapid eye movement sleep Behaviour Disorder SQ (15%); Restless leg syndrome rating scale (10%). Neuropsychological assessment of 20 patients revealed frontal executive dysfunction, attention impairment and visuospatial dysfunction. Frontal lobe was most affected (72%) followed by parietal (16%) and temporal lobe (12%). CONCLUSIONS: The current study provides a comprehensive account of the clinical characteristics in Indian patients with DM1. Hypersomnolence was most commonly seen. Excessive daytime sleepiness and Sleep disordered breathing were the most common sleep related abnormality. Cognitive impairment comprised predominantly of frontal lobe dysfunction.

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. METHODS: Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks." RESULTS: SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. CONCLUSIONS: IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE. KEY POINTS: • ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.

Unraveling Brain Functional Connectivity of encoding and retrieval in the context of education.

Human memory is an enigmatic component of cognition which many researchers have attempted to comprehend. Accumulating studies on functional connectivity see brain as a complex dynamic unit with positively and negatively correlated networks in perfect coherence during a task. We aimed to examine coherence of network connectivity during visual memory encoding and retrieval in the context of education. School Educated (SE) and College Educated (CE) healthy volunteers (n=60) were recruited and assessed for visual encoding and retrieval. Functional connectivity using seed to voxel based connectivity analysis of the posterior cingulate cortex (PCC) was evaluated. We noticed that there were reciprocal dynamic changes in both dorsolateral prefrontal cortex (DLPFC) region and PCC regions during working memory encoding and retrieval. In agreement with the previous studies, there were more positively correlated regions during retrieval compared to encoding. The default mode network (DMN) networks showed greater negative correlations during more attentive task of visual encoding. In tune with the recent studies on cognitive reserve we also found that number of years of education was a significant factor influencing working memory connectivity. SE had higher positive correlation to DLPFC region and lower negative correlation to DMN in comparison with CE during encoding and retrieval.