Morphometric alterations of the mesocorticolimbic network in Parkinson's disease with impulse control disorders.

Impulse control disorders (ICDs) are a group of non-motor symptoms of Parkinson disease (PD) leading to significant psychosocial detrimental outcome. The mesocorticolimbic network plays a distinctive role in reward learning and executive decision making and has been suggested to be involved in ICDs in PD. To study morphometric changes of the mesocorticolimbic network in PD with ICD. A total of 18 patients of PD with ICD (PD + ICD), 19 patients of PD without ICD (PD - ICD) and 19 healthy controls (HC) were included in the study. ICDs were diagnosed using Questionnaire for Impulsive-Compulsive Disorders in PD-Rating Scale (QUIP-RS). MRI was done using a 3T scanner and assessment of cortical thickness and subcortical volumes were done using FreeSurfer. Brain regions known to be part of the mesocorticolimbic network were extracted and included for statistical analysis. There was no difference between PD + ICD and PD - ICD with regard to duration of illness or total dopaminergic medication. In comparison to HC, patients with PD + ICD demonstrated atrophy of the left frontal pole, and this atrophy neared significance in comparison to PD - ICD. The QUIP-RS had a negative correlation with left caudate volume in PD + ICD. The PD + ICD group showed distinct morphometric changes in regions involved in the mesocorticolimbic system which may contribute to the presence of ICD.

Abnormal cortical excitability in patients with spinocerebellar ataxia type 12.

BACKGROUND: Spinocerebellar ataxia type 12 (SCA-12) is an uncommon autosomal dominant cerebellar ataxia characterized by action tremors in the upper limbs, dysarthria, head tremor, and gait ataxia. We aimed to evaluate the motor cortical excitability in patients with SCA-12 using transcranial magnetic stimulation (TMS). METHODS: The study was done in the department of Neurology at the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore. Nine patients with SCA-12 (2 females) and 10 healthy controls (2 females) were included in the study. TMS was performed in all the subjects and various parameters such as resting motor threshold (RMT), central motor conduction time (CMCT) and contralateral silent period (cSP) were recorded. The left motor cortex was stimulated and the recording was done from right first dorsal interossei muscle. The severity of ataxia was assessed using the scale for assessment and rating in ataxia (SARA). RESULTS: The mean age of the patients was 58.11 ± 7.56 years mean age at onset: 51.67 ± 4.18 years. The mean duration of illness was 9.44 ± 4.88 years. The mean SARA score was 13.83 ± 3.60. Patients with SCA-12 had significantly increased RMT (88.80 ± 12.78 %) compared to HC (44.90 ± 9.40 %, p < 0.05). A significantly prolonged CMCT was observed in patients (13.70 ± 2.52 msec) compared to HC (7.31 ± 1.21 msec, p < 0.05). In addition, cSP was significantly increased in SCA-12 patients (144.43 ± 25.79 msec) compared to HC (82.14 ± 28.90 msec, p < 0.05). CONCLUSIONS: Patients with SCA-12 demonstrate a reduced cortical excitability and increased cortical inhibition suggesting an increase in the GABAergic neurotransmission.

Parkinson's disease: Alterations of motor plasticity and motor learning.

This chapter reviews the alterations in motor learning and motor cortical plasticity in Parkinson's disease (PD), the most common movement disorder. Impairments in motor learning, which is a hallmark of basal ganglia disorders, influence the performance of motor learning-related behavioral tasks and have clinical implications for the management of disturbance in gait and posture, and for rehabilitative management of PD. Although plasticity is classically induced and assessed in sliced preparation in animal models, in this review we have concentrated on the results from non-invasive brain stimulation techniques such as transcranial magnetic stimulation (TMS), transcranial alternating current stimulation (tACS) and transcranial direct current stimulation (tDCS) in patients with PD, in addition to a few animal electrophysiologic studies. The chapter summarizes the results from different cortical and subcortical plasticity investigations. Plasticity induction protocols reveal deficient plasticity in PD and these plasticity measures are modulated by medications and deep brain stimulation. There is considerable variability in these measures that are related to inter-individual variations, different disease characteristics and methodological considerations. Nevertheless, these pathophysiologic studies expand our knowledge of cortical excitability, plasticity and the effects of different treatments in PD. These tools of modulating plasticity and motor learning improve our understanding of PD pathophysiology and help to develop new treatments for this disabling condition.

Cognitive impairment and its neuroimaging correlates in spinocerebellar ataxia 2.

INTRODUCTION: Cognitive impairment (CI) is reported but is poorly explored in spinocerebellar ataxia 2 (SCA2). This study was undertaken to evaluate and classify cognitive impairment in patients with SCA2 and to identify their grey matter (GM) correlates. METHODS: We evaluated the neurocognitive profile of 35 SCA2 and 30 age-, gender- and education-matched healthy controls using tests for attention, executive functions, learning and memory, language and fluency, and visuomotor constructive ability. Patients were classified into SCA2 with and without CI based on normative data from population and healthy controls. Furthermore, patients with CI were sub-classified based on the number of impaired domains into multi-domain CI (≥3 domains; MDCI) and limited domain CI (≤2 domains; LDCI). The underlying GM changes were identified using voxel based morphometry. RESULTS: The mean age at onset, duration of disease, and ataxia score was 28.7 ± 8.51 years, 66.7 ± 44.1 months, and 16.1 ± 4.9 points, respectively. CI was present in 71.4% of SCA2 subjects (MDCI: 42.7%; LDCI: 28.5%). Patients with CI had significant atrophy of the posterior cerebellum, sensorimotor cortex, and superior frontal gyrus (FWE p-value <0.05). Patients with MDCI had significant GM atrophy of the angular gyrus compared to LDCI (FWE p-value <0.05). CONCLUSION: Patients with CI had significant GM involvement of the posterior cerebellum and frontal lobe, suggestive of impairment in the cerebello-fronto-cortical circuitry.

In vivo microstructural white matter changes in early spinocerebellar ataxia 2.

OBJECTIVE: White matter (WM) integrity of Spinocerebellar ataxia 2 (SCA2) is poorly understood, more so in the early stages of SCA2. In this study, we evaluated the microstructural integrity of the WM tracts with an emphasis on the nature of in vivo pathological involvement in early SCA2. MATERIALS AND METHODS: We evaluated the MRI images of 26 genetically proven SCA2 patients with disease duration <5 years and 24 age- and gender-matched healthy controls using tract-based spatial statistics (TBSS) to identify the WM tract changes and their clinico-genetic correlates (age at onset, duration of disease, ataxia severity and CAG repeat length) using standard methodology. RESULTS: The mean age at onset and duration of disease were 28.7 ± 8.51 years and 3.5 ± 0.69 months, respectively. The mean CAG repeat length was 42.5 ± 4.6, and the ataxia severity score was 16.1 ± 4.9. Altered DTI scalars signifying degeneration was present in the bilateral anterior thalamic radiation (ATR), corticospinal tract (CST), inferior fronto-occipital fasciculus (IFOF), superior and inferior longitudinal fasciculus (SLF and ILF), uncinate fasciculus (UF), cingulum, corpus callosum (CC), forceps major and forceps minor (corrected p < .05). DTI scalars representing demyelination was seen in the superior cerebellar peduncle (SCP) and cerebellar WM. There was a significant correlation of SARA score with axial diffusivity of the bilateral cingulum, ATR, CST, forceps minor, IFOF, ILF, SLF and SCP on the right side (corrected p < .05). CONCLUSION: Extensive WM involvement is present in early SCA2. The DTI scalars indicate degeneration and demyelination and may have clinical implications.

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.

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients with Temporal Lobe Epilepsy (TLE) in the absence of an interictal discharge (IED). METHOD: 4 Classes of microstates were computed from 2 min artefact free EEG epochs in 42 subjects (21 TLE and 21 controls). The percentage of time coverage, frequency of occurrence and duration for each of these microstates were computed and redundancy reduced using feature selection methods. Subsequently, Fishers Linear Discriminant Analysis (FLDA) and logistic regression were used for classification. RESULT: FLDA distinguished TLE with 76.1% accuracy (85.0% sensitivity, 66.6% specificity) considering frequency of occurrence and percentage of time coverage of microstate C as features. CONCLUSION: Microstate alterations are present in patients with TLE. This feature might be useful in the diagnosis of epilepsy even in the absence of an IED.

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

Background and Purpose: Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".