Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis

We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2–5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS

Heritability of subcortical volumetric traits in mesial temporal lobe epilepsy.

OBJECTIVES: We aimed to 1) determine if subcortical volume deficits are common to mesial temporal lobe epilepsy (MTLE) patients and their unaffected siblings 2) assess the suitability of subcortical volumetric traits as endophenotypes for MTLE. METHODS: MRI-based volume measurements of the hippocampus, amygdala, thalamus, caudate, putamen and pallidium were generated using an automated brain reconstruction method (FreeSurfer) for 101 unrelated 'sporadic' MTLE patients [70 with hippocampal sclerosis (MTLE+HS), 31 with MRI-negative TLE], 83 unaffected full siblings of patients and 86 healthy control subjects. Changes in the volume of subcortical structures in patients and their unaffected siblings were determined by comparison with healthy controls. Narrow sense heritability was estimated ipsilateral and contralateral to the side of seizure activity. RESULTS: MTLE+HS patients displayed significant volume deficits across the hippocampus, amygdala and thalamus ipsilaterally. In addition, volume loss was detected in the putamen bilaterally. These volume deficits were not present in the unaffected siblings of MTLE+HS patients. Ipsilaterally, the heritability estimates were dramatically reduced for the volume of the hippocampus, thalamus and putamen but remained in the expected range for the amygdala. MRI-negative TLE patients and their unaffected siblings showed no significant volume changes across the same structures and heritability estimates were comparable with calculations from a healthy population. CONCLUSIONS: The findings indicate that volume deficits for many subcortical structures in 'sporadic' MTLE+HS are not heritable and likely related to acquired factors. Therefore, they do not represent suitable endophenotypes for MTLE+HS. The findings also support the view that, at a neuroanatomical level, MTLE+HS and MRI-negative TLE represent two distinct forms of MTLE

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Virtual brain biopsies in amyotrophic lateral sclerosis: Diagnostic classification based on in vivo pathological patterns

Background: Diagnostic uncertainty in ALS has serious management implications and delays recruitment into clinical trials. Emerging evidence of presymptomatic disease-burden provides the rationale to develop diagnostic applications based on the evaluation of in-vivo pathological patterns early in the disease. Objectives: To outline and test a diagnostic classification approach based on an array of complementary imaging metrics in key disease-associated anatomical structures. Methods: Data from 75 ALS patients and 75 healthy controls were randomly allocated in a ‘training’ and ‘validation’ cohort. Spatial masks were created for anatomical foci which best discriminate patients from controls in the ‘training sample’. In a virtual ‘brain biopsy’, data was then retrieved from these key disease-associated brain regions. White matter diffusivity indices, grey matter T1-signal intensity values and basal ganglia volumes were evaluated as predictor variables in a canonical discriminant function. Results: Following predictor variable selection, a classification specificity of 85.5% and sensitivity of 89.1% was achieved in the training sample and 90% specificity and 90% sensitivity in the validation sample. Discussion: This study evaluates disease-associated imaging measures in a dummy diagnostic application. Although larger samples will be required for robust validation, the study confirms the potential of multimodal quantitative imaging in future clinical applications. Keywords: Magnetic resonance imaging, Neuroimaging, Diagnosis, Neurodegeneration, Amyotrophic lateral sclerosis, Motor neuron diseas

Heritability of subcortical volumetric traits in mesial temporal lobe epilepsy.

OBJECTIVES: We aimed to 1) determine if subcortical volume deficits are common to mesial temporal lobe epilepsy (MTLE) patients and their unaffected siblings 2) assess the suitability of subcortical volumetric traits as endophenotypes for MTLE. METHODS: MRI-based volume measurements of the hippocampus, amygdala, thalamus, caudate, putamen and pallidium were generated using an automated brain reconstruction method (FreeSurfer) for 101 unrelated 'sporadic' MTLE patients [70 with hippocampal sclerosis (MTLE+HS), 31 with MRI-negative TLE], 83 unaffected full siblings of patients and 86 healthy control subjects. Changes in the volume of subcortical structures in patients and their unaffected siblings were determined by comparison with healthy controls. Narrow sense heritability was estimated ipsilateral and contralateral to the side of seizure activity. RESULTS: MTLE+HS patients displayed significant volume deficits across the hippocampus, amygdala and thalamus ipsilaterally. In addition, volume loss was detected in the putamen bilaterally. These volume deficits were not present in the unaffected siblings of MTLE+HS patients. Ipsilaterally, the heritability estimates were dramatically reduced for the volume of the hippocampus, thalamus and putamen but remained in the expected range for the amygdala. MRI-negative TLE patients and their unaffected siblings showed no significant volume changes across the same structures and heritability estimates were comparable with calculations from a healthy population. CONCLUSIONS: The findings indicate that volume deficits for many subcortical structures in 'sporadic' MTLE+HS are not heritable and likely related to acquired factors. Therefore, they do not represent suitable endophenotypes for MTLE+HS. The findings also support the view that, at a neuroanatomical level, MTLE+HS and MRI-negative TLE represent two distinct forms of MTLE

Dysfunction of attention switching networks in amyotrophic lateral sclerosis

Objective: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. Rationale: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. Methods: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. Results: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10−6, right: p = 1.07 × 10−5) and left superior temporal gyri (p = 9.30 × 10−6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). Interpretation: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI).Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials. Keywords: Amyotrophic lateral sclerosis, Network, EEG, Cognition, Source localisation, Mismatch negativit

Patterned functional network disruption in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting-state electroencephalography recordings from 74 ALS patients and 47 age-matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co-modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ- to β-band), lateral/orbitofrontal (δ- to θ-band) and sensorimotor (β-band) regions of the brain in patients with ALS. Furthermore, we show increased co-modulation of neural oscillations in the central and posterior (δ-, θ- and γl-band) and frontal (δ- and γl-band) regions, as well as decreased synchrony in the temporal and frontal (δ- to β-band) and sensorimotor (β-band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease-associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials