Similar to Alzheimer's disease (AD), dementia with Lewy bodies (DLB) is characterized by a profound degeneration of cortically-projecting cholinergic neurons of the basal forebrain (BF) and associated depletion of cortical cholinergic activity. We aimed to investigate subregional atrophy of the BF in DLB in vivo and compare it to the pattern of BF atrophy in AD. Structural MRI scans of 11 patients with DLB, 11 patients with Alzheimer's disease, and 22 healthy controls were analysed using a recently developed technique for automated BF morphometry based on high-dimensional image warping and cytoarchitectonic maps of BF cholinergic nuclei. For comparison, hippocampus volume was assessed within the same morphometric framework using recently published consensus criteria for the definition of hippocampus outlines on MRI. The DLB group demonstrated pronounced and subregion-specific atrophy of the BF which was comparable to BF atrophy in AD: volume of the nucleus basalis Meynert was significantly reduced by 20-25%, whereas rostral BF nuclei were only marginally affected. By contrast, hippocampus volume was markedly less affected in DLB compared to AD. Global cognition as determined by MMSE score was associated with BF volume in AD, but not in DLB, whereas visuoperceptual function as determined by the trail making test was associated with BF volume in DLB, but not in AD. DLB may be characterized by a more selective degeneration of the cholinergic BF compared to AD, which may be related to the differential cognitive profiles in both conditions.

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.

Amyotrophic lateral sclerosis (ALS) is a terminal progressive adult-onset neurodegeneration of the motor system. Although originally considered a pure motor degeneration, there is increasing evidence of disease heterogeneity with varying degrees of extra-motor involvement. How the combined motor and nonmotor degeneration occurs in the context of broader disruption in neural communication across brain networks has not been well characterized. Here, we have performed high-density crossectional and longitudinal resting-state electroencephalography (EEG) recordings on 100 ALS patients and 34 matched controls, and have identified characteristic patterns of altered EEG connectivity that have persisted in longitudinal analyses. These include strongly increased EEG coherence between parietal-frontal scalp regions (in γ-band) and between bilateral regions over motor areas (in θ-band). Correlation with structural MRI from the same patients shows that disease-specific structural degeneration in motor areas and corticospinal tracts parallels a decrease in neural activity over scalp motor areas, while the EEG over the scalp regions associated with less extensively involved extra-motor regions on MRI exhibit significantly increased neural communication. Our findings demonstrate that EEG-based connectivity mapping can provide novel insights into progressive network decline in ALS. These data pave the way for development of validated cost-effective spectral EEG-based biomarkers that parallel changes in structural imaging.

Amyotrophic lateral sclerosis (ALS) is characterised by degeneration of upper (UMN) and lower motor neurons (LMN).We aimed to relate clinical variables to cortical thinning of the primary motor cortex (PMC). The PMC was defined as the region of interest in high-resolution structural MRI scans. We related vertex-wise measures of cortical thinning to UMN involvement, bulbar/limb onset, the total ALS functional rating scale (ALSFRS-R), and its bulbar and upper limb subscore. In total, 93 ALS patients were recruited (60 with classical ALS; 17 with dominant UMN, e.g., primary lateral sclerosis; 16 with pure LMN variant, e.g., progressive muscular atrophy, flail arm or leg syndrome) and compared to 67 age and gender matched healthy controls. The UMN signs in the bulbar regions were associated with bilateral thinning within the bulbar segment on the motor cortex, and UMN signs in spinal regions were associated with thinning in the limb segment of the motor cortex. The site of disease onset (bulbar/lower limb) exhibited the most pronounced thinning in the corresponding part of the motor cortex. According to our analysis, dominant UMN patients demonstrated the most distinct thinning followed by classical ALS patients. Pure LMN variants did not differ from healthy controls. The bulbar subscore of the ALSFRS-R correlated with thinning of the left inferior PMC. Focal morphological changes within the PMC correspond to clinically measured impairments and depend on disease phenotype. Measuring cortical thickness may potentially offer an objective in vivo marker to quantify disease pathology.

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BackgroundDespite significant advances in quantitative neuroimaging, the diagnosis of ALS remains clinical and MRI-based biomarkers are not currently used to aid the diagnosis. The objective of this study is to develop a robust, disease-specific, multimodal classification protocol and validate its diagnostic accuracy in independent, early-stage and follow-up data sets.Methods147 participants (81 ALS patients and 66 healthy controls) were divided into a training sample and a validation sample. Patients in the validation sample underwent follow-up imaging longitudinally. After removing age-related variability, indices of grey and white matter integrity in ALS-specific pathognomonic brain regions were included in a cross-validated binary logistic regression model to determine the probability of individual scans indicating ALS. The following anatomical regions were assessed for diagnostic classification: average grey matter density of the left and right precentral gyrus, the average fractional anisotropy and radial diffusivity of the left and right superior corona radiata, inferior corona radiata, internal capsule, mesencephalic crus of the cerebral peduncles, pontine segment of the corticospinal tract, and the average diffusivity values of the genu, corpus and splenium of the corpus callosum.ResultsUsing a 50% probability cut-off value of suffering from ALS, the model was able to discriminate ALS patients and HC with good sensitivity (80.0%) and moderate accuracy (70.0%) in the training sample and superior sensitivity (85.7%) and accuracy (78.4%) in the independent validation sample.ConclusionsThis diagnostic classification study endeavours to advance ALS biomarker research towards pragmatic clinical applications by providing an approach of automated individual-data interpretation based on group-level observations.

BackgroundAmyotrophic lateral sclerosis (ALS) a highly heterogeneous neurodegenerative condition. Accurate diagnostic, monitoring and prognostic biomarkers are urgently needed both for individualised patient care and clinical trials. A multimodal magnetic resonance imaging study is presented, where MRI measures of ALS-associated brain regions are utilised to predict 18-month survival.MethodsA total of 60 ALS patients and 69 healthy controls were included in this study. 20% of the patient sample was utilised as an independent validation sample. Surface-based morphometry and diffusion tensor white matter parameters were used to identify anatomical patterns of neurodegeneration in 80% of the patient sample compared to healthy controls. Binary logistic ridge regressions were carried out to predict 18-month survival based on clinical measures alone, MRI features, and a combination of clinical and MRI data. Clinical indices included age at symptoms onset, site of disease onset, diagnostic delay from first symptom to diagnosis, and physical disability (ALSFRS-r). MRI features included the average cortical thickness of the precentral and paracentral gyri, the average fractional anisotropy, radial-, medial-, and axial diffusivity of the superior and inferior corona radiata, internal capsule, cerebral peduncles and the genu, body and splenium of the corpus callosum.ResultsClinical data alone had a survival prediction accuracy of 66.67%, with 62.50% sensitivity and 70.84% specificity. MRI data alone resulted in a prediction accuracy of 77.08%, with 79.16% sensitivity and 75% specificity. The combination of clinical and MRI measures led to a survival prediction accuracy of 79.17%, with 75% sensitivity and 83.34% specificity.ConclusionQuantitative MRI measures of ALS-specific brain regions enhance survival prediction in ALS and should be incorporated in future clinical trial designs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-017-0854-x) contains supplementary material, which is available to authorized users.